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Plant functional traits, in particular specific leaf area (SLA), wood density and seed mass, are often good predictors of individual tree growth rates within communities. Individuals and species with high SLA, low wood density and small seeds tend to have faster growth rates. If community-level relationships between traits and growth have general predictive value, then similar relationships should also be observed in analyses that integrate across taxa, biogeographic regions and environments. Such global consistency would imply that traits could serve as valuable proxies for the complex suite of factors that determine growth rate, and, therefore, could underpin a new generation of robust dynamic vegetation models. Alternatively, growth rates may depend more strongly on the local environment or growth-trait relationships may vary along environmental gradients. We tested these alternative hypotheses using data on 27 352 juvenile trees, representing 278 species from 27 sites on all forested continents, and extensive functional trait data, 38% of which were obtained at the same sites at which growth was assessed. Data on potential evapotranspiration (PET), which summarizes the joint ecological effects of temperature and precipitation, were obtained from a global data base. We estimated size-standardized relative height growth rates (SGR) for all species, then related them to functional traits and PET using mixed-effect models for the fastest growing species and for all species together. Both the mean and 95th percentile SGR were more strongly associated with functional traits than with PET. PET was unrelated to SGR at the global scale. SGR increased with increasing SLA and decreased with increasing wood density and seed mass, but these traits explained only 3.1% of the variation in SGR. SGR-trait relationships were consistently weak across families and biogeographic zones, and over a range of tree statures. Thus, the most widely studied functional traits in plant ecology were poor predictors of tree growth over large scales. Synthesis. We conclude that these functional traits alone may be unsuitable for predicting growth of trees over broad scales. Determining the functional traits that predict vital rates under specific environmental conditions may generate more insight than a monolithic global relationship can offer. The most widely studied functional traits in plant ecology, specific leaf area, wood density and seed mass, were only weakly associated with tree growth rates over broad scales. Assessing trait-growth relationships under specific environmental conditions may generate more insight than a global relationship can offer.

Current policy is driving renewed impetus to restore forests to return ecological function, protect species, sequester carbon and secure livelihoods. Here we assess the contribution of tree planting to ecosystem restoration in tropical and sub-tropical Asia; we synthesize evidence on mortality and growth of planted trees at 176 sites and assess structural and biodiversity recovery of co-located actively restored and naturally regenerating forest plots. Mean mortality of planted trees was 18% 1 year after planting, increasing to 44% after 5 years. Mortality varied strongly by site and was typically ca 20% higher in open areas than degraded forest, with height at planting positively affecting survival. Size-standardized growth rates were negatively related to species-level wood density in degraded forest and plantations enrichment settings. Based on community-level data from 11 landscapes, active restoration resulted in faster accumulation of tree basal area and structural properties were closer to old-growth reference sites, relative to natural regeneration, but tree species richness did not differ. High variability in outcomes across sites indicates that planting for restoration is potentially rewarding but risky and context-dependent. Restoration projects must prepare for and manage commonly occurring challenges and align with efforts to protect and reconnect remaining forest areas. The abstract of this article is available in Bahasa Indonesia in the electronic supplementary material. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

There remains an urgent need for assays to quantify humoral protective immunity to SARS-CoV-2 to understand the immune responses of COVID-19 patients, evaluate efficacy of vaccine candidates in clinical trials, and conduct large-scale epidemiological studies. The plaque-reduction neutralization test (PRNT) is the reference-standard for quantifying antibodies capable of neutralizing SARS-CoV-2. However, the PRNT is logistically demanding, time-consuming, and requires containment level-3 facilities to safely work with live virus. In contrast, a surrogate virus neutralization test (sVNT) manufactured by Genscript is a quick and simple assay that detects antibodies that inhibit the RBD-ACE2 interaction, crucial for virus entry into host cells. In this study, we evaluate the sensitivity, specificity, and cross-reactivity of the sVNT compared with the PRNT using both 50% and 90% SARS-CoV-2 neutralization as a reference-standard. We found that the sVNT provides a high-throughput screening tool prior to confirmatory PRNT testing for the evaluation of SARS-CoV-2 neutralizing antibodies., Competing Interests: Declaration of Competing Interests The authors declare no conflict of interest., (Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved.)

The world's most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data., Competing Interests: The authors have declared that no competing interests exist.

Summary: To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE's modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system., Availability and Implementation: multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE ( E mpowering the D evelopment of G enomics E xpertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data ( Li et al. , 2017 ). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users., (Copyright © 2017 The Authors; exclusive licensee Bio-protocol LLC.)

