Scientific data and theories for salmonellosis dose–response assessment

An “expansive” risk assessment approach is illustrated, characterizing dose–response relationships for salmonellosis in light of the full body of evidence for human and murine superorganisms. Risk assessments often require analysis of costs and benefits for supporting public health decisions. Decision-makers and the public need to understand uncertainty in such analyses for two reasons. Uncertainty analyses provide a range of possibilities within a framework of present scientific knowledge, thus helping to avoid undesirable consequences associated with the selected policies. And, it encourages the risk assessors to scrutinize all available data and models, thus helping avoid subjective or systematic errors. Without the full analysis of uncertainty, decisions could be biased by judgments based solely on default assumptions, beliefs, and statistical analyses of selected correlative data. Alternative data and theories that incorporate variability and heterogeneity for the human and murine superorganisms, particularly colonization resistance, are emerging as major influences for microbial risk assessment. Salmonellosis risk assessments are often based on conservative default models derived from selected sets of outbreak data that overestimate illness. Consequently, the full extent of uncertainty of estimates of annual number of illnesses is not incorporated in risk assessments and the presently used models may be incorrect.     

The Microbiome in Early Life: Self‐Completion and Microbiota Protection as Health Priorities

This minireview considers the benefits of refocusing attention away from treating the patient as a mammalian human to managing the complete patient: a majority microbial superorganism. Under the “completed self” model for formation of the human‐microbial superorganism, the single, most pivotal sign in distinguishing a life course of health versus that filled with disease is self‐completion (i.e., seeding of the minority mammalian human by the majority microbial portion of the symbiont). From a disease prevention perspective, microbial seeding at birth and subsequent nurturing of the microbiota are significant steps to reduce the risk of both noncommunicable diseases (e.g., type 1 diabetes) and certain infectious diseases. Management of the microbiome during pregnancy, birth, and shortly thereafter appears to be the most significant critical window for healthy superorganism formation. However, the bolus for microbiota seeding at birth and the nurturing process are subject to environmental influences and disruption, such as exposure to toxic chemicals and drugs, infections, and other physical and psychological stressors. Additionally, childhood and adult corrective measures, such as fecal transplantation and administration of prebiotics and probiotics, while potentially useful, may have limitations that are yet to be fully defined. This minireview considers (1) basic features of management of the microbiome to facilitate self‐completion, (2) protection of the microbiota from environmental hazards, and (3) the benefits of using a superorganism focus for health management beginning with pregnancy and extending throughout childhood and adult life     

Mechanistic Modeling of Salmonellosis

The serious limitation of the available human data contributes to the need for making simplifying assumptions for dose-response modeling which has led to frequent use of a single function, the beta-Poisson function, as a default dose-response model form. This function is a concave, low-dose linear function. Sub-linear or convex curves may be more appropriate for some host-pathogen interactions due to the series of highly regulated innate and acquired defense systems of the healthy human body that protect against most microbial challenges. A systematic investigation of the steps of non-typhoid salmonellosis in humans leads to biological motivations for sub-linear, or non-concave, dose-response curves in microbial risk assessment. Three phenomena were identified that might contribute to sub-linear, or non-concave, dose-response curves: (1) clumping of bacterial cells in microcolonies in a food matrix; (2) quorum sensing, or density-dependency in expression of virulence genes or other metabolic actions; and (3) need, at least in some circumstances, for multiple lesions for progression to symptomatic illness. This investigation suggests that microbial risk assessors should routinely employ a variety of model forms in addition to the commonly used beta-Poisson model to depict more fully the uncertainty of the true dose-response model.     

