Label-free surface-enhanced Raman spectroscopy for detection of colorectal cancer and precursor lesions using blood plasma

Fecal based tests have limited diagnostic values in detecting adenomatous polyps, the precursor lesions to colorectal cancer (CRC). Surface enhanced Raman spectroscopy (SERS) using silver nanoparticles as substrate is a multiplexed analytical technique capable of detecting biomolecules with high sensitivity. This study utilizes SERS to analyze blood plasma for detecting both CRC and adenomatous polyps for the first time. Blood plasma samples are collected from healthy control subjects and patients diagnosed with adenomas and CRC. Using a real-time Raman system, SERS spectra for blood plasma samples are measured in 1 s. The collected SERS spectra are analyzed with partial least squares-discriminant analysis. Classification of normal versus CRC plus adenomatous polyps achieved diagnostic sensitivity of 86.4% and specificity of 80%. The results suggest that blood plasma SERS analysis could be a potential screening test to detect both CRC and adenomas.OCIS codes: (240.6695) Surface-enhanced Raman scattering, (170.1470) Blood or tissue constituent monitoring, (170.5660) Raman spectroscopy, (170.4580) Optical diagnostics for medicine     

Placental expression of soluble fms-like tyrosine kinase 1 is increased in singletons and twin pregnancies with intrauterine growth restriction

OBJECTIVE: Intrauterine growth restriction (IUGR) is characterized by decreased placental perfusion. Low oxygen has been shown to increase soluble fms-like tyrosine kinase 1 (sFlt-1) expression in the human placenta. The objective of this study was to examine sFlt-1 expression in different types of IUGR pregnancies, including early-onset severe cases characterized by abnormal umbilical and uterine artery Doppler and discordant IUGR twins in which the normal cotwin represents the optimal control because both placentas share the same uterine environment. PATIENTS: Placentas from four subgroups were collected: early severe IUGR with umbilical artery absent end diastolic flow (n = 19), small for gestational age with normal uterine and umbilical artery Doppler (n = 11), severely growth-restricted dichorionic and monochorionic twins with abnormal umbilical artery Doppler (n = 9), preeclamptic twins (n = 3), and age-matched normal singletons (n = 19) and twin controls (n = 8). RESULTS: Expression of sFlt-1 mRNA and protein was significantly increased in IUGR placentas compared with small for gestational age and normal control placentas. sFlt-1 expression levels were also significantly greater in the small IUGR twin placentas from discordant twin pregnancies compared with the normal cotwin. In preeclamptic twins, sFlt-1 expression was increased in only one of the two placentas. CONCLUSIONS: Our results demonstrate that sFlt-1 expression is increased in severe IUGR placentas with abnormal umbilical artery Doppler of singletons and also in discordant IUGR twins. Reduced placental perfusion may contribute to the increased expression of sFlt-1 in IUGR pregnancies. Our data are compatible with differential sFlt-1 expression in placentas from discordant twins.     

Tumor necrosis factor receptor-associated periodic syndrome as a model linking autophagy and inflammation in protein aggregation diseases

Autophagy prevents cellular damage by eliminating insoluble aggregates of mutant misfolded proteins, which accumulate under different pathological conditions. Downregulation of autophagy enhances the inflammatory response and thus represents a possible common pathogenic event underlying a number of autoinflammatory syndromes, such as tumor necrosis factor (TNF) receptor-associated periodic syndrome (TRAPS). The pathogenesis of other monogenic or complex disorders that display symptoms of excessive inflammation also involve the autophagy pathway. Studies have shown that TRAPS-associated TNFRSF1A mutations induce cytoplasmic retention of the TNFR1 receptor, defective TNF-induced apoptosis, and production of reactive oxygen species (ROS). Furthermore, autophagy impairment may account for the pathogenic effects of TNFRSF1A mutations, thus inducing inflammation in TRAPS. In this review, we summarize the molecular interactions and functional links between autophagy with regard to nuclear factor-kappa B activation, ROS production, and apoptosis. Furthermore, we propose a complex interplay of these pathways as a model to explain the relationship between mutant protein misfolding and inflammation in genetically determined and aggregation-prone diseases. Accordingly, autophagy function should be investigated in all diseases showing an inflammatory component, and for which the molecular pathogenesis is still unclear.     

