Endothelial cell metabolism in health and disease: impact of hypoxia

In contrast to the general belief, endothelial cell (EC) metabolism has recently been identified as a driver rather than a bystander effect of angiogenesis in health and disease. Indeed, different EC subtypes present with distinct metabolic properties, which determine their function in angiogenesis upon growth factor stimulation. One of the main stimulators of angiogenesis is hypoxia, frequently observed in disease settings such as cancer and atherosclerosis. It has long been established that hypoxic signalling and metabolism changes are highly interlinked. In this review, we will provide an overview of the literature and recent findings on hypoxia‐driven EC function and metabolism in health and disease. We summarize evidence on metabolic crosstalk between different hypoxic cell types with ECs and suggest new metabolic targets.     

Banking human tissue for research: vision to reality

Advances in scientific understanding of disease together with introduction of new high throughput technologies have led to increased demand for human tissue in research. In general, patients are willing to donate for research, particularly samples that are surplus to diagnostic or therapeutic requirements. New tissue-specific regulations in the UK are intended to facilitate the use of human tissue in research. Despite this positive environment there are challenges to researcher access. Coordinated, systematic collection and storage, via a biobank can provide easier access. However translating a vision for a biobank into reality whether in the public or private sector, has never been simple. But it can be done.     

The evolving biology of cell reprogramming

Modern stem cell biology has achieved a transformation that was thought by many to be every bit as unattainable as the ancient alchemists' dream of transforming base metals into gold. Exciting opportunities arise from the process known as 'cellular reprogramming' in which cells can be reliably changed from one tissue type to another. This is enabling novel approaches to more deeply investigate the fundamental basis of cell identity. In addition, new opportunities have also been created to study (perhaps even to treat) human genetic and degenerative diseases. Specific cell types that are affected in inherited disease can now be generated from easily accessible cells from the patient and compared with equivalent cells from healthy donors. The differences in cellular phenotype between the two may then be identified, and assays developed to establish therapies that prevent the development or progression of disease symptoms. Cellular reprogramming also has the potential to create new cells to replace those whose death or dysfunction causes disease symptoms. For patients suffering from inherited cases of degenerative diseases like Parkinson's disease or amyotrophic lateral sclerosis (also known as motor neuron disease), the future realization of such cell-based therapies would truly be worth its weight in gold. However, before this enormous potential can become a reality, several significant biological and technical challenges must be overcome. Furthermore, to maintain the credibility of the scientific community with the general public, it is important that hope-inspiring advances are not over-hyped. The papers in this issue of the Philosophical Transactions of the Royal Society B: Biological Sciences cover many areas relevant to this topic. In this Introduction, we provide an overall context in which to consider these individual papers.     

Toll-like receptors in the gonads and reproductive tract: emerging roles in reproductive physiology and pathology

Interactions between the immune system and reproductive system have important consequences for fertility and reproductive health in general. There is increasing evidence that many of the interactions between the immune and reproductive systems involve the Toll-like receptors (TLRs). While there is no doubt that TLRs are important in providing protection against infection in the reproductive tract, there is increasing evidence for the involvement of TLRs in more basic pathology and physiology of reproduction. In the female, TLRs have been implicated in critical aspects of ovarian, endometrial and placental function, as well as in ovarian cancer, pelvic inflammatory disease, intrauterine growth restriction, pre-eclampsia and preterm birth. In the male, TLRs appear to play a role in the control of testicular steroidogenesis and spermatogenesis in disease and, potentially, during normal function, as well. Recent studies also have begun to highlight the role of various TLRs in the aetiology of prostatitis and prostatic cancer. Given the nascent state of knowledge concerning this important area, it is clear that more studies are needed, which should provide valuable new insights into the biology of the TLRs and reproductive function in general.     