Elimination of tumor by CD47/PD-L1 dual-targeting fusion protein that engages innate and adaptive immune responses

The host immune system generally serves as a barrier against tumor formation. Programmed death-ligand 1 (PD-L1) is a critical “don't find me” signal to the adaptive immune system, whereas CD47 transmits an anti-phagocytic signal, known as the “don't eat me” signal, to the innate immune system. These and similar immune checkpoints are often overexpressed on human tumors. Thus, dual targeting both innate and adaptive immune checkpoints would likely maximize anti-tumor therapeutic effect and elicit more durable responses. Herein, based on the variable region of atezolizumab and consensus variant 1 (CV1) monomer, we constructed a dual-targeting fusion protein targeting both CD47 and PD-L1 using “Knobs-into-holes” technology, denoted as IAB. It was effective in inducing phagocytosis of tumor cells, stimulating T-cell activation and mediating antibody-dependent cell-mediated cytotoxicity in vitro. No obvious sign of hematological toxicity was observed in mice administered IAB at a dose of 100 mg/kg, and IAB exhibited potent antitumor activity in an immune-competent mouse model of MC38. Additionally, the anti-tumor effect of IAB was impaired by anti-CD8 antibody or clodronate liposomes, which implied that both CD8+ T cells and macrophages were required for the anti-tumor efficacy of IAB and IAB plays an essential role in the engagement of innate and adaptive immune responses. Collectively, these results demonstrate the capacity of an elicited endogenous immune response against tumors and elucidate essential characteristics of synergistic innate and adaptive immune response, and indicate dual blockade of CD47 and PD-L1 by IAB may be a synergistic therapy that activates both innate and adaptive immune response against tumors.     

Methods for evaluating variability in human health dose–response characterization

Abstract

The Reference Dose (RfD) and Reference Concentration (RfC) are human health reference values (RfVs) representing exposure concentrations at or below which there is presumed to be little risk of adverse effects in the general human population. The 2009 National Research Council report Science and Decisions recommended redefining RfVs as “a risk-specific dose (for example, the dose associated with a 1 in 100,000 risk of a particular end point).” Distributions representing variability in human response to environmental contaminant exposures are critical for deriving risk-specific doses. Existing distributions estimating the extent of human toxicokinetic and toxicodynamic variability are based largely on controlled human exposure studies of pharmaceuticals. New data and methods have been developed that are designed to improve estimation of the quantitative variability in human response to environmental chemical exposures. Categories of research with potential to provide new data useful for developing updated human variability distributions include controlled human experiments, human epidemiology, animal models of genetic variability, in vitro estimates of toxicodynamic variability, and in vitro-based models of toxicokinetic variability. In vitro approaches, with further development including studies of different cell types and endpoints, and approaches to incorporate non-genetic sources of variability, appear to provide the greatest opportunity for substantial near-term advances.     

Tumour size can have an impact on the outcomes of epidemiological studies on second cancers after radiotherapy

Obtaining a correct dose–response relationship for radiation-induced cancer after radiotherapy presents a major challenge for epidemiological studies. The purpose of this paper is to gain a better understanding of the associated uncertainties. To accomplish this goal, some aspects of an epidemiological study on breast cancer following radiotherapy of Hodgkin’s disease were simulated with Monte Carlo methods. It is demonstrated that although the doses to the breast volume are calculated by one treatment plan, the locations and sizes of the induced secondary breast tumours can be simulated and, based on these simulated locations and sizes, the absorbed doses at the site of tumour incidence can also be simulated. For the simulations of point dose at tumour site, linear and non-linear mechanistic models which predict risk of cancer induction as a function of dose were applied randomly to the treatment plan. These simulations provided for each second tumour and each simulated tumour size the predicted dose. The predicted-dose–response-characteristic from the analysis of the simulated epidemiological study was analysed. If a linear dose–response relationship for cancer induction was applied to calculate the theoretical doses at the simulated tumour sites, all Monte-Carlo realizations of the epidemiological study yielded strong evidence for a resulting linear risk to predicted-dose–response. However, if a non-linear dose–response of cancer induction was applied to calculate the theoretical doses, the Monte Carlo simulated epidemiological study resulted in a non-linear risk to predicted-dose–response relationship only if the tumour size was small (<?1.5 cm). If the diagnosed breast tumours exceeded an average diameter of 1.5 cm, an applied non-linear theoretical-dose–response relationship for second cancer falsely resulted in strong evidence for a linear predicted-dose relationship from the epidemiological study realizations. For a typical distribution of breast cancer sizes, the model selection probability for a resulting predicted-dose linear model was 61% although a non-linear theoretical-dose–response relationship for cancer induction had been applied. The results of this study, therefore, provide evidence that the shapes of epidemiologically obtained dose–response relationships for cancer induction can be biased by the finite size of the diagnosed second tumour, even though the epidemiological study was done correctly.     