Tyrosine 201 of the cytoplasmic tail of CTLA‐4 critically affects T regulatory cell suppressive function

Cytotoxic T lymphocyte antigen‐4 (CTLA‐4) is a major negative regulatory molecule for T‐cell activation with a complex biology and function. CTLA‐4 is known to regulate homeostatic lymphoproliferation as well as tolerance induction and has been proposed to be an important effector molecule by which Treg cells suppress immunity. The immunoregulatory properties of CTLA‐4 are primarily mediated by competition with the costimulator CD28 for ligand binding but also by delivering negative signals to T cells through its cytoplasmic tail. In this study, we addressed the effect of directly mutating the amino acid residue, Tyrosine 201 (Tyr201), of the intracellular domain of CTLA‐4 in situ and its implications in T‐cell function in the context of autoimmunity. Therefore, a novel CTLA‐4 knock‐in mouse (Y201V KI) was generated, in which Tyr201 was replaced by a valine that could not be phosphorylated. Mice expressing the CTLA‐4 mutant molecule were generally healthy and did not show signs of disruption of T‐cell homeostasis under steady‐state conditions seen in CTLA‐4 deficient mice. However, T cells isolated from Y201V KI mice expressed higher levels of CTLA‐4 on the cell surface and displayed a Th2‐biased phenotype following TCR stimulation. Furthermore, Y201V KI mice developed exacerbated disease as compared to wild‐type upon antigen‐specific T‐cell activation in an in vivo model of EAE. Importantly, the Y201V mutation resulted in impaired suppressive activity of Treg cells while T effector function remained intact. These data suggest that effects associated with and mediated through Tyr201 of CTLA‐4s intracellular domain are critical for Treg‐cell function.     

Molecular and Serological Diversity of Neisseria meningitidis Carrier Strains Isolated from Italian Students Aged 14 to 22 Years

Neisseria meningitidis is an obligate human commensal that commonly colonizes the oropharyngeal mucosa. Carriage is age dependent and very common in young adults. The relationships between carriage and invasive disease are not completely understood. In this work, we performed a longitudinal carrier study in adolescents and young adults (173 subjects). Overall, 32 subjects (18.5%) had results that were positive for meningococcal carriage in at least one visit (average monthly carriage rate, 12.1%). Only five subjects tested positive at all four visits. All meningococcal isolates were characterized by molecular and serological techniques. Multilocus sequence typing, PorA typing, and sequencing of the 4CMenB vaccine antigens were used to assess strain diversity. The majority of positive subjects were colonized by capsule null (34.4%) and capsular group B strains (28.1%), accounting for 23.5% and 29.4% of the total number of isolates, respectively. The fHbp and nhba genes were present in all isolates, while the nadA gene was present in 5% of the isolates. The genetic variability of the 4CMenB vaccine antigens in this collection was relatively high compared with that of other disease-causing strain panels. Indications about the persistence of the carriage state were limited to the time span of the study. All strains isolated from the same subject were identical or cumulated minor changes over time. The expression levels and antigenicities of the 4CMenB vaccine antigens in each strain were analyzed by the meningococcal antigen typing system (MATS), which revealed that expression can change over time in the same individual. Future analysis of antigen variability and expression in carrier strains after the introduction of the MenB vaccine will allow for a definition of its impact on nasopharyngeal/oropharyngeal carriage.     

Search for Neuro-Endocrine Markers (Chromogranin A,Synaptophysin and VGF) in Breast Cancers. An integrated Approach Using Immunohistochemistry and Gene Expression Profiling

Discordant data are reported in the literature on the definition, incidence and clinical features of neuroendocrine (NE) carcinomas of the breast. This tumour entity is currently assessed by immunohistochemistry (IHC) detecting “general” NE markers such as chromogranin A (CHGA) and synaptophysin (SYP), but other markers have been considered as well. In the present study, in addition to CHGA and SYP, we investigated the expression of VGF, a neurotrophin-inducible gene, which is emerging as a new specific NE marker. In order to evaluate the differential expression of these neuro-endocrine markers in breast cancers, we conducted parallel immunohistochemical and gene expression analyses, using PCR, gene array and real-time quantitative PCR procedures. Data obtained in 28 cases were further validated with a meta-analysis of published datasets of 103 breast cancer cases. The value of IHC positivity (irrespective of the percentage of positive cells) was confirmed by over-expression of the related gene. However, the genetic approach emerged as more sensitive, showing over-expression of NE markers in a subset of IHC-negative carcinomas. In conclusion, the present study confirms, by a novel approach, the occurrence of NE differentiation in breast cancers. Over-expression of one or more NE marker (CHGA and/or SYP and/or VGF) characterizes a significant fraction (approximately 10 %) of infiltrative breast cancers.     

