Combined In Silico,In Vivo,and In Vitro Studies Shed Insights into the Acute Inflammatory Response in Middle-Aged Mice

We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO₂ ^−/NO₃ ⁻ data from “middle-aged” (6–8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for “young” (2–3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging.     

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx,a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.     

Autophagy in intra-hepatic cholangiocarcinoma

Intra-hepatic cholangiocarcinoma (IHCC) is a primary cancer of the liver that shares histological features with the hepatic bile ducts from which it is thought to arise. The incidence of this disease is increasing, possibly related to increased inflammatory liver diseases such as viral hepatitis and steatohepatitis (commonly called “fatty liver”), induced by obesity, diabetes, and other metabolic derangements. Its prognosis is generally poor with early metastasis and presently there are limited effective treatments. A basic understanding of the disease has long been hampered by limited tumor cell lines and good model systems. Similarly, such limitations have obstructed new therapeutic inroads.     

Amelioration of human osteoarthritis symptoms with topical ‘biotherapeutics’: a phase I human trial

Osteoarthritis (OA) treatments presently rely on analgesics, which manage pain but fail to restore imbalances between catabolic and anabolic processes that underlie OA pathogenesis. Recently, biologic (biotherapeutic) drugs, which alter the activity of catabolic agents such as nitric oxide and inflammatory cytokines in ways, allowing tissue regeneration, were evaluated for efficacy in OA treatment. These studies failed to demonstrate dramatic abatement of OA symptoms by these drugs, but suggested strategies by which biologic agents might be used to treat OA. The present review summarizes current understanding of OA pathogenesis and evolving treatments. Preliminary evaluations of a novel biotherapeutic strategy are presented here. Twenty OA patients receiving sour topical cherry seed extract (SCE), an inducer of heme oxygenase-1 (HO-1), a major physiological protectant against oxidative stress exhibited significantly decreased joint pain and activation of CD4+ T cells expressing inflammatory cytokines (p < 0.05), significantly decreased peripheral blood C-reactive protein (CRP), and increased leukocyte HO-1 (p < 0.05) in comparison with ten placebo-treated patients. SCE inhibits joint-damaging inflammatory mediator production. This agent therefore meets the main criterion for classification as a “biotherapeutic,” or “biologic” agent. The negligible toxicity and low cost of such materials make them promising contributors to OA treatment, sustainable within resource limitations of a wide range of patients.     

CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.     

Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM)

In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression. The result suggested a specific expression signature nine miRNAs (hsa-mir-21, hsa-mir-29a, hsa-mir-29c, hsa-mir-148a, hsa-mir-141, hsa-let-7b, hsa-mir-31, hsa-mir-451, and hsa-mir-192), all with significant values (p-value < 0.01 and fold change > 5) that clustered the samples into two groups: healthy tissue and gastric cancer tissue. The results obtained “in silico” must be validated in a molecular biology laboratory; if confirmed, this method may be used in the future as a risk marker for gastric cancer development.     

Amplification Biases and Consistent Recovery of Loci in a Double-Digest RAD-seq Protocol

A growing variety of “genotype-by-sequencing” (GBS) methods use restriction enzymes and high throughput DNA sequencing to generate data for a subset of genomic loci, allowing the simultaneous discovery and genotyping of thousands of polymorphisms in a set of multiplexed samples. We evaluated a “double-digest” restriction-site associated DNA sequencing (ddRAD-seq) protocol by 1) comparing results for a zebra finch (Taeniopygia guttata) sample with in silico predictions from the zebra finch reference genome; 2) assessing data quality for a population sample of indigobirds (Vidua spp.); and 3) testing for consistent recovery of loci across multiple samples and sequencing runs. Comparison with in silico predictions revealed that 1) over 90% of predicted, single-copy loci in our targeted size range (178–328 bp) were recovered; 2) short restriction fragments (38–178 bp) were carried through the size selection step and sequenced at appreciable depth, generating unexpected but nonetheless useful data; 3) amplification bias favored shorter, GC-rich fragments, contributing to among locus variation in sequencing depth that was strongly correlated across samples; 4) our use of restriction enzymes with a GC-rich recognition sequence resulted in an up to four-fold overrepresentation of GC-rich portions of the genome; and 5) star activity (i.e., non-specific cutting) resulted in thousands of “extra” loci sequenced at low depth. Results for three species of indigobirds show that a common set of thousands of loci can be consistently recovered across both individual samples and sequencing runs. In a run with 46 samples, we genotyped 5,996 loci in all individuals and 9,833 loci in 42 or more individuals, resulting in <1% missing data for the larger data set. We compare our approach to similar methods and discuss the range of factors (fragment library preparation, natural genetic variation, bioinformatics) influencing the recovery of a consistent set of loci among samples.     

