An Automated Brain Metastasis Detection and Segmentation System from MRI with a Large Multi-Institutional Dataset

PURPOSE/OBJECTIVE(S)

Developments of automated systems for brain metastasis (BM) detection and segmentation from MRI for assisting early detection and stereotactic radiosurgery (SRS) have been reported but most based upon relatively small datasets from single institutes. This work aims to develop and evaluate a system using a large multi-institutional dataset, and to improve both identification of small/subtle BMs and segmentation accuracy of large BMs.

MATERIALS/METHODS

A 3D U-Net system was trained and evaluated to detect and segment intraparenchymal BMs with a size > 2mm using 1856 MRI volumes from 1791 patients treated with SRS from seven institutions (1539 volumes for training, 183 for validation, and 134 for testing). All patients had 3D post-Gd T1w MRI scans pre-SRS. Gross tumor volumes (GTVs) of BMs for SRS were curated by each institute first. Then, additional efforts were spent to create GTVs for the untreated and/or uncontoured BMs, including central reviews by two radiologists, to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 3773 MRIs using a 3D generative pipeline. Our system consists of two U-Nets with one using small 3D patches dedicated for detecting small BMs and another using large 3D patches for segmenting large BMs, and a random-forest based fusion module for combining the two network outputs. The first U-Net was trained with 3D patches containing at least one BM < 0.1 cm3. For detection performance, we measured BM-level sensitivity and case-level false-positive (FP) rate. For segmentation performance, we measured BM-level Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95). We also stratified performances based upon BM sizes.

RESULTS

For 739 BMs in the 134 testing cases, the overall lesion-level sensitivity was 0.870 with an average case-level FP of 1.34±1.92 (95% CI: 1.02-1.67). The sensitivity was >0.969 for the BMs >0.1 cm3, but dropped to 0.755 for the BMs < 0.1 cm3 (Table 1). The average DSC and HD95 for all detected BMs were 0.786 and 1.35mm. The worse performance for BMs > 20 cm3 was caused by a case with 83 cm3 GTV and artifacts in the MRI volume.

CONCLUSION

We achieved excellent detection sensitivity and segmentation accuracy for BMs > 0.1 cm3, and promising performance for small BMs (<0.1cm3) with a controlled FP rate using a large multi-institutional dataset. Clinical utility for assisting early detection and SRS planning will be investigated. Table 1: Per-lesion detection and segmentation performance stratified by individual BM size. N is the number of BMs in each category.

PASCAR: a multiscale framework to explore the design space of constitutive and inducible CAR T cells

CAR T cells are engineered to bind and destroy tumor cells by targeting overexpressed surface antigens. However, healthy cells expressing lower abundances of these antigens can also be lysed by CAR T cells. Various CAR T cell designs increase tumor cell elimination, whereas reducing damage to healthy cells. However, these efforts are costly and labor-intensive, constraining systematic exploration of potential hypotheses. We develop a protein abundance structured population dynamic model for CAR T cells (PASCAR), a framework that combines multiscale population dynamic models and multi-objective optimization approaches with data from cytometry and cytotoxicity assays to systematically explore the design space of constitutive and tunable CAR T cells. PASCAR can quantitatively describe in vitro and in vivo results for constitutive and inducible CAR T cells and can successfully predict experiments outside the training data. Our exploration of the CAR design space reveals that optimal CAR affinities in the intermediate range of dissociation constants effectively reduce healthy cell lysis, whereas maintaining high tumor cell-killing rates. Furthermore, our modeling offers guidance for optimizing CAR expressions in synthetic notch CAR T cells. PASCAR can be extended to other CAR immune cells.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

McSNAC: A software to approximate first-order signaling networks from mass cytometry data

Background

Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.

Methods

We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.

Results

We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.

Conclusions

These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Coping strategies and barriers to coping in climate- vulnerable Bangladesh: a qualitative study

 

With the mental wellbeing of billions of people at risk due to climate change, more research is required to better understand mental health and psychological implications of climate vulnerability. This research contributes to understanding how people in climate vulnerable populations psychologically cope with stress with crucial implications for adaptation efforts. We conducted n = 60 qualitative in-depth interviews with men and women in Bhola, Bangladesh to elicit the lived experiences of a climate vulnerable population. We analysed data following the tenets of Grounded Theory. Through our inductive analysis, we found coping strategies where participants highlighted what they did when encountering stress, such as ‘Resignation’ or ‘Help Seeking'. Barriers to coping were, among others, limited ‘Efficacy', ‘Time’ or ‘Stigma'. We categorized coping strategies with barriers as high-barrier coping strategies and, those without reported barriers, as low- barrier coping strategies. High-barriers restricted participants - especially women - in their coping efforts and led them to using low-barrier coping strategies. Some low-barrier coping strategies can be interpreted as maladaptive if used frequently, as they are unhealthy and draw upon resources needed to adapt for the future. Maladaptive coping strategies can thus impede long-term adaptation by reducing motivation and the ability and willingness to act. To enable adaptive coping, we recommend lifting the barriers to coping through community-led interventions where community workers create platforms for sharing problems and knowledge, such as group support meetings. Sharing and discussing could strengthen efficacy and open new opportunities for functional, adaptive coping. As the negative impacts of climate change will be felt globally with more intensity and frequency, enabling adaptive coping and removing barriers to coping in frontline communities will be essential to supporting physical and mental wellbeing.