Towards a processual microbial ontology

Standard microbial evolutionary ontology is organized according to a nested hierarchy of entities at various levels of biological organization. It typically detects and defines these entities in relation to the most stable aspects of evolutionary processes, by identifying lineages evolving by a process of vertical inheritance from an ancestral entity. However, recent advances in microbiology indicate that such an ontology has important limitations. The various dynamics detected within microbiological systems reveal that a focus on the most stable entities (or features of entities) over time inevitably underestimates the extent and nature of microbial diversity. These dynamics are not the outcome of the process of vertical descent alone. Other processes, often involving causal interactions between entities from distinct levels of biological organisation, or operating at different time scales, are responsible not only for the destabilisation of pre-existing entities, but also for the emergence and stabilisation of novel entities in the microbial world. In this article we consider microbial entities as more or less stabilised functional wholes, and sketch a network-based ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, and genetic exchanges. We use this pluralistic framework for evaluating (i) the existing ontological assumptions in evolution (e.g. whether currently recognized entities are adequate for understanding the causes of change and stabilisation in the microbial world), and (ii) for identifying hidden ontological kinds, essentially invisible from within a more limited perspective. We propose to recognize additional classes of entities that provide new insights into the structure of the microbial world, namely “processually equivalent” entities, “processually versatile” entities, and “stabilized” entities.     

Evaluation of genetic radiation hazards in man: History,present status and perspectives

The evaluation of genetic radiation hazards in man is an ongoing scientific enterprise from about the mid-1950s. Since estimates of genetic risks are essential for providing a basis for protecting our genetical endowment and since strictly relevant human data are limited, there is no alternative at present but to use the data from mouse and certain non-human primates. This paper reviews the general principles and methods that have thus far been used, appraises the evolution of the conceptual framework, the data base and the assumptions involved, presents current estimates of genetic risks and provides some perspective of the advances that are likely to be made in the near future. Currently, risk estimates are made using the so-called “direct method” and the “doubling dose method”. Both these methods involve a number of assumptions and consequent uncertainties. With the direct method, it is now estimated that following low LET, low dose-rate or low-dose irradiation of males, there will be (i) about 10–20 cases of affected children per million births per rad of exposure, who will suffer from the effects of induced mutations having dominant effects and (ii) about 1 to 10 cases of congenitally malformed children (again per million births per rad), a consequence of the induction of reciprocal translocations. For irradiation of females under similar conditions, the estimated risks are 0–9 and 0–3 affected children per million births per rad, these being the consequence of induction of dominant mutations and of reciprocal translocations, respectively. The doubling dose method is used to estimate risks to a population under continuous irradiation. If the population is exposed to low LET irradiation at a rate of 1 rad/generation (1 generation = 30 years), the expected total increments in the frequencies of genetic diseases are about 20 cases per million births in the first generation and about 150 cases per million births at equilibrium. These expected increments are very small fractions of the spontaneous prevalence of genetic and partially genetic disorders, currently estimated to be about 10.6 %.     

Autophagy and innate immunity: Triggering,targeting and tuning

Autophagy is a conserved catabolic stress response pathway that is increasingly recognized as an important component of both innate and acquired immunity to pathogens. The activation of autophagy during infection not only provides cell-autonomous protection through lysosomal degradation of invading pathogens (xenophagy), but also regulates signaling by other innate immune pathways. This review will focus on recent advances in our understanding of three major areas of the interrelationship between autophagy and innate immunity, including how autophagy is triggered during infection, how invading pathogens are targeted to autophagosomes, and how the autophagy pathway participates in “tuning” the innate immune response.     

