Age-associated B cells predict impaired humoral immunity after COVID-19 vaccination in patients receiving immune checkpoint blockade

Age-associated B cells (ABC) accumulate with age and in individuals with different immunological disorders, including cancer patients treated with immune checkpoint blockade and those with inborn errors of immunity. Here, we investigate whether ABCs from different conditions are similar and how they impact the longitudinal level of the COVID-19 vaccine response. Single-cell RNA sequencing indicates that ABCs with distinct aetiologies have common transcriptional profiles and can be categorised according to their expression of immune genes, such as the autoimmune regulator (AIRE). Furthermore, higher baseline ABC frequency correlates with decreased levels of antigen-specific memory B cells and reduced neutralising capacity against SARS-CoV-2. ABCs express high levels of the inhibitory FcγRIIB receptor and are distinctive in their ability to bind immune complexes, which could contribute to diminish vaccine responses either directly, or indirectly via enhanced clearance of immune complexed-antigen. Expansion of ABCs may, therefore, serve as a biomarker identifying individuals at risk of suboptimal responses to vaccination.

Parents' Perception of Fissure Sealant Therapy in 6-12 Year Old Children: Evaluating a theory-driven intervention

BACKGROUND

Dental caries is considered a major global health issue and among the most challenging diseases worldwide. An effective way of preventing dental caries is the fissure sealant (FS) therapy.

OBJECTIVE

To determine the effectiveness of an educational intervention developed based on the health belief model (HBM) for parents' perception of FS therapy for their children.

METHODS

Quasi-experiment among 300 parents of 6-12 year-old children, 150 in the intervention group (IG) and 150 in the control (CG), in the south of Iran recruited via both clustering and convenience sampling. Data were collected using a validated questionnaire collecting demographic information, knowledge and data on HBM constructs and FS behaviour. Eight intervention sessions, 40-60 minutes long, were held for over month. The primary outcome was child's receipt of fissure sealants 3 months after the intervention.

RESULTS

The two groups had similar knowledge and the HBM constructs at baseline. After the intervention, the receipt of FS therapy was 65% and 12% in the IG and CG, respectively (p ⟨ 0.001, Chi Sq.). ANCOVA supported post-test differences between the intervention and control groups when accounting for baseline scores (p⟨0.05).

CONCLUSIONS

The educational HBM-based intervention improved parents' perceptions and their children's receipt of FS therapy. The intervention affected the HBM constructs. Barriers to healthy oral/dental behaviours may be reduced by interventions at multiple layers (beyond the individual level).

Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

MOTIVATION

Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parameterize and analyze large-scale kinetic models intuitively and efficiently.

RESULTS

We present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can implement multispecies bioreactor simulations to assess biotechnological processes.

AVAILABILITY AND IMPLEMENTATION

The software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Steroid use for recovery of advanced atrioventricular block immediately after valvular surgery (survival): a randomized clinical trial

Funding Acknowledgements

Type of funding sources: None.

Background

Limited inconsistent evidence support use of steroids in recovery of atrioventricular block (AVB) after trans-catheter aortic valve implantation. However, there is no study to examine the efficacy of steroids in recovery of high-grade AVB following valvular surgery.

Purpose

 To assess the effect of high-dose dexamethasone on recovery of advanced AVB following valvular heart surgery.

Methods

In this randomized controlled trial, patients with advanced postoperative AVB (either Mobitz type II or third degree) were randomly assigned to receive either dexamethasone (0.4 mg /kg, maximum 30 mg /day in three divided doses) intravenously for three days or conservative care only. Primary endpoint was recovery rate in day five since randomization. Secondary endpoints were recovery rate in day 7 and day 10, cumulative AVB time, PPM implantation rate, length of stay in critical care units, and post-operative major adverse events (MAE) during one month. We defined MAE as composite of all-cause mortality, all-type infections, major bleeding, prolonged sternal dehiscence, new stroke, postoperative MI, complicated uncontrolled hyperglycemia and readmission for decompensated heart failure.