Higher Landing Accuracy in Expert Pilots is Associated with Lower Activity in the Caudate Nucleus

The most common lethal accidents in General Aviation are caused by improperly executed landing approaches in which a pilot descends below the minimum safe altitude without proper visual references. To understand how expertise might reduce such erroneous decision-making, we examined relevant neural processes in pilots performing a simulated landing approach inside a functional MRI scanner. Pilots (aged 20–66) were asked to “fly” a series of simulated “cockpit view” instrument landing scenarios in an MRI scanner. The scenarios were either high risk (heavy fog–legally unsafe to land) or low risk (medium fog–legally safe to land). Pilots with one of two levels of expertise participated: Moderate Expertise (Instrument Flight Rules pilots, n = 8) or High Expertise (Certified Instrument Flight Instructors or Air-Transport Pilots, n = 12). High Expertise pilots were more accurate than Moderate Expertise pilots in making a “land” versus “do not land” decision (CFII: d′ = 3.62±2.52; IFR: d′ = 0.98±1.04; p<.01). Brain activity in bilateral caudate nucleus was examined for main effects of expertise during a “land” versus “do not land” decision with the no-decision control condition modeled as baseline. In making landing decisions, High Expertise pilots showed lower activation in the bilateral caudate nucleus (0.97±0.80) compared to Moderate Expertise pilots (1.91±1.16) (p<.05). These findings provide evidence for increased “neural efficiency” in High Expertise pilots relative to Moderate Expertise pilots. During an instrument approach the pilot is engaged in detailed examination of flight instruments while monitoring certain visual references for making landing decisions. The caudate nucleus regulates saccade eye control of gaze, the brain area where the “expertise” effect was observed. These data provide evidence that performing “real world” aviation tasks in an fMRI provide objective data regarding the relative expertise of pilots and brain regions involved in it.     

Phylogenetic and Evolutionary Patterns in Microbial Carotenoid Biosynthesis Are Revealed by Comparative Genomics

Background

Carotenoids are multifunctional, taxonomically widespread and biotechnologically important pigments. Their biosynthesis serves as a model system for understanding the evolution of secondary metabolism. Microbial carotenoid diversity and evolution has hitherto been analyzed primarily from structural and biosynthetic perspectives, with the few phylogenetic analyses of microbial carotenoid biosynthetic proteins using either used limited datasets or lacking methodological rigor. Given the recent accumulation of microbial genome sequences, a reappraisal of microbial carotenoid biosynthetic diversity and evolution from the perspective of comparative genomics is warranted to validate and complement models of microbial carotenoid diversity and evolution based upon structural and biosynthetic data.

Methodology/Principal Findings

Comparative genomics were used to identify and analyze in silico microbial carotenoid biosynthetic pathways. Four major phylogenetic lineages of carotenoid biosynthesis are suggested composed of: (i) Proteobacteria; (ii) Firmicutes; (iii) Chlorobi, Cyanobacteria and photosynthetic eukaryotes; and (iv) Archaea, Bacteroidetes and two separate sub-lineages of Actinobacteria. Using this phylogenetic framework, specific evolutionary mechanisms are proposed for carotenoid desaturase CrtI-family enzymes and carotenoid cyclases. Several phylogenetic lineage-specific evolutionary mechanisms are also suggested, including: (i) horizontal gene transfer; (ii) gene acquisition followed by differential gene loss; (iii) co-evolution with other biochemical structures such as proteorhodopsins; and (iv) positive selection.