Key messages

• Barriers to adaptive coping strategies can lead people to using maladaptive low-barrier coping strategies, which draw upon resources needed for long-term adaptation.

• Enabling adaptive coping by lifting barriers to coping in climate vulnerable populations is crucial to strengthen adaptation efforts.

Selective targeting of GARP-LTGFβ axis in the tumor microenvironment augments PD-1 blockade via enhancing CD8+ T cell antitumor immunity

BACKGROUND

Immune checkpoint blockade (ICB) has revolutionized cancer immunotherapy. However, most patients with cancer fail to respond clinically. One potential reason is the accumulation of immunosuppressive transforming growth factor β (TGFβ) in the tumor microenvironment (TME). TGFβ drives cancer immune evasion in part by inducing regulatory T cells (Tregs) and limiting CD8+ T cell function. Glycoprotein-A repetitions predominant (GARP) is a cell surface docking receptor for activating latent TGFβ1, TGFβ2 and TGFβ3, with its expression restricted predominantly to effector Tregs, cancer cells, and platelets.

METHODS

We investigated the role of GARP in human patients with cancer by analyzing existing large databases. In addition, we generated and humanized an anti-GARP monoclonal antibody and evaluated its antitumor efficacy and underlying mechanisms of action in murine models of cancer.

RESULTS

We demonstrate that GARP overexpression in human cancers correlates with a tolerogenic TME and poor clinical response to ICB, suggesting GARP blockade may improve cancer immunotherapy. We report on a unique anti-human GARP antibody (named PIIO-1) that specifically binds the ligand-interacting domain of all latent TGFβ isoforms. PIIO-1 lacks recognition of GARP-TGFβ complex on platelets. Using human LRRC32 (encoding GARP) knock-in mice, we find that PIIO-1 does not cause thrombocytopenia; is preferentially distributed in the TME; and exhibits therapeutic efficacy against GARP+ and GARP- cancers, alone or in combination with anti-PD-1 antibody. Mechanistically, PIIO-1 treatment reduces canonical TGFβ signaling in tumor-infiltrating immune cells, prevents T cell exhaustion, and enhances CD8+ T cell migration into the TME in a C-X-C motif chemokine receptor 3 (CXCR3)-dependent manner.

CONCLUSION

GARP contributes to multiple aspects of immune resistance in cancer. Anti-human GARP antibody PIIO-1 is an efficacious and safe strategy to block GARP-mediated LTGFβ activation, enhance CD8+ T cell trafficking and functionality in the tumor, and overcome primary resistance to anti-PD-1 ICB. PIIO-1 therefore warrants clinical development as a novel cancer immunotherapeutic.

Spatially resolved in silico modeling of NKG2D signaling kinetics suggests a key role of NKG2D and Vav1 Co-clustering in generating natural killer cell activation

Natural Killer (NK) cells provide key resistance against viral infections and tumors. A diverse set of activating and inhibitory NK cell receptors (NKRs) interact with cognate ligands presented by target host cells, where integration of dueling signals initiated by the ligand-NKR interactions determines NK cell activation or tolerance. Imaging experiments over decades have shown micron and sub-micron scale spatial clustering of activating and inhibitory NKRs. The mechanistic roles of these clusters in affecting downstream signaling and activation are often unclear. To this end, we developed a predictive in silico framework by combining spatially resolved mechanistic agent based modeling, published TIRF imaging data, and parameter estimation to determine mechanisms by which formation and spatial movements of activating NKG2D microclusters affect early time NKG2D signaling kinetics in a human cell line NKL. We show co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in NKG2D microclusters plays a dominant role over ligand (ULBP3) rebinding in increasing production of phospho-Vav1(pVav1), an activation marker of early NKG2D signaling. The in silico model successfully predicts several scenarios of inhibition of NKG2D signaling and time course of NKG2D spatial clustering over a short (~3 min) interval. Modeling shows the presence of a spatial positive feedback relating formation and centripetal movements of NKG2D microclusters, and pVav1 production offers flexibility towards suppression of activating signals by inhibitory KIR ligands organized in inhomogeneous spatial patterns (e.g., a ring). Our in silico framework marks a major improvement in developing spatiotemporal signaling models with quantitatively estimated model parameters using imaging data.

Natural Killer cells are lymphocytes of our innate immunity and provide important resistance against viral infections and tumors. NK cells scan the local environment with diverse activating and inhibitory NK cell receptors (NKRs) and remain tolerized or lyse target cells expressing cognate ligands to NKRs. NKRs have been found to form micron sized clusters (or microclusters) as they interact with cognate ligands, and mechanisms regarding how the formation and movements of these microclusters influence NK cell signaling and activation, specifically related to activating NKRs, are often unclear. To this end, we develop a predictive spatially resolved early-time NK cell signaling model to study the interplay between membrane-proximal biochemical signaling events and the kinetics of microclusters of activating NKG2D and inhibitory KIR2DL2 receptors. We used published TIRF imaging data to validate our in silico models and estimate model parameters. Predictions from multiple in silico models are tested against a variety of data obtained from published imaging experiments and immunoassays. Our analysis suggests co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in the microclusters plays a major role in enhancing downstream activating signals. The developed framework can be extended to describe spatiotemporal signaling for other activating NKRs including CD16.

Dynamic variability in SHP-1 abundance determines natural killer cell responsiveness

[Figure: see text].

