Molecular engineering improves antigen quality and enables integrated manufacturing of a trivalent subunit vaccine candidate for rotavirus

Background Vaccines comprising recombinant subunit proteins are well-suited to low-cost and high-volume production for global use. The design of manufacturing processes to produce subunit vaccines depends, however, on the inherent biophysical traits presented by an individual antigen of interest. New candidate antigens typically require developing custom processes for each one and may require unique steps to ensure sufficient yields without product-related variants. Results We describe a holistic approach for the molecular design of recombinant protein antigens—considering both their manufacturability and antigenicity—informed by bioinformatic analyses such as RNA-seq, ribosome profiling, and sequence-based prediction tools. We demonstrate this approach by engineering the product sequences of a trivalent non-replicating rotavirus vaccine (NRRV) candidate to improve titers and mitigate product variants caused by N-terminal truncation, hypermannosylation, and aggregation. The three engineered NRRV antigens retained their original antigenicity and immunogenicity, while their improved manufacturability enabled concomitant production and purification of all three serotypes in a single, end-to-end perfusion-based process using the biotechnical yeast Komagataella phaffii. Conclusions This study demonstrates that molecular engineering of subunit antigens using advanced genomic methods can facilitate their manufacturing in continuous production. Such capabilities have potential to lower the cost and volumetric requirements in manufacturing vaccines based on recombinant protein subunits

Prenatal and perinatal factors associated with neonatal neurobehavioral profiles in the ECHO Program

BackgroundSingle-cohort studies have identified distinct neurobehavioral profiles that are associated with prenatal and neonatal factors based on the NICU Network Neurobehavioral Scale (NNNS). We examined socioeconomic, medical, and substance use variables as predictors of NNNS profiles in a multi-cohort study of preterm and term-born infants with different perinatal exposures.MethodsWe studied 1112 infants with a neonatal NNNS exam from the Environmental influences on Child Health Outcomes (ECHO) consortium. We used latent profile analysis to characterize infant neurobehavioral profiles and generalized estimating equations to determine predictors of NNNS profiles.ResultsSix distinct neonatal neurobehavioral profiles were identified, including two dysregulated profiles: a hypo-aroused profile (16%) characterized by lethargy, hypotonicity, and nonoptimal reflexes; and a hyper-aroused profile (6%) characterized by high arousal, excitability, and stress, with low regulation and poor movement quality. Infants in the hypo-aroused profile were more likely to be male, have younger mothers, and have mothers who were depressed prenatally. Infants in the hyper-aroused profile were more likely to be Hispanic/Latino and have mothers who were depressed or used tobacco prenatally.ConclusionsWe identified two dysregulated neurobehavioral profiles with distinct perinatal antecedents. Further understanding of their etiology could inform targeted interventions to promote positive developmental outcomes.ImpactPrior research on predictors of neonatal neurobehavior have included single-cohort studies, which limits generalizability of findings.In a multi-cohort study of preterm and term-born infants, we found six distinct neonatal neurobehavioral profiles, with two profiles being identified as dysregulated.Hypo- and hyper-aroused neurobehavioral profiles had distinct perinatal antecedents.Understanding perinatal factors associated with dysregulated neurobehavior could help promote positive developmental outcomes

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Trolley dilemma in the sky: Context matters when civilians and cadets make remotely piloted aircraft decisions

Crews operating remotely piloted aircrafts (RPAs) in military operations may be among the few that truly experience tragic dilemmas similar to the famous Trolley Problem. In order to analyze decision-making and emotional conflict of RPA operators within Trolley-Problem-like dilemma situations, we created an RPA simulation that varied mission contexts (firefighter, military and surveillance as a control condition) and the social “value” of a potential victim. We found that participants (Air Force cadets and civilian students) were less likely to make the common utilitarian choice (sacrificing one to save five), when the value of the one increased, especially in the military context. However, in the firefighter context, this decision pattern was much less pronounced. The results demonstrate behavioral and justification differences when people are more invested in a particular context despite ostensibly similar dilemmas

Measurement and Quantification of Gross Human Shoulder Motion

The shoulder girdle plays an important role in the large pointing workspace that humans enjoy. The goal of this work was to characterize the human shoulder girdle motion in relation to the arm. The overall motion of the human shoulder girdle was characterized based on motion studies completed on test subjects during voluntary (natural/unforced) motion. The collected data from the experiments were used to develop surface fit equations that represent the position and orientation of the glenohumeral joint for a given humeral pointing direction. These equations completely quantify gross human shoulder girdle motion relative to the humerus. The equations are presented along with goodness-of-fit results that indicate the equations well approximate the motion of the human glenohumeral joint. This is the first time the motion has been quantified for the entire workspace, and the equations provide a reference against which to compare future work

Effects of Filtering the Center of Pressure Feedback Provided in Visually Guided Mediolateral Weight Shifting

<div><p>Thirty healthy adults completed a mediolateral weight-shifting balance task in which they were instructed to shift their weight to visually displayed target regions. A model-based filter and three different moving average filters employing 10, 34, and 58 samples were applied to the center of pressure visual feedback that guided the activity. The effects of filter selection on both the displayed feedback and the shift performance were examined in terms of shift time and non-minimum phase behavior. Shift time relates to feedback delay and shift speed, whereas non-minimum phase behavior relates to the force applied in shift initiation. Results indicated that increasing the number of samples in moving average filters (indicative of stronger filtering) significantly increases shift speed and shift initiation force. These effects indicate that careful selection and documentation of data filtering is warranted in future work and suggest opportunities for strategic filtering of visual feedback in clinical weight-shifting balance activities in order to improve outcomes based on such feedback.</p></div