Safety and risk assessment for the human superorganism

This editorial on safety evaluation and risk assessment for the human superorganism introduces a series of papers arguing for a fundamental shift in how we approach human health risk assessment. In this series emphasis is placed on the risk of infectious disease. Our 21^stst century understanding of human biology is that, as holobionts, we possess a majority of microbial cells and genes. In fact, our microbes fundamentally affect our interactions with the external environment, metabolism, physiology, and risk of both pathology and disease. As holobionts, we require our microbial partners for us to be both complete and healthy. Using the 20^th century understanding of human biology, we failed to capture the effects of the microbiome in evaluating health risks. But 21^st century risk assessments such as those associated with exposure to specific microbial pathogens need to include the human microbiome. Microbiome status will be central in determining the health risks for both individuals and populations.     

Newly arrived or previously overlooked: is there evidence for climate-driven changes in the distribution of molluscs in the Barents Sea?

In recent years, there have been frequent reports of invertebrate species newly recorded from particular areas of the Northeastern Atlantic, and it has often been suggested that these are the result of changes in species ranges due to recent warming. These suggestions make three assumptions: (1) that we have a good knowledge of the fauna of these areas; (2) that new records of “southern” species are more frequent than new records of “northern” species; (3) that climate change is the only factor affecting species range. I tested these assumptions on published records of 30 benthic molluscan species which have been found alive for the first time in the Russian part of the Barents Sea since 2006. Some of the discussed species are warm-water species and may have extended their ranges northward in response to climate change. However, our baseline knowledge of the molluscan fauna of this area before 2006 is limited by the frequent lack of molluscan specialists to study the available material, by the frequent lack of detailed publication and by changes in sampling and processing methods. New records of “southern” species are in fact not significantly commoner than new records of “northern” species. Also reasons other than climate change for observed changes in species distribution should be considered.     

U.S. Environmental Protection Agency's revised guidelines for carcinogen risk assessment: evaluating a postulated mode of carcinogenic action in guiding dose–response extrapolation

There are new opportunities to using data from molecular and cellular studies in order to bring together a fuller biological understanding of how chemicals induce neoplasia. In 1996, the Environmental Protection Agency (EPA) published a proposal to replace its 1986 Guidelines for Carcinogen Risk Assessment to take advantage of these new scientific advances in cancer biology. The analytical framework within the new guidelines focuses on an understanding of the mode of carcinogenic action. Mode of action data come into play in a couple of ways in these new guidelines. For example, such information can inform the dose–response relationship below the experimental observable range of tumours. Thus, mode of action data can be useful in establishing more appropriate guidance levels for environmental contaminants. It is the understanding of the biological processes that lead to tumour development along with the response data derived from experimental studies that can help discern the shape of the dose–response at low doses (linear vs. nonlinear). Because it is experimentally difficult to establish “true thresholds” from others with a nonlinear dose–response relationship, the proposed guidelines take a practical approach to depart from low-dose linear extrapolation procedures when there is sufficient experimental support for a mode of action consistent with nonlinear biological processes (e.g., tumours resulting from the disruption of normal physiological processes).     

Is “Wolf‐Pack” Predation by Antimicrobial Bacteria Cooperative? Cell Behaviour and Predatory Mechanisms Indicate Profound Selfishness,Even when Working Alongside Kin

For decades, myxobacteria have been spotlighted as exemplars of social “wolf‐pack” predation, communally secreting antimicrobial substances into the shared public milieu. This behavior has been described as cooperative, becoming more efficient if performed by more cells. However, laboratory evidence for cooperativity is limited and of little relevance to predation in a natural setting. In contrast, there is accumulating evidence for predatory mechanisms promoting “selfish” behavior during predation, which together with conflicting definitions of cooperativity, casts doubt on whether microbial “wolf‐pack” predation really is cooperative. Here, it is hypothesized that public‐goods‐mediated predation is not cooperative, and it is argued that a holistic model of microbial predation is needed, accounting for predator and prey relatedness, social phenotypes, spatial organization, activity/specificity/transport of secreted toxins, and prey resistance mechanisms. Filling such gaps in our knowledge is vital if the evolutionary benefits of potentially costly microbial behaviors mediated by public goods are to be properly understood.     