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

Assessment of cosmesis and quality of life and their correlation using novel, validated methods in breast cancer patients who had undergone breast conservation therapy

PURPOSE

To assess cosmesis [Breast Cancer Conservation Treatment. Cosmetic Results (BCCT.CORE) software] & QOL [EORTC QLQ (Quality of Life Questionnaire)-C30, BR23 questionnaires] using objective methods &to correlate effect of cosmesis on QOL (Quality of life) in breast cancer patients post BCT (Breast conservation therapy) using a cross-sectional observational study from a tertiary hospital.

MATERIALS & METHODS

Cosmesis & QOL had been assessed in all biopsy-proven adult patients of unilateral breast cancer, who had undergone BCT with minimum three months post-BCT & three months post last adjuvant chemotherapy cycle, whichever is delivered last (with just hormonal therapy or followup remaining based on hormone receptor status). Pearson chi-square & Kendall correlation Tau-b tests had been used for testing association between Cosmesis & QOL. Variation of QOL scores between Cosmetic groups had been assessed using Mann-Whitney U-test.

RESULTS

Cosmesis was Excellent/Good in 60.4 % of patients. 58.4 % of patients had Excellent/Good Quality of Life. Cosmesis & QOL had a positive correlation (intermediate strength) [Kendall correlation factor-0.484]. QOL-Global scale had better median scores in Excellent/ Good cosmetic group when compared to Fair/Poor group, while for functional & symptomatic scales, contrary had been true. Difference of median-scores between the two groups hadn't been statistically significant in five aspects - being upset over hair loss (p-0.09), sexual-enjoyment (p-0.33), nausea-vomiting (p-0.29), constipation (p0.32) & diarrhea (p-0.72).

CONCLUSION

About 2/3rds of patients post-BCT had Excellent/Good Cosmesis & Excellent/Good QOL. Every measure taken before, during & post-BCT to improve Cosmesis might result in better QOL. Future studies must use more accurate three-dimensional methods to assess Cosmesis & its effect on QOL.

Genome-wide identification and expression analysis of the plant specific LIM genes in Gossypium arboreum under phytohormone, salt and pathogen stress

Asiatic cotton (Gossypium arboreum) cultivated as ‘desi cotton’ in India, is renowned for its climate resilience and robustness against biotic and abiotic stresses. The genome of G. arboreum is therefore, considered as a valued reserve of information for discovering novel genes or gene functions for trait improvements in the present context of cotton cultivation world-wide. In the present study, we carried out genome-wide analysis of LIM gene family in desi cotton and identified twenty LIM domain proteins (GaLIMs) which include sixteen animals CRP-like GaLIMs and four plant specific GaLIMs with presence (GaDA1) or absence (GaDAR) of UIM (Ubiquitin Interacting Motifs). Among the sixteen CRP-like GaLIMs, eleven had two conventional LIM domains while, five had single LIM domain which was not reported in LIM gene family of the plant species studied, except in Brassica rapa. Phylogenetic analysis of these twenty GaLIM proteins in comparison with LIMs of Arabidopsis, chickpea and poplar categorized them into distinct αLIM1, βLIM1, γLIM2, δLIM2 groups in CRP-like LIMs, and GaDA1 and GaDAR in plant specific LIMs group. Domain analysis had revealed consensus [(C-X₂-C-X₁₇-H-X₂-C)-X₂-(C-X₂-C-X₁₇-C-X₂-H)] and [(C-X₂-C-X₁₇-H-X₂-C)-X₂-(C-X₄-C-X₁₅-C-X₂-H)] being conserved as first and/or second LIM domains of animal CRP-like GaLIMs, respectively. Interestingly, single LIM domain containing GaLIM15 was found to contain unique consensus with longer inter-zinc-motif spacer but shorter second zinc finger motif. All twenty GaLIMs showed variable spatio-temporal expression patterns and accordingly further categorized into distinct groups of αLIM1, βLIM1, γLIM2 δLIM2 and plant specific LIM (DA1/DAR). For the first time, response of GaDA1/DAR under the influence of biotic and abiotic stresses were studied in cotton, involving treatments with phytohormones (Jasmonic acid and Abscisic acid), salt (NaCl) and wilt causing pathogen (Fusarium oxysporum). Expressions patterns of GaDA1/DAR showed variable response and identified GaDA2 as a probable candidate gene for stress tolerance in G. arboreum.

Determining clinically relevant features in cytometry data using persistent homology

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. Our method identifies proteins of interest by a decision-tree based classifier and passes them to a kernel-density estimator (KDE) for sampling points from the density distribution. We then compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or in the presence of large batch effects.

Author summary

Identifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply persistent homology used in topological data analysis to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.

Autologous haematopoietic stem cell transplantation as a first-line disease-modifying therapy in patients with 'aggressive' multiple sclerosis

BACKGROUND

Autologous haematopoietic stem cell transplantation (AHSCT) is an effective treatment for patients with multiple sclerosis (MS) who have highly active disease, despite the use of standard disease-modifying therapies (DMTs). However, the optimal time for offering AHSCT to patients with 'aggressive' MS is yet to be established.

OBJECTIVES

The objective was to explore the safety and efficacy of AHSCT as a first-line DMT in patients with 'aggressive' MS.

METHODS

All patients with 'aggressive' MS who received AHSCT as a first-line DMT in five European and North American centres were retrospectively evaluated.

