Newborns With Zika Virus-Associated Microcephaly Exhibit Marked Systemic Inflammatory Imbalance

Abstract

Background Zika virus (ZIKV) is an emergent flavivirus initially considered a benign and self-limited exanthematic illness. In 2015, a new epidemic emerged in northeastern of Brazil with increased incidence of a previously rare clinical outcome, microcephaly, in newborns from mothers who were infected during pregnancy. Little is known about the immunopathogenesis of ZIKV-associated microcephaly. Understanding the inflammatory profile and degree of inflammation of persons affected with such condition is an important step towards development of innovative therapeutic strategies. Methods A case-control study compared plasma levels of several inflammatory biomarkers from newborns with ZIKV microcephaly, asymptomatic ZKV infection, or uninfected controls. Plasma biomarkers were assessed using Luminex. A series of multidimensional analysis was performed to characterize the systemic immune activation profile of the clinical groups. Results We identified an inflammatory signature associated with ZIKV microcephaly that suggested an increased inflammation. Network analysis suggested that ZIKV microcephaly is associated with imbalanced immune activation and inflammation. The cephalic perimeter was inversely proportional with the degree of inflammatory perturbation. Furthermore, a combination of plasma inflammatory biomarkers could discriminate ZIKV with microcephaly from those with ZIKV without microcephaly or uninfected neonates. Conclusions An intense inflammatory imbalance that is proportional to the disease severity hallmarks ZIKV microcephaly.

Heightened Plasma Levels of Transforming Growth Factor Beta (TGF-β) and Increased Degree of Systemic Biochemical Perturbation Characterizes Hepatic Steatosis in Overweight Pediatric Patients: A Cross-Sectional Study

“…Among all markers, only median values of ALT, GGT, and TGF-β were statistically distinct between the study groups, with higher values being detected in the group of participants with hepatic steatosis (all with adjusted p -values < 0.05). After identifying the consistent changes in the concentrations of biochemical parameters between the clinical groups, we employed a discriminant model using sparse canonical correlation analysis (CCA), as previously described [ 32 , 33 , 34 ] to test whether the statistical relationships between the biomarkers, rather their concentrations/values themselves, could be used to distinguish the groups. Using this approach, we found that the patients with hepatic steatosis could be distinguished from those without steatosis with high degree of accuracy (area under the curve (AUC) of the receiver operator characteristics (ROC) curve = 0.97 (CI: 0.85–1.0), sensitivity 91% (CI: 77.0–96.7), specificity 86% (72.0–94.0); p < 0.0001, Figure 1 B, left panel).…”

“…Given that we observed in the results presented above that the overall profile of correlations was distinct between the study groups, we then tried to visualize the numerous correlations detected. We employed an approach using network analysis in which Spearman correlations are visualized as connections between the parameters [ 33 , 34 , 35 , 36 , 37 ]. This approach helped us to identify the dynamicity, strength and quality of the relationships between values of the biochemical markers and inflammatory proteins in plasma of the different groups of individuals.…”

“…To deepen the analyses in overall disturbances in biochemical profiles, we calculated the Degree of Biochemical Perturbation (DBP, as described in Methods) ( Figure 4 A), which is an adaptation from the molecular degree of perturbation previously published [ 33 , 34 , 35 ]. We found that patients with hepatic steatosis exhibited substantial increase in DBP score values compared to those without steatosis ( p < 0.0001).…”

“…A discriminant model using sparse canonical correlation analysis (CCA) was employed to assess whether a combination of circulating biomarkers could discriminate individuals with hepatic steatosis from those without this condition. Our experience with biomarkers has revealed that not only is the concentrations of a given parameter important, but also the correlation profile with other parameters is critical in evaluation of its involvement in a biological phenomenon [ 32 , 33 ]. Thus, we employed basically two types of analyses: one based on individual concentration values and a second exploring the profile of correlations between several markers in each clinical group.…”

“…The molecular degree of perturbation (MDP) was calculated using values of the biochemical parameters to infer the degree of biochemical perturbation (DBP) associated with hepatic steatosis. This method has been used and detailed previously [ 33 , 34 , 35 ]. In the present study, “No steatosis” was defined as the “reference” group, and the average level and standard deviation of this reference group were calculated for the plasma concentrations of each inflammatory marker.…”