Multi-Stage Markov Analysis of Progressive Disease Applied to Melanoma

A fundamental research goal in clinical studies of progressive, multi-stage disease is to understand its natural history and its relationship with prognostic factors. Our current understanding of this topic is based on the use of two-stage methods for event-time analysis which neglect intermediate transition information. In contrast, a multi-stage model utilizes all available data and provides more accurate insight into disease progression. We specify a forward-flowing multi-stage Markov model based on the discrete clinical stages of disease. By assuming the process to be Markovian, we avoid unnecessary complications to our numerical estimation procedure. Due to noncontinuous patient monitoring and the chronic nature of progressive disease, heavy right- and interval-censoring exists in the transition data. We develop a modified ECM algorithm to numerically carry out the otherwise complicated parameter estimation for this process. We also identify significant prognostic factors relevant to each transition, along with the relative importance of each prognostic factor. The numerical estimation is stable, and the parameter estimates are maximum likelihood estimates (Meng, 1990). In general our forward-flowing multi-stage models provide a flexible framework for the study of the effects of prognostic factors on progression among several stages. We apply our Markov model to a dataset of malignant melanoma patients, and present an inferential discussion. Results from our multi-stage Markov model provide an improved understanding of melanoma progression.     

Determination of the spread of HIV from the AIDS incidence history

The relation between the incidence of HIV in the general population, the number of AIDS cases, and the incubation period for the disease is examined. The number of AIDS cases can be expressed in terms of a convolution integral over the incubation period distribution and the temporal history of HIV incidence. In order to determine the level of HIV incidence it is necessary to invert the convolution. In this manner, it is possible to determine the spread of HIV up to the present time from knowledge of the AIDS incidence history and the incubation period. We describe the inversion of the convolution in terms of a Laplace transform technique that is applicable for any given incubation period distribution. Substantial simplifications in the technique are found in the case of an Erlang distribution for the probability density. The spread of HIV infections in the United States is charted through 1988 using AIDS incidence data that are corrected for both the revised AIDS case definition and reporting time delays. The results are consistent with current estimates of the HIV incidence in the United States and show no evidence of saturation in the rate of new infections. Indeed, the rate of new infections still appears to be climbing as of that date. While the technique is unable to predict the future course of the epidemic, it may provide a useful benchmark for comparison with mathematical models of the epidemic. The techniques are conceptually applicable to diseases other than AIDS.     

Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.     

Fish-on-a-chip: a sensitive detection microfluidic system for alzheimer's disease

Microfluidics has become an important tool in diagnosing many diseases, including neurological and genetic disorders. Alzheimer's disease (AD) is a neurodegenerative disease that irreversibly and progressively destroys memory, language ability, and thinking skills. Commonly, detection of AD is expensive and complex. Fluorescence in situ hybridization (FISH)-based microfluidic chip platform is capable of diagnosing AD at an early stage and they are effective tools for the diagnosis with low cost, high speed, and high sensitivity. In this review, we tried to provide basic information on the diagnosis of AD via FISH-based microfluidics. Different sample preparations using a microfluidic chip for diagnosis of AD are highlighted. Moreover, rapid innovations in nanotechnology for diagnosis are explained. This review will provide information on dynamic quantification methods for the diagnosis and treatment of AD. The knowledge provided in this review will help develop new integration diagnostic techniques based on FISH and microfluidics.     

Proteomics-based diagnosis of chronic obstructive pulmonary disease: the hunt for new markers

Chronic obstructive pulmonary disease (COPD) is an inflammatory disease characterized by the progressive deterioration of pulmonary function and irreversible airway obstruction. Investigations of the molecular pathogenesis of COPD have not yet provided complete answers to the mechanisms that determine the onset and progression of this illness. Therefore, therapeutic choices are limited and new strategies are needed to prevent, manage and treat this disorder. In particular, the application of complementary approaches, including gel- and liquid chromatography mass spectrometry-based proteomic techniques on sputum and/or bronchoalveolar lavage may provide a better understanding of the proteome differentially expressed by COPD patients in the course of the disease. The identification of appropriate and reliable biomarkers is, thus, an essential step for the diagnostics and treatment of these patients.     

A log-linear modeling framework for selective mixing.

Nonrandom mixing can significantly alter the diffusion path of an infectious disease such as AIDS that requires intimate contact. Recent attempts to model this effect have sought a general framework capable of representing both simple and arbitrarily complicated mixing structures, and of solving the balancing problem in a nonequilibrium multigroup population. Log-linear models are proposed here as a general framework for solving the first problem. This approach offers several additional benefits: The parameters used to govern the mixing have a simple, intuitive interpretation, the framework provides a statistically sound basis for the estimation of these parameters from mixing-matrix data, and the resulting estimates are easily integrated into compartmental models for diffusion. A modified selection model is proposed to solve the second problem of generalizing the selection process to nonequilibrium populations. The distribution of contacts under this model is derived and is found to satisfy the assumptions of statistical inference for log-linear models. Together these techniques provide an integrated and flexible framework for modeling the role of selective mixing in the spread of disease.     