RESULTS

Twenty patients were identified. The median interval between diagnosis and AHSCT was 5 (1-20) months. All had multiple poor prognostic markers with a median pre-transplant Expanded Disability Status Scale (EDSS) score of 5.0 (1.5-9.5). After a median follow-up of 30 (12-118) months, the median EDSS score improved to 2.0 (0-6.5, p < 0.0001). No patient had further relapses. Three had residual magnetic resonance imaging (MRI) disease activities in the first 6 months post-transplant, but no further new or enhancing lesions were observed in subsequent scans.

CONCLUSION

AHSCT is safe and effective as a first-line DMT in inducing rapid and sustained remission in patients with 'aggressive' MS.

Role of nanoscale antigen organization on B-cell activation probed using DNA origami

Vaccine efficacy can be increased by arraying immunogens in multivalent form on virus-like nanoparticles to enhance B-cell activation. However, the effects of antigen copy number, spacing and affinity, as well as the dimensionality and rigidity of scaffold presentation on B-cell activation remain poorly understood. Here, we display the clinical vaccine immunogen eOD-GT8, an engineered outer domain of the HIV-1 glycoprotein-120, on DNA origami nanoparticles to systematically interrogate the impact of these nanoscale parameters on B-cell activation in vitro. We find that B-cell signalling is maximized by as few as five antigens maximally spaced on the surface of a 40-nm viral-like nanoparticle. Increasing antigen spacing up to ~25-30 nm monotonically increases B-cell receptor activation. Moreover, scaffold rigidity is essential for robust B-cell triggering. These results reveal molecular vaccine design principles that may be used to drive functional B-cell responses.

Energy saving and Fuzzy-PID position control of electro-hydraulic system by leakage compensation through proportional flow control valve

In this article, the focus is on the energy efficiency along with the position control of the linear actuator used in heavy earth moving equipment. It is quite evident that linear actuators are one of the critical machinery components used in the construction and mining activities like in booms of excavation equipment. The proposed work employed two different hydraulic circuits and a contrast has been carried out in terms of the energy efficiency. In one hydraulic circuit, the conventional proportional directional control valve (PDCV) is used for the position control. In another one, an innovative solution of using proportional flow control valve (PFCV) by creating artificial leakage between the two ends of the actuator is evaluated according to its energy efficiency. The extra flow coming from the pump during position control is by-passed by PFCV rather than the pressure relief valve in PDCV. This reduces the energy loss in the form of heat and increases the efficiency of the hydraulic circuit. The simulation of the hydraulic circuit is performed using MATLAB/Simulink and results are compared with the experiments and it is found that hydraulic circuit using PFCV is 8.5% more energy efficient than the conventional circuit using PDCV. The position control of the actuator is done using PID controller tuned by the fuzzy controller.

A novel approach for the energy recovery and position control of a hybrid hydraulic excavator

The heavy earth moving machineries (HEMM) like hydraulic excavator play a major role in construction and mining industries. In this context, the energy saving strategies in hydraulic excavator needs to be addressed considering its vital importance. Since the hydraulic excavators are subjected to heavy loads, hence the opportunity to harness the potential gravitational energy (GPE) remains a key area which can be effectively explored in order to minimize the energy consumption in consideration with hydraulic excavator. In the projected system, the potential energy is stored as pressure energy in hydro-pneumatic accumulator. The upward movement of the boom is executed with the help of prime mover during the starting of the first duty cycle. In the latter duty cycles, the stored pressurized energy is utilized together with the prime mover energy capable to execute the upward movement of the boom. The position of the boom cylinder is controlled by using the conventional PID controller using proportional flow control valve (PFCV) and accumulator. The error between the actual position and demand position of the linear actuator is minimized along with attainment of superior controlled performance while utilizing Model Predictive Controller (MPC). The pressurized accumulator with PFCV has been utilized to cater the different position demands. This has been also justified both experimentally and analytically with the error in the permissible range of 2%. It has been observed that the proposed system is 10% more efficient in contrast to the conventional system.

Stochastic Sequestration Promotes Specificity in Decision Making in Single Cells

Cellular functions are mediated by specific molecular interactions; however, often competing nonspecific interactions can occur instead, for example, in noncoding regions of genes during transcription or in the response of cell receptors to external signals. Various functional roles have been proposed for such interactions. Motivated by these considerations, we study the time-dependent behavior of a class of discrete, stochastic models in which decoy molecules mediate nonspecific reactions that sequester activated molecules. It is shown that such nonspecific interactions can lead to a time delay in the completion of the specific reaction by the activated molecule, thus permitting discrimination between signals of different duration. We study the effect of stochastic fluctuations in a simple model of gene transcription by numerical solution of the Master Equation and find that the distribution of first passage times for the specific reaction shows surprising nonexponential (non-Debye) behavior over a range of time scales. The mathematical mechanism underlying this behavior is explained in terms of the behavior of the eigensystem of the linear operator associated with the time evolution. Our results demonstrate that stochastic sequestration can be used to enhance the specificity achieved by the well-known kinetic proofreading mechanism.

Mathematical modelling identifies the role of adaptive immunity as a key controller of respiratory syncytial virus in cotton rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modelling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well-differentiated human bronchial epithelial cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titre kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titre kinetic features including a faster timescale of viral titre clearance than viral production, and a monotonic decrease in the peak RSV titre with decreasing inoculum dose. Parameter estimation in the ODE model using a nonlinear mixed effects approach revealed a very low rate (average single-cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titre kinetics at early times post-infection (less than 5 dpi) but a slower decay in RSV titre in immunosuppressed cotton rats compared to that in non-suppressed cotton rats at later times (greater than 5 dpi) in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.