Conclusions/Significance

Comparative genomics analyses of microbial carotenoid biosynthetic proteins indicate a much greater taxonomic diversity then that identified based on structural and biosynthetic data, and divides microbial carotenoid biosynthesis into several, well-supported phylogenetic lineages not evident previously. This phylogenetic framework is applicable to understanding the evolution of specific carotenoid biosynthetic proteins or the unique characteristics of carotenoid biosynthetic evolution in a specific phylogenetic lineage. Together, these analyses suggest a “bramble” model for microbial carotenoid biosynthesis whereby later biosynthetic steps exhibit greater evolutionary plasticity and reticulation compared to those closer to the biosynthetic “root”. Structural diversification may be constrained (“trimmed”) where selection is strong, but less so where selection is weaker. These analyses also highlight likely productive avenues for future research and bioprospecting by identifying both gaps in current knowledge and taxa which may particularly facilitate carotenoid diversification.     

The Convallis Rule for Unsupervised Learning in Cortical Networks

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.     

Insertion Sequence Element Single Nucleotide Polymorphism Typing Provides Insights into the Population Structure and Evolution of Mycobacterium ulcerans across Africa

Buruli ulcer is an indolent, slowly progressing necrotizing disease of the skin caused by infection with Mycobacterium ulcerans. In the present study, we applied a redesigned technique to a vast panel of M. ulcerans disease isolates and clinical samples originating from multiple African disease foci in order to (i) gain fundamental insights into the population structure and evolutionary history of the pathogen and (ii) disentangle the phylogeographic relationships within the genetically conserved cluster of African M. ulcerans. Our analyses identified 23 different African insertion sequence element single nucleotide polymorphism (ISE-SNP) types that dominate in different areas where Buruli ulcer is endemic. These ISE-SNP types appear to be the initial stages of clonal diversification from a common, possibly ancestral ISE-SNP type. ISE-SNP types were found unevenly distributed over the greater West African hydrological drainage basins. Our findings suggest that geographical barriers bordering the basins to some extent prevented bacterial gene flow between basins and that this resulted in independent focal transmission clusters associated with the hydrological drainage areas. Different phylogenetic methods yielded two well-supported sister clades within the African ISE-SNP types. The ISE-SNP types from the “pan-African clade” were found to be widespread throughout Africa, while the ISE-SNP types of the “Gabonese/Cameroonian clade” were much rarer and found in a more restricted area, which suggested that the latter clade evolved more recently. Additionally, the Gabonese/Cameroonian clade was found to form a strongly supported monophyletic group with Papua New Guinean ISE-SNP type 8, which is unrelated to other Southeast Asian ISE-SNP types.     

A Computational Model of Lipopolysaccharide-Induced Nuclear Factor Kappa B Activation: A Key Signalling Pathway in Infection-Induced Preterm Labour

Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be “tested”. Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.     

Clinical Presentation of Acute Abdomen: Study of 600 Patients

This paper presents the clinical features of 600 patients suffering from abdominal pain of acute onset and admitted to either the General Infirmary or St. James''s Hospital, Leeds. The survey was initially retrospective, but later put on a prospective basis. Roughly two-thirds of these 600 patients presented a “typical” picture of the disease with which they presented, while the remaining third presented one or more atypical features. Since other prospective studies have indicated that the diagnostic accuracy of a group of clinicians in respect of the acute abdomen is roughly 65% it is tentatively suggested (a) that clinical diagnosis contains a large element of “pattern-matching,” and (b) that such a policy can be expected to be ineffective in roughly one-third of all cases of acute abdominal pain.     

From simple to detailed models for cell polarization

Many mathematical models have been proposed for the process of cell polarization. Some of these are ‘functional models’ that capture a class of dynamical behaviour, whereas others are derived from features of signalling molecules. Some mechanistic models are detailed, and therefore complex, whereas others are simplified. Each type contributes to our understanding of cell polarization. However, the huge variety at different levels of detail makes comparisons challenging. Here, we provide examples of both elementary and more detailed models for polarization. We also display how a recent mathematical method, local perturbation analysis, can provide an appropriate tool for such comparisons. This technique simplifies and speeds up the model development process by revealing the effect of model extensions, parameter variations and in silico manipulations such as knock-out or over-expression of key molecules. Finally, simulations in both one dimension and two dimensions, and particularly in deforming two-dimensional ‘cells’, can highlight behaviour not captured by traditional simulation methods.     

