Empirical modeling of T cell activation predicts interplay of host cytokines and bacterial indole

Engineering live attenuated vaccines: Old dogs learning new tricks

Autoimmune diseases such as rheumatoid arthritis and type 1 diabetes are increasingly common global problems. Concerns about increases in the prevalence of such diseases and the limited efficacy of conventional treatment regimens necessitates new therapies to address these challenges. Autoimmune disease severity and dysbiosis are interconnected. Although probiotics have been established as a therapy to rebalance the microbiome and suppress autoimmune symptoms, these microbes tend to lack a number of advantageous qualities found in non-commensal bacteria. Through attenuation and genetic manipulation, these non-commensal bacteria have been engineered into recombinant forms that offer malleable platforms capable of addressing the immune imbalances found in RA and T1D. Such bacteria have been engineered to express valuable gene products known to suppress autoimmunity such as anti-inflammatory cytokines, autoantigens, and enzymes synthesizing microbial metabolites. This review will highlight current and emerging trends in the field and discuss how they may be used to prevent and control autoimmune diseases.

Alteration of the gut microbiota after surgery in preterm infants with necrotizing enterocolitis

PURPOSE

To investigate the dynamic changes in the intestinal microbiota in preterm infants with necrotizing enterocolitis (NEC) before and after treatment via a prospective case-control study.

METHODS

Preterm infants with NEC and preterm infants with similar age and weight (control group) were enrolled in this study. They were divided into NEC_Onset (diagnosis time), NEC_Refeed (refeed time), NEC_FullEn (full enteral nutrition time), Control_Onset, and Control_FullEn groups according to the time of the fecal material collected. Except for basic clinical information, fecal specimens of the infants were obtained as well at indicated times for 16S rRNA gene sequencing. All infants were followed up after discharge from the NICU, and the growth data of the corrected age of 12 months were acquired from the electronic outpatient system and telephonic interviews.

RESULTS

A total of 13 infants with NEC and 15 control infants were enrolled. A gut microbiota analysis showed that the Shannon and Simpson indices were lower in the NEC_FullEn group than in the Control_FullEn group (p < .05). Methylobacterium, Clostridium_butyricum, and Acidobacteria were more abundant in infants with NEC during diagnosis. Methylobacterium and Acidobacteria were remained plentiful in the NEC group until the end of treatment. These bacteria species were significantly positively correlated with CRP and negatively correlated with platelet count. The rate of delayed growth was higher in the NEC group than in the control group (25% vs. 7.1%) at 12 months of corrected age, but there was no significant difference. In addition, the pathways of synthesis and degradation of ketone bodies were more active in the NEC subgroups, including both the NEC_Onset group and the NEC_FullEn group. The pathway of sphingolipid metabolism was more active in the Control_FullEn group.

CONCLUSION

Even after reaching the full enteral nutrition period, alpha diversity in infants with NEC who underwent surgery was lower than that in the control group infants. It may take more time to reestablish the normal gut flora of NEC infants after surgery. The pathways of the synthesis and degradation of ketone bodies and sphingolipid metabolism might be related to the pathogenesis of NEC and physical development after the occurrence of NEC.

A metabolically engineered bacterium controls autoimmunity and inflammation by remodeling the pro-inflammatory microenvironment

Immunotherapy has led to impressive advances in the treatment of autoimmune and pro-inflammatory disorders; yet, its clinical outcomes remain limited by a variety of factors including the pro-inflammatory microenvironment (IME). Discovering effective immunomodulatory agents, and the mechanisms by which they control disease, will lead to innovative strategies for enhancing the effectiveness of current immunotherapeutic approaches. We have metabolically engineered an attenuated bacterial strain (i.e., Brucella melitensis 16M ∆vjbR, Bm∆vjbR::tnaA) to produce indole, a tryptophan metabolite that controls the fate and function of regulatory T (Treg) cells. We demonstrated that treatment with Bm∆vjbR::tnaA polarized macrophages (Mφ) which produced anti-inflammatory cytokines (e.g., IL-10) and promoted Treg function; moreover, when combined with adoptive cell transfer (ACT) of Treg cells, a single treatment with our engineered bacterial strain dramatically reduced the incidence and score of autoimmune arthritis and decreased joint damage. These findings show how a metabolically engineered bacterium can constitute a powerful vehicle for improving the efficacy of immunotherapy, defeating autoimmunity, and reducing inflammation by remodeling the IME and augmenting Treg cell function.