In Silico Modeling of Biofilm Formation by Nontypeable Haemophilus influenzae In Vivo

Biofilms formed by nontypeable Haemophilus influenzae (NTHI) bacteria play an important role in multiple respiratory tract diseases. Visual inspection of the morphology of biofilms formed during chronic infections shows distinct differences from biofilms formed in vitro To better understand these differences, we analyzed images of NTHI biofilms formed in the middle ears of Chinchilla lanigera and developed an in silico agent-based model of the formation of NTHI biofilms in vivo We found that, as in vitro, NTHI bacteria are organized in self-similar patterns; however, the sizes of NTHI clusters in vivo are more than 10-fold smaller than their in vitro counterparts. The agent-based model reproduced these patterns and suggested that smaller clusters occur due to elimination of planktonic NTHI cells by the host responses. Estimation of model parameters by fitting simulation results to imaging data showed that the effects of several processes in the model change during the course of the infection.IMPORTANCE Multiple respiratory illnesses are associated with formation of biofilms within the human airway by NTHI. However, a substantial amount of our understanding of the mechanisms that underlie NTHI biofilm formation is obtained from in vitro studies. Our in silico model that describes biofilm formation by NTHI within the middle ears of Chinchilla lanigera will help isolate processes potentially responsible for the differences between the morphologies of biofilms formed in vivo versus those formed in vitro Thus, the in silico model can be used to glean mechanisms that underlie biofilm formation in vivo and connect those mechanisms to those obtained from in vitro experiments. The in silico model developed here can be extended to investigate potential roles of specific host responses (e.g., mucociliary clearance) on NTHI biofilm formation in vivo The developed computational tools can also be used to analyze and describe biofilm formation by other bacterial species in vivo.

Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute?

The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.

Spatial Clustering of Receptors and Signaling Molecules Regulates NK Cell Response to Peptide Repertoire Changes

Natural Killer (NK) cell activation requires integration of inhibitory and activating signaling. Inhibitory signals are determined by members of the killer cell immunoglobulin-like receptor (KIR) family, which have major histocompatibility complex (MHC) class I ligands. Loss of this inhibitory signal leads to NK cell activation. Thus, down-regulation of MHC I during viral infection or cancer induces NK cell activation. However, NK cell activation in the presence of MHC-I has been demonstrated for HLA-C^*0102 through changes in its peptide content: "peptide antagonism." Here we identify an antagonist peptide for HLA-C^*0304 suggesting that peptide antagonism is a generalizable phenomenon and, using a combination of mathematical modeling, confocal imaging, and immune-assays, we quantitatively determine mechanisms that underlie peptide antagonism in inhibitory KIR2DL2/3 signaling. These data provide a mechanism for NK cell activation based on a reduction of inhibitory signaling in the presence of preserved levels of MHC class I.

Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media

Otitis media (OM) is a common polymicrobial infection of the middle ear in children under the age of 15 years. A widely used experimental strategy to analyse roles of specific phenotypes of bacterial pathogens of OM is to study changes in co-infection kinetics of bacterial populations in animal models when a wild-type bacterial strain is replaced by a specific isogenic mutant strain in the co-inoculating mixtures. As relationships between the OM bacterial pathogens within the host are regulated by many interlinked processes, connecting the changes in the co-infection kinetics to a bacterial phenotype can be challenging. We investigated middle ear co-infections in adult chinchillas (Chinchilla lanigera) by two major OM pathogens: non-typeable Haemophilus influenzae (NTHi) and Moraxella catarrhalis (Mcat), as well as isogenic mutant strains in each bacterial species. We analysed the infection kinetic data using Lotka-Volterra population dynamics, maximum entropy inference and Akaike information criteria-(AIC)-based model selection. We found that changes in relationships between the bacterial pathogens that were not anticipated in the design of the co-infection experiments involving mutant strains are common and were strong regulators of the co-infecting bacterial populations. The framework developed here allows for a systematic analysis of host-host variations of bacterial populations and small sizes of animal cohorts in co-infection experiments to quantify the role of specific mutant strains in changing the infection kinetics. Our combined approach can be used to analyse the functional footprint of mutant strains in regulating co-infection kinetics in models of experimental OM and other polymicrobial diseases.

Studies on the interaction of a synthetic nitro-flavone derivative with DNA: A multi-spectroscopic and molecular docking approach

Interaction of a ligand with DNA is often the basis of drug action of many molecules. Flavones are important in this regard as their structural features confer them the ability to bind to DNA. 2-(4-Nitrophenyl)-4H-chromen-4-one (4NCO) is an important biologically active synthetic flavone derivative. We are therefore interested in studying its interaction with DNA. Absorption spectroscopy studies included standard and reverse titration, effect of ionic strength on titration, determination of stoichiometry of binding and thermal denaturation. Spectrofluorimetry techniques included fluorimetric titration, quenching studies and fluorescence displacement assay. Assessment of relative viscosity and estimation of thermodynamic parameters from CD spectral studies were also undertaken. Furthermore, molecular docking analyses were also done with different short DNA sequences. The fluorescent flavone 4NCO reversibly interacted with DNA through partial intercalation as well as minor-groove binding. The binding constant and the number of binding sites were of the order 10⁴ M^-1 and 1 respectively. The binding stoichiometry with DNA was found to be 1:1. The nature of the interaction of 4NCO with DNA was hydrophobic in nature and the process of binding was spontaneous, endothermic and entropy-driven. The flavone also showed a preference for binding to GC rich sequences. The study presents a profile for structural and thermodynamic parameters, for the binding of 4NCO with DNA. DNA is an important target for ligands that are effective against cell proliferative disorders. In this regard, the molecule 4NCO is important since it can exert its biological activity through its DNA binding ability and can be a potential drug candidate.