Results

We enrolled 139 subjects (48.9% male) with mean age of 59.9 years who were randomly allocated to intervention group (n= 69) and control group (n= 70). Dexamethasone led to higher recovery rates at day 5 (82.6% vs. 62.9%, P= 0.009) and day 7 (88.4% vs. 61.4%, P< 0.0001) respectively. Random 24-hours ECG Holter monitoring of patients at day 5 revealed the same results and confirmed the superiority of dexamethasone for recovery of AVB (80.5% vs 61.2%, respectively, P = 0.024). Although this benefit ceased at day 10 (83.05 vs 78.6 %, P = 0.547), Generalized Estimating Equation analysis for recovery over 10 days favored intervention (odds ratio: 2.56, 95% CI: 1.27- 5.15, P = 0.008). Median cumulative AVB time was shorter in dexamethasone group compared to control group (41 hours vs 64 hours, P = 0.044). PPM implantation rates were similar between the dexamethasone and control groups (15.9% vs 17.1 %, respectively, P = 0.849). Median length of stay in ICU (10 days vs 12 days, P= 0.03) and MAE (17.4 % vs 25.7%, P = 0.133) tended to be lower with dexamethasone.

Conclusion

Our findings suggest that dexamethasone may serve as a safe and effective medication to improve recovery of advanced AVB after valvular surgery. Further studies are needed to confirm these findings, particularly regarding subsequent PPM implantation rate.

Evaluation of the relationship between dental caries and dermatoglyphics in 3 to 6-Year-Old Iranian children

Background

As enamel and finger buds have an ectodermal origin and grow at the same time, it might be supposed that factors and agents that affect dental caries might give rise to the diversity of dermatoglyphic dermal ridge patterns.

Aim

The present study aimed to evaluate the relationship between dental caries and dermatoglyphics in 3 to 6-year-old Iranian children.

Methods

The present case-control study was performed on 3 to 6-year-old children. The decayed, missing, and filled teeth (dmft) scale was used to evaluate the oral and dental health status in the participants. Then, the patients were divided into two groups: 1) The case group: patients with a dmft of ≥3 (101 cases); 2) the control group: those with dmft value of zero (86 cases). Afterward, using a fingerprint recording device, the fingerprints were obtained from both hands of the participants.

Results

There was no significant relationship between the patterns of fingerprints and dental caries. The percentages of whorls, loops, and arches in the case group were higher than those in the control group; however, these differences were not statistically significant.

Conclusion

Dermatoglyphic patterns could not be used as a variable for the detection or prediction of dental caries in Iranian children.

ASSOCIATION OF IRS1 GLY971ARG GENE POLYMORPHISM WITH INSULIN RESISTANCE IN IRANIAN NEWLY DIAGNOSED DIABETIC ADULTS

Context

Insulin receptor substrate-1 (IRS-1) has an important role in insulin signaling and the common Gly971Arg polymorphism is related to type 2 diabetes (T2D). IRS-1 Gly971Arg polymorphism can modify tyrosine phosphorylation at a specific site of IRS-1 and may have a critical role in the development of insulin resistance (IR).

Objective

The purpose of this study was to investigate the association between this polymorphism and IR in Iranian patients with newly-diagnosed type 2 diabetes.

Design

The study was conducted on 114 individuals with newly-diagnosed T2D and 118 healthy matched controls, aged 20-80 years. Fasting blood glucose and insulin were measured by the enzymatic method and enzyme-linked immunosorbent assay, respectively. Insulin-resistance was calculated by homeostasis model assessment estimated-insulin resistance (HOMA-IR). The gene polymorphism was examined by polymerase chain reaction-restriction fragment length polymorphism.

Results

There are significant differences between IRS1 Gly971Arg polymorphism and studied individuals (P<0.0001). The findings showed that the risk of developing T2D in individuals who had R-alleles was 3.74 folds higher than those without R-alleles. However, IRS1 Gly971Arg polymorphism was not associated with high HOMA-IR, high BMI and familial history of diabetes.

Conclusions

Even though there was not a significant relationship between IRS-1 G971R polymorphism with insulin resistance and high BMI, this polymorphism was correlated to newly-diagnosed diabetic patients. Thus, the evaluation of IRS-1 G971R polymorphism may be helpful for predicting T2D new cases.

Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications

Background

A genome-scale metabolic network model (GEM) is a mathematical representation of an organism’s metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains.

Objectives

In the present study, we have evaluated the predictive power of two GEMs, namely iBsu1103 (for Bacillus subtilis 168) and iMZ1055 (for Bacillus megaterium WSH002).

Materials and Methods

For comparing the predictive power of Bacillus subtilis and Bacillus megaterium GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used.

Results

By using the wealth of data in the literature, we evaluated the accuracy of in silico simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where B. subtilis and B. megaterium do not have similar phenotypes.

Conclusions

Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two Bacillus species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.

Impact of iron reduction on the metabolism of Clostridium acetobutylicum

Iron is essential for most living organisms. In addition, its biogeochemical cycling influences important processes in the geosphere (e.g., the mobilization or immobilization of trace elements and contaminants). The reduction of Fe(III) to Fe(II) can be catalysed microbially, particularly by metal‐respiring bacteria utilizing Fe(III) as a terminal electron acceptor. Furthermore, Gram‐positive fermentative iron reducers are known to reduce Fe(III) by using it as a sink for excess reducing equivalents, as a form of enhanced fermentation. Here, we use the Gram‐positive fermentative bacterium Clostridium acetobutylicum as a model system due to its ability to reduce heavy metals. We investigated the reduction of soluble and solid iron during fermentation. We found that exogenous (resazurin, resorufin, anthraquinone‐2,6‐disulfonate) as well as endogenous (riboflavin) electron mediators enhance solid iron reduction. In addition, iron reduction buffers the pH, and elicits a shift in the carbon and electron flow to less reduced products relative to fermentation. This study underscores the role fermentative bacteria can play in iron cycling and provides insights into the metabolic profile of coupled fermentation and iron reduction with laboratory experiments and metabolic network modelling.

Technology for An Instant Almond Gruel

This paper describes methods for processing an instant baby food made from rice and almonds. Commercial production of ready-to-serve baby foods in Iran is limited, and imported baby foods are expensive. Since home preparation is time-consuming and often neglected, a ready-to-serve local product is desirable. The resultant product was acceptable and caused no digestive disturbance when fed to children for three months.

Morphology-Specific Discrimination between MS White Matter Lesions and Benign White Matter Hyperintensities Using Ultra-High-Field MRI

BACKGROUND AND PURPOSE

Recently published North American Imaging in Multiple Sclerosis guidelines call for derivation of a specific radiologic definition of MS WM lesions and mimics. The purpose of this study was to use SWI and magnetization-prepared FLAIR images for sensitive differentiation of MS from benign WM lesions using the morphologic characteristics of WM lesions.

MATERIALS AND METHODS

Seventeen patients with relapsing-remitting MS and 18 healthy control subjects were enrolled retrospectively. For each subject, FLAIR and multiecho gradient-echo images were acquired using 7T MR imaging. Optimized postprocessing was used to generate single-slice SWI of cerebral veins. SWI/FLAIR images were registered, and 3 trained readers performed lesion assessment. Morphology, location of lesions, and the time required for assessment were recorded. Analyses were performed on 3 different pools: 1) lesions of >3 mm, 2) nonconfluent lesions of >3 mm, and 3) nonconfluent lesions of >3 mm with no or a single central vein.

RESULTS

The SWI/FLAIR acquisition and processing protocol enabled effective assessment of central veins and hypointense rims in WM lesions. Assessment of nonconfluent lesions with ≥1 central vein enabled the most specific and sensitive differentiation of patients with MS from controls. A threshold of 67% perivenous WM lesions separated patients with MS from controls with a sensitivity of 94% and specificity of 100%. Lesion assessment took an average of 12 minutes 10 seconds and 4 minutes 33 seconds for patients with MS and control subjects, respectively.

CONCLUSIONS

Nonconfluent lesions of >3 mm with ≥1 central vein were the most sensitive and specific differentiators between patients with MS and control subjects.

Discovering missing reactions of metabolic networks by using gene co-expression data

Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

On correlated reaction sets and coupled reaction sets in metabolic networks

Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.