Stimulator of interferon genes (STING): A “new chapter” in virus-associated cancer research. Lessons from wild-derived mouse models of innate immunity

Thanks to the numerous studies that have been carried out recently in the field of cytosolic DNA sensing, STING (Stimulator of Interferon Genes) is now recognized as a key mediator of innate immune signaling. A substantial body of evidence derived from in vivo mouse models demonstrates that STING-regulated pathways underlie the pathogenesis of many diseases including infectious diseases and cancers. It has also become evident from these studies that STING is a promising therapeutic target for the treatment of cancer. However, mouse strains commonly used for modelling innate immune response against infections or tumors do not allow investigators to accurately reproduce certain specific characteristics of immune response observed in human cells. In this review, we will discuss recent data demonstrating that the use of wild-derived genetically distinct inbred mice as a model for investigation into the innate immunity signaling networks may provide valuable insight into the STING-regulated pathways specific for human cells. The maximum complexity of STING-mediated mechanisms can probably be seen in case of DNA virus-induced carcinogenesis in which STING may perform unexpected biological activities. Therefore, in another part of this review we will summarize emerging data on the role of STING in human DNA virus-related oncopathologies, with particular attention to HPV-associated cervical cancer, aiming to demonstrate that STING indeed “starts a new chapter” in research on this issue and that wild-derived mouse models of STING-mediated response to infections will probably be helpful in finding out molecular basis for clinical observations.     

More Accurate Dose-Response Estimation Using Monte-Carlo Uncertainty Analysis: The Data Cube Approach

The traditional q₁ ^* methodology for constructing upper confidence limits (UCLs) for the low-dose slopes of quantal dose-response functions has two limitations: (i) it is based on an asymptotic statistical result that has been shown via Monte Carlo simulation not to hold in practice for small, real bioassay experiments (Portier and Hoel, 1983); and (ii) it assumes that the multistage model (which represents cumulative hazard as a polynomial function of dose) is correct. This paper presents an uncertainty analysis approach for fitting dose-response functions to data that does not require specific parametric assumptions or depend on asymptotic results. It has the advantage that the resulting estimates of the dose-response function (and uncertainties about it) no longer depend on the validity of an assumed parametric family nor on the accuracy of the asymptotic approximation. The method derives posterior densities for the true response rates in the dose groups, rather than deriving posterior densities for model parameters, as in other Bayesian approaches (Sielken, 1991), or resampling the observed data points, as in the bootstrap and other resampling methods. It does so by conditioning constrained maximum-entropy priors on the observed data. Monte Carlo sampling of the posterior (constrained, conditioned) probability distributions generate values of response probabilities that might be observed if the experiment were repeated with very large sample sizes. A dose-response curve is fit to each such simulated dataset. If no parametric model has been specified, then a generalized representation (e.g., a power-series or orthonormal polynomial expansion) of the unknown dose-response function is fit to each simulated dataset using “model-free” methods. The simulation-based frequency distribution of all the dose-response curves fit to the simulated datasets yields a posterior distribution function for the low-dose slope of the dose-response curve. An upper confidence limit on the low-dose slope is obtained directly from this posterior distribution. This “Data Cube” procedure is illustrated with a real dataset for benzene, and is seen to produce more policy-relevant insights than does the traditional q₁ ^* methodology. For example, it shows how far apart are the 90%, 95%, and 99% limits and reveals how uncertainty about total and incremental risk vary with dose level (typically being dominated at low doses by uncertainty about the response of the control group, and being dominated at high doses by sampling variability). Strengths and limitations of the Data Cube approach are summarized, and potential decision-analytic applications to making better informed risk management decisions are briefly discussed.     