Defining pharyngeal contractile integral during high-resolution manometry in neonates: a neuromotor marker of pharyngeal vigor

BACKGROUND

Pharyngeal contractility is critical for safe bolus propulsion. Pharyngeal contractile vigor can be measured by Pharyngeal Contractile Integral (PhCI): product of mean pharyngeal contractile amplitude, length, and duration. We characterized PhCI in neonates and examined the hypothesis that PhCI differs with mode of stimulation.

METHODS

Nineteen neonates born at 38.6 (34-41) weeks gestation were evaluated at 42.9 (40.4-44.0) weeks postmenstrual age using high-resolution manometry (HRM). PhCI was calculated using: (a) Conventional and (b) Automated Swallow Detection algorithm (ASDA) methods. Contractility metrics of all pharyngeal regions were examined using mixed statistical models during spontaneous and adaptive state (pharyngeal and oral stimulus) swallowing.

RESULTS

PhCI of oral stimuli swallows were distinct from pharyngeal stimuli and spontaneous swallows (P < 0.05). Correlation between conventional and ASDA methods was high (P < 0.001). PhCI increased with swallows for pharyngeal stimulation (P < 0.05) but remained stable for swallows with oral stimulation. PhCI differed between proximal and distal pharynx (P < 0.001).

CONCLUSIONS

PhCI is a novel reliable metric capable of distinguishing (1) proximal and distal pharyngeal activity, (2) effects of oral and pharyngeal stimulation, and (3) effects of prolonged stimulation. Changes in pharyngeal contractility with maturation, disease, and therapies can be examined with PhCI.

Respiratory Morbidity among Indian Tea Industry Workers

Background:

Indian tea industry workers are exposed to various exposures at their workplace.

Objective:

To investigate the respiratory health of Indian tea industry workers.

Methods:

We administered a respiratory questionnaire to and measured lung function in workers of 34 tea gardens and 46 tea factories. We used correlation matrices to test the association between their respiratory symptoms and lung functions.

Results:

The garden workers complained of shortness of breath 3 times higher than the factory workers. However, nasal allergy was more predominant among the factory workers compared to garden workers (69.6% vs 41.2%, p=0.02). The factory workers had higher total (median 107.3% vs 92.9%, p=0.05, as measured by R at 5 Hz) and peripheral airway resistance (143.8% vs 61.1%, p=0.005, as measured by R at 5–20 Hz) than the garden workers. Respiratory symptoms were inversely associated with airway obstruction as measured by the ratio between forced expiratory volume in 1 second (FEV₁) and forced vital capacity (FVC) and positively correlated with increased overall airway reactance among the workers.

Conclusion:

Respiratory symptoms and increased allergen susceptibility of Indian tea industry workers due to occupational exposures warrant routine systematic surveillance of their workplace air quality and health monitoring.

Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data

Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

In silico modeling identifies CD45 as a regulator of IL-2 synergy in the NKG2D-mediated activation of immature human NK cells

Natural killer (NK) cells perform immunosurveillance of virally infected and transformed cells, and their activation depends on the balance between signaling by inhibitory and activating receptors. Cytokine receptor signaling can synergize with activating receptor signaling to induce NK cell activation. We investigated the interplay between the signaling pathways stimulated by the cytokine interleukin-2 (IL-2) and the activating receptor NKG2D in immature (CD56^bright) and mature (CD56^dim) subsets of human primary NK cells using mass cytometry experiments and in silico modeling. Our analysis revealed that IL-2 changed the abundances of several key proteins, including NKG2D and the phosphatase CD45. Furthermore, we found differences in correlations between protein abundances, which were associated with the maturation state of the NK cells. The mass cytometry measurements also revealed that the signaling kinetics of key protein abundances induced by NKG2D stimulation depended on the maturation state and the pretreatment condition of the NK cells. Our in silico model, which described the multidimensional data with coupled first-order reactions, predicted that the increase in CD45 abundance was a major enhancer of NKG2D-mediated activation in IL-2-treated CD56^bright NK cells but not in IL-2-treated CD56^dim NK cells. This dependence on CD45 was verified by measurement of CD107a mobilization to the NK cell surface (a marker of activation). Our mathematical framework can be used to glean mechanisms underlying synergistic signaling pathways in other activated immune cells.