Free radical theory of aging.

Free radical reactions are ubiquitous in living things. Studies on the origin and evolution of life provide a reasonable explanation for the prominent presence of this unruly class of chemical reactions. These reactions have been implicated in aging. This phenomenon is the accumulation of changes responsible for the sequential alterations that accompany advancing age and the associated progressive increases in the chance of disease and death. Aging changes are attributed to the environment and disease, and to an inborn process, the aging process. The latter produces aging changes at an exponentially increasing rate with advancing age. Past improvements in general living conditions have decreased the chances for death so that they are now near limiting values in the developed countries. In these countries the intrinsic aging process is the major cause of disease and death after about age 28. The free radical theory of aging postulates that aging changes are caused by free radical reactions. The data supporting this theory indicate that average life expectancy at birth may be increased by 5 or more years, by nutritious low caloric diets supplemented with one or more free radical reaction inhibitors.     

Biochemical systems theory: increasing predictive power by using second-order derivatives measurements

Models based on the power-law formalism provide a useful tool for analyzing metabolic systems. Within this methodology, the S-system variant furnishes the best strategy. In this paper we explore an extension of this formalism by considering second-order derivative terms of the Taylor series which the power-law is based upon. Results show that the S-system equations which include second-order Taylor coefficients give better accuracy in predicting the response of the system to a perturbation. Hence, models based on this new approach could provide a useful tool for quantitative purposes if one is able to measure the required derivatives experimentally. In particular we show the utility of this approach when it comes to discriminating between two mechanisms that are equivalent in the S-system a representation based on first-order coefficients. However, the loss of analytical tractability is a serious disadvantage for using this approach as a general tool for studying metabolic systems.     

Sex differences in disease risk from reported genome-wide association study findings

Men and women differ in susceptibility to many diseases and in responses to treatment. Recent advances in genome-wide association studies (GWAS) provide a wealth of data for associating genetic profiles with disease risk; however, in general, these data have not been systematically probed for sex differences in gene-disease associations. Incorporating sex into the analysis of GWAS results can elucidate new relationships between single nucleotide polymorphisms (SNPs) and human disease. In this study, we performed a sex-differentiated analysis on significant SNPs from GWAS data of the seven common diseases studied by the Wellcome Trust Case Control Consortium. We employed and compared three methods: logistic regression, Woolf’s test of heterogeneity, and a novel statistical metric that we developed called permutation method to assess sex effects (PMASE). After correction for false discovery, PMASE finds SNPs that are significantly associated with disease in only one sex. These sexually dimorphic SNP-disease associations occur in Coronary Artery Disease and Crohn’s Disease. GWAS analyses that fail to consider sex-specific effects may miss discovering sexual dimorphism in SNP-disease associations that give new insights into differences in disease mechanism between men and women.     

COVID-19 therapy: What weapons do we bring into battle?

Urgent treatments, in any modality, to fight SARS-CoV-2 infections are desired by society in general, by health professionals, by Estate-leaders and, mainly, by the scientific community, because one thing is certain amidst the numerous uncertainties regarding COVID-19: knowledge is the means to discover or to produce an effective treatment against this global disease. Scientists from several areas in the world are still committed to this mission, as shown by the accelerated scientific production in the first half of 2020 with over 25,000 published articles related to the new coronavirus. Three great lines of publications related to COVID-19 were identified for building this article: The first refers to knowledge production concerning the virus and pathophysiology of COVID-19; the second regards efforts to produce vaccines against SARS-CoV-2 at a speed without precedent in the history of science; the third comprehends the attempts to find a marketed drug that can be used to treat COVID-19 by drug repurposing. In this review, the drugs that have been repurposed so far are grouped according to their chemical class. Their structures will be presented to provide better understanding of their structural similarities and possible correlations with mechanisms of actions. This can help identifying anti-SARS-CoV-2 promising therapeutic agents.     

Focusing in on structural genomics: the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.     

Long-range correlations improve understanding of the influence of network structure on contact dynamics

Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a host’s interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable and significantly improves the approximation’s performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a network’s clustering coefficient, as well as of new geometrical measures, such as a network’s square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation-extended to incorporate long-range correlations according to our method-is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence.     

