Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis.

Introduction: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which early recognition is a major challenge. Autoantibodies against fetal brain antigens have been found in the blood of mothers of children with ASD (m-ASD) and can be transferred to the fetus where they can impact neurodevelopment by binding to fetal brain proteins. This study aims to identify novel maternal autoantibodies reactive against human fetal brain antigens, and explore their use as biomarkers for ASD screening and diagnosis. Methods: A custom-made human fetal brain cDNA phage display library was constructed, and screened for antibody reactivity in m-ASD samples from the Simons Simplex Collection (SSC) of the Simons Foundation Autism Research Initiative (SFARI). Antibody reactivity against 6 identified antigens was determined in plasma samples of 238 m-ASD and 90 mothers with typically developing children (m-TD). Results: We identified antibodies to 6 novel University Hasselt (UH)-ASD antigens, including three novel m-ASD autoantigens, i.e., ribosomal protein L23 (RPL23), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and calmodulin-regulated spectrin-associated protein 3 (CAMSAP3). Antibody reactivity against a panel of four of these targets was found in 16% of m-ASD samples, compared to 4% in m-TD samples (p = 0.0049). Discussion: Maternal antibodies against 4 UH-ASD antigens could therefore provide a novel tool to support the diagnosis of ASD in a subset of individuals.

Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2.

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

Comparison of two simulators for individual based models in HIV epidemiology in a population with HSV 2 in Yaoundé (Cameroon).

Model comparisons have been widely used to guide intervention strategies to control infectious diseases. Agreement between different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their predictions. In this study, we compared models implemented by two individual-based model simulators for HIV epidemiology in a heterosexual population with Herpes simplex virus type-2 (HSV-2). For each model simulator, we constructed four models, starting from a simplified basic model and stepwise including more model complexity. For the resulting eight models, the predictions of the impact of behavioural interventions on the HIV epidemic in Yaoundé-Cameroon were compared. The results show that differences in model assumptions and model complexity can influence the size of the predicted impact of the intervention, as well as the predicted qualitative behaviour of the HIV epidemic after the intervention. These differences in predictions of an intervention were also observed for two models that agreed in their predictions of the HIV epidemic in the absence of that intervention. Without additional data, it is impossible to determine which of these two models is the most reliable. These findings highlight the importance of making more data available for the calibration and validation of epidemiological models.

Antibodies Against Three Novel Peptides in Early Axial Spondyloarthritis Patients From Two Independent Cohorts.

OBJECTIVE: This study was undertaken to identify novel autoantibodies in axial spondyloarthritis (SpA) and determine their diagnostic potential in patients with early axial SpA and controls from 2 independent cohorts. METHODS: An axial SpA complementary DNA phage display library was used to screen for novel IgG antibodies in plasma from patients with early axial SpA. The presence of these antibodies against novel peptides (i.e., peptides identified in an early axial SpA cohort from Hasselt University, designated UH-axSpA) was determined by enzyme-linked immunosorbent assay in 76 patients with early axial SpA, 75 controls with nonspecific chronic low back pain, 60 patients with rheumatoid arthritis, and 94 healthy controls from the UH cohort. Antibody reactivity to these novel peptides was further validated in 174 patients with axial SpA (of whom 79 had early axial SpA) from the University Hospitals Leuven (Bio)SPAR (Spondyloarthritis [Biologics]) cohort. RESULTS: We identified antibodies to 9 novel UH-axSpA peptides, corresponding to randomly formed peptides and to a novel axial SpA autoantigen, double homeobox protein 4. Antibodies to 3 UH-axSpA peptides with the highest positive likelihood ratio (LR) for a diagnosis of axial SpA were present in significantly more patients with early axial SpA from the UH and (Bio)SPAR cohorts (14.2% [22/155]) compared to controls with chronic low back pain (5% [4/75]), resulting in 95% specificity. The positive LR for confirming axial SpA using antibodies to these 3 UH-axSpA peptides was 2.7, which is higher than the LR obtained with the currently used laboratory marker C-reactive protein. Testing for antibodies to these 3 UH-axSpA peptides in patients with chronic low back pain increased the posttest probability of a diagnosis of axial SpA from 79% to 91%. CONCLUSION: Antibodies to 3 UH-axSpA peptides could provide a novel tool in the diagnosis of a subset of axial SpA patients.

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces.

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic "intervention" event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.