Innate and humoral recognition of the products of cell death: differential antigenicity and immunogenicity in lupus

While apoptotic debris is believed to constitute the original antigenic insult in lupus (which is characterized by a time-dependent diversification of autoreactivity), whether such debris and autoantibodies specifically recognizing its constituents mediate differential effects on innate and humoral responses in lupus-prone mice is currently unknown. Apoptotic blebs (as opposed to cellular lysate) enhanced preferentially the maturation of dendritic cells (DCs) from bone marrow precursors drawn from lupus-prone mice. Murine, somatically mutated, apoptotic cell-reactive immunoglobulin (Ig)G monoclonal antibodies demonstrated enhanced recognition of DCs and also displayed a prominent lupus strain-specific bias in mediating DC maturation. Further, immunization of such antibodies specifically in lupus-prone mice resulted in widespread humoral autoreactivity; hypergammaglobulinaemia (a hallmark of systemic autoimmunity) was observed, accompanied by enhanced antibody titres to cellular moieties. Induced antibodies recognized antigens distinct from those recognized by the antibodies employed for immunization; in particular, nephritis-associated anti-double stranded (ds) DNA antibodies and neonatal lupus-associated anti-Ro60 antibodies were elicited by a non-dsDNA, non-Ro60 reactive antibody, and Sm was a favoured target. Further, only in lupus-prone mice did such immunization enhance the kinetics of humoral anti-self responses, resulting in the advanced onset of glomerulosclerosis. These studies reveal that preferential innate and humoral recognition of the products of cell death in a lupus milieu influence the indices associated with autoimmune pathology.

Quality of tuberculosis care in India: a systematic review

BACKGROUND

While Indian studies have assessed care providers' knowledge and practices, there is no systematic review on the quality of tuberculosis (TB) care.

METHODS

We searched multiple sources to identify studies (2000-2014) on providers' knowledge and practices. We used the International Standards for TB Care to benchmark quality of care.

RESULTS

Of the 47 studies included, 35 were questionnaire surveys and 12 used chart abstraction. None assessed actual practice using standardised patients. Heterogeneity in the findings precluded meta-analysis. Of 22 studies evaluating provider knowledge about using sputum smears for diagnosis, 10 found that less than half of providers had correct knowledge; 3 of 4 studies assessing self-reported practices by providers found that less than a quarter reported ordering smears for patients with chest symptoms. In 11 of 14 studies that assessed treatment, less than one third of providers knew the standard regimen for drug-susceptible TB. Adherence to standards in practice was generally lower than correct knowledge of those standards. Eleven studies with both public and private providers found higher levels of appropriate knowledge/practice in the public sector.

CONCLUSIONS

Available evidence suggests suboptimal quality of TB care, particularly in the private sector. Improvement of quality of care should be a priority for India.

Limiting Energy Dissipation Induces Glassy Kinetics in Single-Cell High-Precision Responses

Single cells often generate precise responses by involving dissipative out-of-thermodynamic-equilibrium processes in signaling networks. The available free energy to fuel these processes could become limited depending on the metabolic state of an individual cell. How does limiting dissipation affect the kinetics of high-precision responses in single cells? I address this question in the context of a kinetic proofreading scheme used in a simple model of early-time T cell signaling. Using exact analytical calculations and numerical simulations, I show that limiting dissipation qualitatively changes the kinetics in single cells marked by emergence of slow kinetics, large cell-to-cell variations of copy numbers, temporally correlated stochastic events (dynamic facilitation), and ergodicity breaking. Thus, constraints in energy dissipation, in addition to negatively affecting ligand discrimination in T cells, can create a fundamental difficulty in determining single-cell kinetics from cell-population results.

Non-canonical antagonism of PI3K by the kinase Itpkb delays thymocyte β-selection and renders it Notch-dependent

β-selection is the most pivotal event determining αβ T cell fate. Here, surface-expression of a pre-T cell receptor (pre-TCR) induces thymocyte metabolic activation, proliferation, survival and differentiation. Besides the pre-TCR, β-selection also requires co-stimulatory signals from Notch receptors - key cell fate determinants in eukaryotes. Here, we show that this Notch-dependence is established through antagonistic signaling by the pre-TCR/Notch effector, phosphoinositide 3-kinase (PI3K), and by inositol-trisphosphate 3-kinase B (Itpkb). Canonically, PI3K is counteracted by the lipid-phosphatases Pten and Inpp5d/SHIP-1. In contrast, Itpkb dampens pre-TCR induced PI3K/Akt signaling by producing IP₄, a soluble antagonist of the Akt-activating PI3K-product PIP₃. Itpkb^-/- thymocytes are pre-TCR hyperresponsive, hyperactivate Akt, downstream mTOR and metabolism, undergo an accelerated β-selection and can develop to CD4⁺CD8⁺ cells without Notch. This is reversed by inhibition of Akt, mTOR or glucose metabolism. Thus, non-canonical PI3K-antagonism by Itpkb restricts pre-TCR induced metabolic activation to enforce coincidence-detection of pre-TCR expression and Notch-engagement.

DOI:http://dx.doi.org/10.7554/eLife.10786.001

T cells defend our body against cancer and infectious agents such as viruses. However, they can also cause rheumatoid arthritis and other autoimmune diseases by attacking healthy tissue. T cells recognize target cells via receptor proteins on their surface. To maximize the variety of infections and cancers our immune system can recognize, we generate millions of T cells with different T cell receptors every day.

To ensure T cells work correctly, T cell receptors are tested at various checkpoints. The first checkpoint involves a process called beta (β) selection, during which T cells produce their first T cell receptor – the so-called pre-T cell receptor. This receptor causes T cells to divide and mature, and sets their future identity or “fate”. To complete β-selection, T cells must also receive signals from another surface receptor – one that belongs to the Notch family, which determines cell fate in many different tissues.