Voltage-gated sodium channels and metastatic disease

Voltage-gated Na⁺ channels (VGSCs) are macromolecular protein complexes containing a pore-forming α subunit and smaller non-pore-forming β subunits. VGSCs are expressed in metastatic cells from a number of cancers. In these cells, Na⁺ current carried by α subunits enhances migration, invasion and metastasis in vivo. In contrast, the β subunits mediate cellular adhesion and process extension. The prevailing hypothesis is that VGSCs are upregulated in cancer, in general favoring an invasive/metastatic phenotype, although the mechanisms are still not fully clear. Expression of the Na_v1.5 α subunit associates with poor prognosis in clinical breast cancer specimens, suggesting that VGSCs may have utility as prognostic markers for cancer progression. Furthermore, repurposing existing VGSC-blocking therapeutic drugs may provide a new strategy to improve outcomes in patients suffering from metastatic disease, which is the major cause of cancer-related deaths, and for which there is currently no cure.     

The Dutch experience of open access echocardiography

Cardiac disease is not easy to recognise in general practice. An echocardiogram is an excellent way to provide information about left ventricular mass and diastolic (dys)function and the presence of valvular heart disease. To improve diagnostic care of cardiac patients, an open access echocardiography service was established in the referral area of our hospital, where general practitioners were able to ask for an echocardiogram without referring the patient to the cardiologist. Between December 2002 and October 2006 echocardiograms were requested for 471 patients. Thirteen percent of the patients referred for dyspnoea and 3% of patients with a cardiac murmur had a left ventricular ejection fraction <40%. In 28% of patients no cardiac abnormality could be found. If we looked at the prevalence of hypertension in the referred patients, this was very high with a prevalence of up to 60% in the older age groups. If we included hypertension in the analysis, only 16% of patients had no structural cardiac or vascular abnormality. The study shows that the advantage of open access echocardiography in the Netherlands is that the general practitioner is able to make a better diagnosis and unnecessary referrals of patients with suspected cardiac disease can be avoided. (Neth Heart J 2007;15:432-7.)     

The emergence of new virus diseases: an overview

From experience, we know that many different elements can contribute to the emergence of a new virus disease; these include virologic determinants [such as mutation, recombination, reassortment (drift and shift), natural selection, fitness adaptation, evolutionary progressional], natural influences (such as ecologic, environmental and zoonotic influences) and factors pertaining to human activity (such as behavioral, societal, transport, commercial and iatrogenic factors). In general, there is no way to predict when or where the next important new viral pathogen will emerge; neither is there any way to reliably predict the ultimate importance of a virus as it first emerges. Given this reality, initial investigation at the first sign of the emergence of a new virus disease must focus on characteristics such as mortality, severity of disease, transmissibility, and remote spread, all of which are important predictors of epidemic potential and societal threat. Clinical observations, pathologic examinations and preliminary virus identification and characterization often provide early clues, since new, emerging viruses often resemble their closest genetic relatives in regard to their epidemiologic and pathogenetic characteristics. Development of a global surveillance/diagnostics/communications network is needed, but this, in turn, must be linked to a global action network. This network must be designed to be flexible; capable, for example, in one instance of emphasizing local professional infrastructure development, and in the next of emphasizing global epidemic aid. Given the nature and magnitude of the threat represented by new and emerging virus diseases, the development of a global surveillance/action network is, indeed, a worthy goal for national and international public health authorities.     

Unraveling monogenic channelopathies and their implications for complex polygenic disease

Ion channels are a large family of >400 related proteins representing >1% of our genetic endowment; however, ion-channel diseases reflect a relatively new category of inborn error. They were first recognized in 1989, with the discovery of cystic fibrosis transmembrane conductance regulator, and rapidly advanced as positional and functional studies converged in the dissection of components of the action potential of excitable tissues. Although it remains true that diseases of excitable tissue still most clearly illustrate this family of disease, ion-channel disorders now cover the gamut of medical disciplines, causing significant pathology in virtually every organ system, producing a surprising range of often unanticipated symptoms, and providing valuable targets for pharmacological intervention. Many of the features shared among the monogenic ion-channel diseases provide a general framework for formulating a foundation for considering their intrinsically promising role in polygenic disease. Since an increasingly important approach to the identification of genes underlying polygenic disease is to identify "functional candidates" within a critical region and to test their disease association, it becomes increasingly important to appreciate how these ion-channel mechanisms can be implicated in pathophysiology.