The AIMAR recommendations for early diagnosis of chronic obstructive respiratory disease based on the WHO/GARD model*

Respiratory diseases in Italy already now represent an emergency (they are the 3^rd ranking cause of death in the world, and the 2^nd if Lung cancer is included). In countries similar to our own, they result as the principal cause for a visit to the general practitioner (GP) and the second main cause after injury for recourse to Emergency Care. Their frequency is probably higher than estimated (given that respiratory diseases are currently underdiagnosed). The trend is towards a further increase due to epidemiologic and demographic factors (foremost amongst which are the widespread diffusion of cigarette smoking, the increasing mean age of the general population, immigration, and pollution). Within the more general problem of chronic disease care, chronic respiratory diseases (CRDs) constitute one of the four national priorities in that they represent an important burden for society in terms of mortality, invalidity, and direct healthcare costs. The strategy suggested by the World Health Organization (WHO) is an integrated approach consisting of three goals: inform about health, reduce risk exposure, improve patient care. The three goals are translated into practice in the three areas of prevention (1-primary, 2-secondary, 3-tertiary) as: 1) actions of primary (universal) prevention targeted at the general population with the aim to control the causes of disease, and actions of Predictive Medicine - again addressing the general population but aimed at measuring the individual’s risk for disease insurgence; 2) actions of early diagnosis targeted at groups or - more precisely - subgroups identified as at risk; 3) continuous improvement and integration of care and rehabilitation support - destined at the greatest possible number of patients, at all stages of disease severity. In Italy, COPD care is generally still inadequate. Existing guidelines, institutional and non-institutional, are inadequately implemented: the international guidelines are not always adaptable to the Italian context; the document of the Agency for Regional Healthcare Services (AGE.NA.S) is a more suited compendium for consultation, and the recent joint statement on integrated COPD management of the three major Italian scientific Associations in the respiratory area together with the contribution of a Society of General Medicine deals prevalently with some critical issues (appropriateness of diagnosis, pharmacological treatment, rehabilitation, continuing care); also the document “Care Continuity: Chronic Obstructive Pulmonary Disease (COPD)” of the Global Alliance against chronic Respiratory Diseases (GARD)-Italy does not treat in depth the issue of early diagnosis. The present document – produced by the AIMAR (Interdisciplinary Association for Research in Lung Disease) Task Force for early diagnosis of chronic respiratory disease based on the WHO/GARD model and on available evidence and expertise –after a general examination of the main epidemiologic aspects, proposes to integrate the above-mentioned existing documents. In particular: a) it formally indicates on the basis of the available evidence the modalities and the instruments necessary for carrying out secondary prevention at the primary care level (a pro-active,‘case-finding’approach; assessment of the individual’s level of risk of COPD; use of short questionnaires for an initial screening based on symptoms; use of simple spirometry for the second level of screening); b) it identifies possible ways of including these activities within primary care practice; c) it places early diagnosis within the “systemic”, consequential management of chronic respiratory diseases, which will be briefly described with the aid of schemes taken from the Italian and international reference documents.     