The Notch receptor and the pre-T cell receptor both activate an enzyme called PI3K – a key mediator of β-selection. But the pre-T cell receptor also activates another enzyme called Itpkb that is required for T cell development. Westernberg, Conche et al. have now investigated how these different proteins and signaling processes work and interact during β-selection, using mice that lack several immune genes, including the gene that produces Itpkb.

The results of the experiments show that during β-selection, Itpkb limits the ability of PI3K to activate some of its key target proteins. This “dampened” PI3K signaling ensures that both the pre-T cell receptor and the Notch receptor must be activated to trigger T cell maturation. Without Itpkb, β-selection can occur in the absence of Notch signaling.

As Notch signaling is important for determining the fate of many different cell types, Westernberg, Conche et al.’s findings raise the possibility that Itpkb might also regulate cell fate determination in other tissues. Moreover, Itpkb may suppress tumor development, because excessive PI3K signaling drives many cancers.

DOI:http://dx.doi.org/10.7554/eLife.10786.002

Redox signalling to nuclear regulatory proteins by reactive oxygen species contributes to oestrogen-induced growth of breast cancer cells

Background:

17β-Oestradiol (E2)-induced reactive oxygen species (ROS) have been implicated in regulating the growth of breast cancer cells. However, the underlying mechanism of this is not clear. Here we show how ROS through a novel redox signalling pathway involving nuclear respiratory factor-1 (NRF-1) and p27 contribute to E2-induced growth of MCF-7 breast cancer cells.

Methods:

Chromatin immunoprecipitation, qPCR, mass spectrometry, redox western blot, colony formation, cell proliferation, ROS assay, and immunofluorescence microscopy were used to study the role of NRF-1.

Results:

The major novel finding of this study is the demonstration of oxidative modification of phosphatases PTEN and CDC25A by E2-generated ROS along with the subsequent activation of AKT and ERK pathways that culminated in the activation of NRF-1 leading to the upregulation of cell cycle genes. 17β-Oestradiol-induced ROS by influencing nuclear proteins p27 and Jab1 also contributed to the growth of MCF-7 cells.

Conclusions:

Taken together, our results present evidence in the support of E2-induced ROS-mediated AKT signalling leading to the activation of NRF-1-regulated cell cycle genes as well as the impairment of p27 activity, which is presumably necessary for the growth of MCF-7 cells. These observations are important because they provide a new paradigm by which oestrogen may contribute to the growth of breast cancer.

Expression of serum miR-200a, miR-200b, and miR-200c as candidate biomarkers in epithelial ovarian cancer and their association with clinicopathological features

BACKGROUND

MicroRNAs (miRs) have been implicated in the etiology of various human cancers. The aim of this study was to investigate the association of the expression of three members--miR 200a, miR 200b, and miR 200c belonging to the miR-200 family with clinicopathological characteristics and their impact on the progression of epithelial ovarian cancer (EOC).

MATERIALS AND METHODS

Total RNA from serum was isolated by Trizol method, polyadenylated, and reverse transcribed into cDNA. Expression levels of miR-200a, miR-200b, and miR-200c were detected by using miRNA qRT-PCR. We measured miR expression in 70 serum samples of EOC patients with matched controls using U6 snRNA as a reference. Levels of miR expression was compared with distinct clinicopathological features.

RESULTS

Expression of miR-200a was found to be greater than six-fold (p = 0.01), miR-200b and miR-200c greater than three-fold (p = 0.01) in comparison with matched normal controls. Association of miRNA expression with clinicopathological factors and progression was statistically evaluated. The expression levels of miR-200a and miR-200c were found to be significantly associated with disease progression (p = 0.04 and p < 0.001, respectively). miR-200a overexpression was found be associated with tumor histology and stage. Patients with lymph node metastasis showed significant elevation of miR-200c (p = 0.006). The AUC in ROC curve also indicated that serum levels of miR-200a and miR-200c might be worthwhile as a diagnostic tool in the near future.

CONCLUSION

Our findings suggest that miR-200a, miR-200b, and miR-200c overexpressions are associated with the aggressive tumor progression and be recognized as reliable markers to predict the prognosis and survival in EOC patients.

Metabolic inhibition by inositol-tetrakisphosphate delays thymocyte β-selection and renders it Notch-dependent (HEM2P.241)

In β-selection, surface-expression of a pre-T cell receptor (pre-TCR) induces thymocyte metabolic activation, proliferation, survival and differentiation. This pivotal αβ T cell fate-determining event requires co-stimulatory Notch signals. Here, we show that this Notch dependence is enforced through antagonistic signaling by inositol-trisphosphate 3-kinase B (Itpkb) and phosphoinositide 3-kinase (PI3K). Itpkb produces soluble inositol-tetrakisphosphate which competes with the PI3K lipid-product PIP3 for binding to the effector-kinase Akt. Itpkb-/- thymocytes are pre-TCR hyperresponsive, hyperactivate Akt, downstream mTOR and metabolism, undergo an accelerated β-selection and can develop to CD4+CD8+ cells without Notch. This is reversed by inhibition of Akt, mTOR or glucose-metabolism. Thus, Itpkb restricts pre-TCR induced metabolic activation to enforce coincidence-detection of pre-TCR expression and Notch-engagement, and to prevent premature thymocyte maturation.

Host-to-host variation of ecological interactions in polymicrobial infections

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.