Expression of CD3 complexes by native and UV-irradiated T lymphocytes from human blood upon treatment with Leukocytic α-interferon

The influence of leukocytic α-interferon on the expression of CD3 complexes by native and UV-irradiated T cells from human blood has been studied. Irradiation at doses of 151–906 J/m² increased the quantity of CD3 complexes on the surface of their membranes, whereas a dose of 1359 J/m² decreased it. Subsequent incubation with the cytokine (0.01–100 IU/ml) could level off the stimulatory effect of “therapeutic” UV doses but reversed the CD3-suppressive effect of the maximal UV dose. These data may be important with regard to combined therapy.     

The current status of the adaptive response to ionizing radiation in mammalian cells

The results of numerous studies indicate that cells can become refractory to the detrimental effect of ionizing radiation when previously exposed to a low, “adapting dose”;. This phenomenon has been termed an “adaptive response”; to ionizing radiation. It has been postulated that the induced radioresistance is due to the induction of DNA repair systems which efficiently protect the adapted cells from the effects of a subsequent, high “challenging dose”;. However, a direct proof of this hypothesis is still lacking. The analyzed endpoints include chromosomal aberrations, survival, mutations, genetic instability and DNA damage repair measured by the comet assay. Frequently contradictory results were published by different authors. For example some authors observed a reduced frequency of apoptosis in adapted cells, whereas others reported the opposite. The source of variablity of the adaptive response in human lymphocytes remains unresolved. While there is no doubt that an adapting dose can trigger some protecting mechanisms within the cell it appears that there is no single, universal mechanism of the adaptive response that is valid for all cell types and irradiation conditions.     

Global quantitative proteomic analysis profiles host protein expression in response to Sendai virus infection

Sendai virus (SeV) is an enveloped nonsegmented negative‐strand RNA virus that belongs to the genus Respirovirus of the Paramyxoviridae family. As a model pathogen, SeV has been extensively studied to define the basic biochemical and molecular biologic properties of the paramyxoviruses. In addition, SeV‐infected host cells were widely employed to uncover the mechanism of innate immune response. To identify proteins involved in the SeV infection process or the SeV‐induced innate immune response process, system‐wide evaluations of SeV–host interactions have been performed. cDNA microarray, siRNA screening and phosphoproteomic analysis suggested that multiple signaling pathways are involved in SeV infection process. Here, to study SeV–host interaction, a global quantitative proteomic analysis was performed on SeV‐infected HEK 293T cells. A total of 4699 host proteins were quantified, with 742 proteins being differentially regulated. Bioinformatics analysis indicated that regulated proteins were mainly involved in “interferon type I (IFN‐I) signaling pathway” and “defense response to virus,” suggesting that these processes play roles in SeV infection. Further RNAi‐based functional studies indicated that the regulated proteins, tripartite motif (TRIM24) and TRIM27, affect SeV‐induced IFN‐I production. Our data provided a comprehensive view of host cell response to SeV and identified host proteins involved in the SeV infection process or the SeV‐induced innate immune response process.     

秀丽隐杆线虫固有免疫功能神经调控机制研究进展

固有免疫系统是动植物个体应对外来微生物侵入感染时非常重要的抵御防线。秀丽隐杆线虫(Caenorhabditis elegans,简称线虫)作为研究宿主与病原菌之间相互作用的经典模式动物,近年来在神经和免疫之间相互作用的分子与遗传机制等方面的研究取得了长足进展。研究表明,线虫神经元通过释放神经递质与神经多肽(如多巴胺、NLP-20)等,激活相关信号通路途经,参与线虫对病原菌的识别、逃避、调节物理屏障防御能力和激活固有免疫反应,并表达分泌抗菌肽以清除病原菌等的调控进程。本文综述了线虫神经系统调控固有免疫功能机制的最新研究进展,为人们深入了解神经与免疫系统间相互作用的功能分子及其调控机制和揭示人类神经与免疫系统相关疾病的病理机理提供了重要信息。