Post-Gastrectomy Problems in Patients with Personality Defects: The “Albatross” Syndrome

Of a series of 130 patients undergoing operation for peptic ulcer disease at the Vancouver General Hospital, seven patients with personality defects had a disastrous outcome after operation.The main features of this postgastrectomy syndrome were remarkably similar: persistent abdominal pain without demonstrable cause, intermittent and inexplicable nausea and vomiting, continued analgesic drug dependence and marked nutritional deficiencies. The high incidence was surprising and was not confined to any particular socioeconomic group. Such patients fall into three groups: those with true ulcer disease, those with salicylate addiction, and those without positive signs of ulcer but with chronic complaints. A history of a personality defect should warn the surgeon, and operation should be performed only for the complications of true ulcer disease. Though operation may cure the ulcer, the patient is worse off because the resulting physiologic derangements cannot be accepted or handled by him. These patients continue to haunt the surgeon, and the syndrome has been named the “albatross” syndrome.     

Fungal Parasitism: Life Cycle,Dynamics and Impact on Cyanobacterial Blooms

Many species of phytoplankton are susceptible to parasitism by fungi from the phylum Chytridiomycota (i.e. chytrids). However, few studies have reported the effects of fungal parasites on filamentous cyanobacterial blooms. To investigate the missing components of bloom ecosystems, we examined an entire field bloom of the cyanobacterium Anabaena macrospora for evidence of chytrid infection in a productive freshwater lake, using a high resolution sampling strategy. A. macrospora was infected by two species of the genus Rhizosiphon which have similar life cycles but differed in their infective regimes depending on the cellular niches offered by their host. R. crassum infected both vegetative cells and akinetes while R. akinetum infected only akinetes. A tentative reconstruction of the developmental stages suggested that the life cycle of R. crassum was completed in about 3 days. The infection affected 6% of total cells (and 4% of akinètes), spread over a maximum of 17% of the filaments of cyanobacteria, in which 60% of the cells could be parasitized. Furthermore, chytrids may reduce the length of filaments of Anabaena macrospora significantly by “mechanistic fragmentation” following infection. All these results suggest that chytrid parasitism is one of the driving factors involved in the decline of a cyanobacteria blooms, by direct mortality of parasitized cells and indirectly by the mechanistic fragmentation, which could weaken the resistance of A. macrospora to grazing.     

Introduction to the Thematic Minireview Series on Radical S-Adenosylmethionine (SAM) Enzymes

In the early days, radical enzyme reactions that use S-adenosylmethionine (SAM) coordinated to an Fe-S cluster, which Perry Frey described as a “poor man''s coenzyme B₁₂,” were believed to be relatively rare chemical curiosities. Today, bioinformatics analyses have revealed the wide prevalence and sheer numbers of radical SAM enzymes, conferring superfamily status. In this thematic minireview series, the JBC presents six articles on radical SAM enzymes that accomplish wide-ranging chemical transformations. We learn that despite the diversity of the reactions catalyzed, family members share some common structural and mechanistic themes. Still in its infancy, continued explorations promise to be fertile grounds for discoveries that will undoubtedly further broaden our understanding of the catalytic repertoire and deepen our understanding of the chemical strategies used by radical SAM enzymes.     

ADAM17 is a survival factor for microglial cells in vitro and in vivo after spinal cord injury in mice

A disintegrin and metalloprotease 17 (ADAM17) is a sheddase with important substrates including tumor necrosis factor-α (TNF-α) and its receptors, the p75 neurotrophin receptor (p75NTR), and members of the epidermal growth factor family. The rationale of this study was to inhibit ADAM17-induced shedding of soluble TNF-α in order to reduce detrimental inflammation after spinal cord injury (SCI). However, using the specific ADAM17 blocker BMS-561392 in neuronal and glial cell cultures, we show that proper functioning of ADAM17 is vital for oligodendrocyte and microglia survival in a p44 MAPK-dependent manner. In contrast, genetic ablation of ADAM17 specifically increases microglial death. Surprisingly, although blocking ADAM17 in vivo does not substantially change the ratio between membrane-bound and soluble TNF-α, it increases expression of the pro-apoptotic marker Bax and microglial apoptosis while impairing functional recovery after SCI. These data suggest that ADAM17 is a key survival factor for microglial cells after SCI.