Topological sensitivity analysis for systems biology

Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.Using simple models to study complex systems has become standard practice in different fields, including systems biology, ecology, and economics. Although we know and accept that such models do not fully capture the complexity of the underlying systems, they can nevertheless provide meaningful predictions and insights (1). A successful model is one that captures the key features of the system while omitting extraneous details that hinder interpretation and understanding. Constructing such a model is usually a nontrivial task involving stages of refinement and improvement.When dealing with models that are (necessarily and by design) gross oversimplifications of the reality they represent, it is important that we are aware of their limitations and do not seek to overinterpret them. This is particularly true when modeling complex systems for which there are only limited or incomplete observations. In such cases, we expect there to be numerous models that would be supported by the observed data, many (perhaps most) of which we may not yet have identified. The literature is awash with papers in which a single model is proposed and fitted to a dataset, and conclusions drawn without any consideration of (i) possible alternative models that might describe the observed behavior and known facts equally well (or even better); or (ii) whether the conclusions drawn from different models (still consistent with current observations) would agree with one another.We propose an approach to assess the impact of uncertainty in model structure on our conclusions. Our approach is distinct from—and complementary to—existing methods designed to address structural uncertainty, including model selection, model averaging, and ensemble modeling (2–9). Analogous to parametric sensitivity analysis (PSA), which assesses the sensitivity of a model’s behavior to changes in parameter values, we consider the sensitivity of a model’s output to changes in its inherent structural assumptions. PSA techniques can usually be classified as (i) local analyses, in which we identify a single “optimal” vector of parameter values, and then quantify the degree to which small perturbations to these values change our conclusions or predictions; or (ii) global analyses, where we consider an ensemble of parameter vectors (e.g., samples from the posterior distribution in the Bayesian formalism) and quantify the corresponding variability in the model’s output. Although several approaches fall within these categories (10–12), all implicitly condition on a particular model architecture. Here we present a method for performing sensitivity analyses for ordinary differential equation (ODE) models where the architecture of these models is not perfectly known, which is likely to be the case for all realistic complex systems. We do this by considering network representations of our models, and assessing the sensitivity of our inferences to the network topology. We refer to our approach as topological sensitivity analysis (TSA).Here we illustrate TSA in the context of parameter inference, but we could also apply our method to study other conclusions drawn from ODE models (e.g., model forecasts or steady-state analyses). When we use experimental data to infer parameters associated with a specific model it is critical to assess the uncertainty associated with our parameter estimates (13), particularly if we wish to relate model parameters to physical (e.g., reaction rate) constants in the real world. Too often this uncertainty is estimated only by considering the variation in a parameter estimate conditional on a particular model, while ignoring the component of uncertainty that stems from potential model misspecification. The latter can, in principle, be considered within model selection or averaging frameworks, where several distinct models are proposed and weighted according to their ability to fit the observed data (2–5). However, the models tend to be limited to a small, often diverse, group that act as exemplars for each competing hypothesis but ignore similar model structures that could represent the same hypotheses. Moreover, we know that model selection results can be sensitive to the particular experiments performed (14).We assume that an initial model, together with parameters or plausible parameter ranges, has been proposed to describe the dynamics of a given system. This model may have been constructed based on expert knowledge of the system, selected from previous studies, or (particularly in the case of large systems) proposed automatically using network inference algorithms (15–19), for example. Using TSA, we aim to identify how reliant any conclusions and inferences are on the particular set of structural assumptions made in this initial candidate model. We do this by identifying alterations to model topology that maintain consistency with the observed dynamics and test how these changes impact the conclusions we draw (Fig. 1). Analogous to PSA we may perform local or global analyses—by testing a small set of “close” models with minor structural changes, or performing large-scale searches of diverse model topologies, respectively. To do this we require efficient techniques for exploring the space of network topologies and, for each topology, inferring the parameters of the corresponding ODE models.Open in a separate windowFig. 1.Overview of TSA applied to parameter inference. (A) Model space includes our initial candidate model and a series of altered topologies that are consistent with our chosen rules (e.g., all two-edge, three-node networks, where nodes indicate species and directed edges show interactions). One topology may correspond to one or several ODE models depending on the parametric forms we choose to represent interactions. (B) We test each ODE model to see whether it can generate dynamics consistent with our candidate model and the available experimental data. For TSA, we select a group of these compatible models and compare the conclusions we would draw using each of them. (C) Associated with each model m is a parameter space Θ_m (gray); using Bayesian methods we can infer the joint posterior parameter distribution (dashed contours) for a particular model and dataset. (D) In some cases, equivalent parameters will be present in several selected models (e.g., θ₁, which is associated with the same interaction in models a–c). We can compare the marginal posterior distribution inferred using each model for a common parameter to test whether our inferences are robust to topological changes, or rely on one specific set of model assumptions (i.e., sensitive). Different models may result in marginal distributions that differ in position and/or shape for equivalent parameters, but we cannot tell from this alone which model better represents reality—this requires model selection approaches (2–4).Even for networks with relatively few nodes (corresponding to ODE models involving few interacting entities), the number of possible topologies can be enormous. Searching this “model space” presents formidable computational challenges. We use here a gradient-matching parameter inference approach that exploits the fact that the nth node, x_n, in our network representation is conditionally independent of all other nodes given its regulating parents, Pa(x_n) (20–26). The exploration of network topologies is then reduced to the much simpler problem of considering, independently for each n, the possible parent sets of x_n in an approach that is straightforwardly parallelized.We use biological examples to illustrate local and global searches of model spaces to identify alternative model structures that are consistent with available data. In some cases we find that even minor structural uncertainty in model topology can render our conclusions—here parameter inferences—unreliable and make PSA results positively misleading. However, other inferences are robust across diverse compatible model structures, allowing us to be more confident in assigning scientific meaning to the inferred parameter values.