Diverse roles of microbial indole compounds in eukaryotic systems

Indole and its derivatives are widespread across different life forms, functioning as signalling molecules in prokaryotes and with more diverse roles in eukaryotes. A majority of indoles found in the environment are attributed to bacterial enzymes converting tryptophan into indole and its derivatives. The involvement of indoles among lower organisms as an interspecies and intraspecies signal is well known, with many reports showing that inter‐kingdom interactions involving microbial indole compounds are equally important as they influence defence systems and even the behaviour of higher organisms. This review summarizes recent advances in our understanding of the functional properties of indole and indole derivatives in diverse eukaryotes. Furthermore, we discuss current perspectives on the role of microbial indoles in human diseases such as diabetes, obesity, atherosclerosis, and cancers. Deciphering the function of indoles as biomarkers of metabolic state will facilitate the formulation of diet‐based treatments and open unique therapeutic opportunities.

A sensitive electrochemical method for indole based on the signal amplification strategy by gold/iron-oxide composite nanoparticles

Indole is a major metabolite of tryptophan, which plays an important role in the intestinal microecological balance and human physiological activities. The determination of indole becomes important for its researches. So, it is urgent to establish a sensitive and cost-effective method for indole detection. Herein, a sensitive electrochemical method was constructed to determine the concentration of indole using screen-printed carbon electrode (SPCE) with the signal amplification strategy by gold/iron-oxide composite nanoparticles (Au/Fe₃O₄). Au/Fe₃O₄ nanoparticles were successfully synthesized under the irradiation by high-energy electron beams. 4-aminothiophenol (4-ATP) was connected to Au/Fe₃O₄ via Au-S bond. And then NaNO₂ reacted with 4-ATP to form the azo bond, which could form the final product of Au/Fe₃O₄@ATP-azo-indole by the coupling reaction. Thus, the concentration of indole was detected by the electrochemical signal produced by Au/Fe₃O₄@ATP-azo-indole indirectly. The detection sensitivity was greatly improved by the large specific surface area provided by Au/Fe₃O₄ after the modification. The linear range of indole was from 0.50 to 120.00 μg L^-1 and the limit of detection (LOD) was as low as 0.10 μg L^-1 (S/N = 3). Furthermore, the developed method exhibited acceptable intra-day and inter-day precisions with the coefficient of variations (CV) less than 4.9% and 8.2%, respectively. And the recoveries were from 97.2% to 105.4%. An innovative, sensitive, cost-effective method was established for indole determination in human plasma matrix in this manuscript, which provides a promising way for indole detection in conventional laboratories.

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

An intracellular signaling pathway is often represented by a set of nonlinear ordinary differential equations, which translate our current knowledge about the signaling pathway into a testable mathematical model. However, predictions from such models are often subject to high uncertainty since many signaling pathways are only partially known beforehand. In this study, we propose a systematic approach to develop a hybrid model to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN.

Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture

The isolation of T cells, followed by differentiation into Regulatory T cells (Tregs), and re‐transplantation into the body has been proposed as a therapeutic option for inflammatory bowel disease. A key requirement for making this a viable therapeutic option is the generation of a large population of Tregs. However, cytokines in the local microenvironment can impact the yield of Tregs during differentiation. As such, experimental design is an essential part of evaluating the importance of different cytokine concentrations for Treg differentiation. However, currently only single, constant concentrations of the cytokines have been investigated. This work addresses this point by performing experimental design in silico which seeks to maximize the predicted induction of Tregs relative to Th17 cells, by selecting an optimal input function for the concentrations of TGF‐β, IL‐2, IL‐6, and IL‐23. While this approach sounds promising, the results show that only marginal improvements in the concentration of Tregs can be achieved for dynamic cytokine profiles as compared to optimal constant concentrations. Since constant concentrations are easier to implement in experiments, it is recommended for this particular system to keep the concentrations constant where IL‐6 should be kept low and high concentrations of TGF‐β, IL‐2, and IL‐23 should be used.

Role of nutrition, infection, and the microbiota in the efficacy of oral vaccines

Oral vaccines (OVs), provide protection against pathogens that infect mucosal surfaces and their potency relies on their capacity to elicit T- and B-cell responses directed to these surfaces. Oral vaccination efficacy has been found to vary considerably with differences in geographical locations and socioeconomic status. Specifically, in children living in resource-poor countries, undernourishment and chronic gastrointestinal (GI) infection are associated with the failure of OVs, which is a tragic outcome for the children who would benefit most from mucosal-based protection from infection. Both undernutrition and GI infection have been shown to profoundly affect the microbiota, inducing 'dysbiosis' characterized by narrowed bacterial diversity and increased frequency of bacterial clades associated with the induction of inflammation. Recent studies have demonstrated that the microbiota exerts a profound effect on the development of mucosal immune responses. Therefore, it seems likely that OV failure in resource-poor regions is affected by alterations to the immune response driven by dysbiotic changes to the microbiota. Here, we review the contribution of the microbiota to OV efficacy in the context of diet and GI infection.