Lanthionine synthetase cyclase-like receptor 2 (LANCL2) is a novel therapeutic target for Crohn's disease (CD). BT-11 is a small molecule that binds LANCL2, is orally active, and has demonstrated therapeutic efficacy in 3 validated mouse models of colitis at doses as low as 8 mg/kg/d. Exploratory experiments evaluated BT-11 in male Harlan Sprague Dawley rats with a single oral dose of 500 mg/kg and 80 mg/kg/d for 14 days (n = 10 rats dosed/group). Treated and control rats were observed for behavioral detriments, and blood and tissues were collected for clinical pathology and histopathological examination. A functional observational battery demonstrated no differences between treated and control groups over multiple times of observation for quantal, categorical, and continuous end points, including posture, in cage activity, approach, response to touch, weight, grip strength, body temperature, and time on a rotarod. Histopathological examination of the brain, kidney, liver, adrenal gland, testes, stomach, small and large intestines, duodenum, pancreas, heart, lungs, spleen, thymus, and rib found no significant differences between the groups. Plasma enzymes associated with liver function were transiently elevated 2 to 4 days after the 500 mg/kg single dose but returned to normal values by 8 days and were not observed at any time in rats given 80 mg/kg/d for 14 days. One hour after oral administration of a single dose of 80 mg/kg, BT-11 had a maximal concentration of 21 ng/mL; the half-life was 3 hours. These experimental results demonstrated that BT-11 is well tolerated in rats, and, with further testing, may hold promise as an orally active therapeutic for CD., (© The Author(s) 2016.)

This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through "use cases" centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.

Helicobacter pylori is the dominant member of the gastric microbiota in over half of the human population of which 5-15% develop gastritis or gastric malignancies. Immune responses to H. pylori are characterized by mixed T helper cell, cytotoxic T cell and NK cell responses. The presence of Tregs is essential for the control of gastritis and together with regulatory CX3CR1+ mononuclear phagocytes and immune-evasion strategies they enable life-long persistence of H. pylori. This H. pylori-induced regulatory environment might contribute to its cross-protective effect in inflammatory bowel disease and obesity. Here we review host-microbe interactions, the development of pro- and anti-inflammatory immune responses and how the latter contribute to H. pylori's role as beneficial member of the gut microbiota. Furthermore, we present the integration of existing and new data into a computational/mathematical model and its use for the investigation of immunological mechanisms underlying initiation, progression and outcomes of H. pylori infection.

Nucleotide-binding domain and leucine-rich repeat containing (NLR) family are intracellular sentinels of cytosolic homeostasis that orchestrate immune and inflammatory responses in infectious and immune-mediated diseases. NLRX1 is a mitochondrial-associated NOD-like receptor involved in the modulation of immune and metabolic responses. This study utilizes molecular docking approaches to investigate the structure of NLRX1 and experimentally assesses binding to naturally occurring compounds from several natural product and lipid databases. Screening of compound libraries predicts targeting of NLRX1 by conjugated trienes, polyketides, prenol lipids, sterol lipids, and coenzyme A-containing fatty acids for activating the NLRX1 pathway. The ligands of NLRX1 were identified by docking punicic acid (PUA), eleostearic acid (ESA), and docosahexaenoic acid (DHA) to the C-terminal fragment of the human NLRX1 (cNLRX1). Their binding and that of positive control RNA to cNLRX1 were experimentally determined by surface plasmon resonance (SPR) spectroscopy. In addition, the ligand binding sites of cNLRX1 were predicted in silico and validated experimentally. Target mutagenesis studies demonstrate that mutation of 4 critical residues ASP677, PHE680, PHE681, and GLU684 to alanine resulted in diminished affinity of PUA, ESA, and DHA to NLRX1. Consistent with the regulatory actions of NLRX1 on the NF-κB pathway, treatment of bone marrow derived macrophages (BMDM)s with PUA and DHA suppressed NF-κB activity in a NLRX1 dependent mechanism. In addition, a series of pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the regulatory function of PUA on colitis is NLRX1 dependent. Thus, we identified novel small molecules that bind to NLRX1 and exert anti-inflammatory actions.

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

Background: Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation., Implementation: Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed., Conclusion: We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

Pattern recognition receptors are essential mediators of host defense and inflammation in the gastrointestinal system. Recent data have revealed that toll-like receptors and nucleotide-binding domain and leucine-rich repeat-containing proteins (NLRs) function to maintain homeostasis between the host microbiome and mucosal immunity. The NLR proteins are a diverse class of cytoplasmic pattern recognition receptors. In humans, only about half of the identified NLRs have been adequately characterized. The majority of well-characterized NLRs participate in the formation of a multiprotein complex, termed the inflammasome, which is responsible for the maturation of interleukin-1β and interleukin-18. However, recent observations have also uncovered the presence of a novel subgroup of NLRs that function as positive or negative regulators of inflammation through modulating critical signaling pathways, including NF-κB. Dysregulation of specific NLRs from both proinflammatory and inhibitory subgroups have been associated with the development of inflammatory bowel disease (IBD) in genetically susceptible human populations. Our own preliminary retrospective data mining efforts have identified a diverse range of NLRs that are significantly altered at the messenger RNA level in colons from patients with IBD. Likewise, studies using genetically modified mouse strains have revealed that multiple NLR family members have the potential to dramatically modulate the immune response during IBD. Targeting NLR signaling represents a promising and novel therapeutic strategy. However, significant effort is necessary to translate the current understanding of NLR biology into effective therapies.