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

NASA's Carbon Cycle OSSE Initiative - Informing Future Space-Based Observing Strategies Through Advanced Modeling and Data Assimilation

Land and ocean carbon sinks absorb half of human CO2 emissions. The fate of these sinks in a changing world is unknown, introducing large uncertainties in climate projections. Satellite measurements of atmospheric CO2 are required to better understand the processes governing carbon uptake. Careful planning of future missions using Observing System Simulation Experiments (OSSEs) can help ensure that they meet the needs of the scientific and policy communities. NASA's Carbon Cycle OSSE Initiative brings together researchers from multiple universities and NASA centers to create model-derived data products in support of informed mission planning

National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the Global Stocktake

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements, or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° × 1° gridded dataset and as a country-level dataset. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2 yr-1 (0.90–1.25 PgC yr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems

A Bacterial Acetyltransferase Destroys Plant Microtubule Networks and Blocks Secretion

The eukaryotic cytoskeleton is essential for structural support and intracellular transport, and is therefore a common target of animal pathogens. However, no phytopathogenic effector has yet been demonstrated to specifically target the plant cytoskeleton. Here we show that the Pseudomonas syringae type III secreted effector HopZ1a interacts with tubulin and polymerized microtubules. We demonstrate that HopZ1a is an acetyltransferase activated by the eukaryotic co-factor phytic acid. Activated HopZ1a acetylates itself and tubulin. The conserved autoacetylation site of the YopJ / HopZ superfamily, K289, plays a critical role in both the avirulence and virulence function of HopZ1a. Furthermore, HopZ1a requires its acetyltransferase activity to cause a dramatic decrease in Arabidopsis thaliana microtubule networks, disrupt the plant secretory pathway and suppress cell wall-mediated defense. Together, this study supports the hypothesis that HopZ1a promotes virulence through cytoskeletal and secretory disruption

Implication of a Chromosome 15q15.2 Locus in Regulating UBR1 and Predisposing Smokers to MGMT Methylation in Lung

O6-methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme that protects cells from carcinogenic effects of alkylating agents; however, MGMT is silenced by promoter hypermethylation during carcinogenesis. A single nucleotide polymorphism (SNP) in an enhancer in the MGMT promoter was previously identified to be highly significantly associated with risk for MGMT methylation in lung cancer and sputum from smokers. To further genetic investigations, a genome-wide association and replication study was conducted in two smoker cohorts to identify novel loci for MGMT methylation in sputum that were independent of the MGMT enhancer polymorphism. Two novel trans-acting loci (15q15.2 and 17q24.3) that were identified acted together with the enhancer SNP to empower risk prediction for MGMT methylation. We found that the predisposition to MGMT methylation arising from the 15q15.2 locus involved regulation of the ubiquitin protein ligase E3 component UBR1. UBR1 attenuation reduced turnover of MGMT protein and increased repair of O6-methylguanine in nitrosomethylurea-treated human bronchial epithelial cells (HBEC), while also reducing MGMT promoter activity and abolishing MGMT induction. Overall, our results substantiate reduced gene transcription as a major mechanism for predisposition to MGMT methylation in the lungs of smokers, and support the importance of UBR1 in regulating MGMT homeostasis and DNA repair of alkylated DNA adducts in cells

Neonatal Handling Affects Durably Bonding and Social Development

The neonatal period in humans and in most mammals is characterized by intense mother-young interactions favoring pair bonding and the adaptation of neonates to their new environment. However, in many post-delivery procedures, human babies commonly experience combined maternal separation and intense handling for about one hour post-birth. Currently, the effects of such disturbances on later attachment and on the development of newborns are still debated: clearly, further investigations are required. As animals present good models for controlled experimentation, we chose domestic horses to investigate this issue. Horses, like humans, are characterized by single births, long lactating periods and selective mother-infant bonds. Routine postnatal procedures for foals, as for human babies, also involve intense handling and maternal separation. In the present study, we monitored the behavior of foals from early stages of development to “adolescence”, in a normal ecological context (social groups with adults and peers). Experimental foals, separated from their mothers and handled for only 1 hour post-birth, were compared to control foals, left undisturbed after birth. Our results revealed short- and long-term effects of this unique neonatal experience on attachment and subsequent social competences. Thus, experimental foals presented patterns of insecure attachment to their mothers (strong dependence on their mothers, little play) and impaired social competences (social withdrawal, aggressiveness) at all ages. We discuss these results in terms of mother-young interactions, timing of interactions and relationships between bonding and subsequent social competences. Our results indicate that this ungulate species could become an interesting animal model. To our knowledge, this is the first clear demonstration that intervention just after birth affects bonding and subsequent social competences (at least until “adolescence”). It opens new research directions for studies on both humans and other animals

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