Mucosal memory T cells in breastmilk are modulated by SARS-CoV-2 mRNA vaccination

We compared the phenotype, diversity, and antigen specificity of T cells in the breastmilk and peripheral blood of lactating individuals who received SARS-CoV-2 mRNA vaccination. Relative to blood, breastmilk contained higher frequencies of T effector and central memory populations that expressed mucosal-homing markers. T cell receptor (TCR) sequence overlap was limited between blood and breastmilk. Overabundant breastmilk clones were observed in all individuals, were structurally diverse, and contained CDR3 sequences with known epitope specificity including to SARS-CoV-2 Spike. Spike-specific TCRs were more frequent in breastmilk compared to blood and expanded in breastmilk following a third mRNA vaccine dose. Our observations indicate that the lactating breast contains a distinct T cell population that can be modulated by maternal vaccination with potential implications for infant passive protection.SummaryThe breastmilk T cell repertoire is distinct and enriched for SARS-CoV-2 Spike-specificity after maternal mRNA vaccination.

Visualizing the phenotype diversity: a case study of Alexander disease

Since only a small number of patients have a rare disease, it is difficult to identify all of the features of these diseases. This is especially true for patients presenting with the rarest diseases. It can also be difficult for the patient, their families, and even clinicians to know which one of a number of disease phenotypes the patient is exhibiting. This, again, is especially true for patients uncommonly presenting with rare diseases. To address this issue, during Biomedical Linked Annotation Hackathon 7 (BLAH7), we tried to extract Alexander disease patient data in Portable Document Format. We then visualized the phenotypic diversity of those Alexander disease patients with uncommon presentations. This led to us identifying several issues that we need to overcome in our future work.

Molecular Systems Predict Equilibrium Distributions of Phenotype Diversity Available for Selection

Two long-standing challenges in theoretical population genetics and evolution are predicting the distribution of phenotype diversity generated by mutation and available for selection and determining the interaction of mutation, selection, and drift to characterize evolutionary equilibria and dynamics. More fundamental for enabling such predictions is the current inability to causally link population genetic parameters, selection and mutation, to the underlying molecular parameters, kinetic and thermodynamic. Such predictions would also have implications for understanding cryptic genetic variation and the role of phenotypic robustness. Here we provide a new theoretical framework for addressing these challenges. It is built on Systems Design Space methods that relate system phenotypes to genetically-determined parameters and environmentally-determined variables. These methods, based on the foundation of biochemical kinetics and the deconstruction of complex systems into rigorously defined biochemical phenotypes, provide several innovations that automate (1) enumeration of the phenotypic repertoire without knowledge of kinetic parameter values, (2) representation of phenotypic regions and their relationships in a System Design Space, and (3) prediction of values for kinetic parameters, concentrations, fluxes and global tolerances for each phenotype. We now show that these methods also automate prediction of phenotype-specific mutation rate constants and equilibrium distributions of phenotype diversity in populations undergoing steady-state exponential growth. We introduce this theoretical framework in the context of a case study involving a small molecular system, a primordial circadian clock, compare and contrast this framework with other approaches in theoretical population genetics, and discuss experimental challenges for testing predictions.

Impacts of Epigenetic Processes on the Health and Productivity of Livestock

The dynamic changes in the epigenome resulting from the intricate interactions of genetic and environmental factors play crucial roles in individual growth and development. Numerous studies in plants, rodents, and humans have provided evidence of the regulatory roles of epigenetic processes in health and disease. There is increasing pressure to increase livestock production in light of increasing food needs of an expanding human population and environment challenges, but there is limited related epigenetic data on livestock to complement genomic information and support advances in improvement breeding and health management. This review examines the recent discoveries on epigenetic processes due to DNA methylation, histone modification, and chromatin remodeling and their impacts on health and production traits in farm animals, including bovine, swine, sheep, goat, and poultry species. Most of the reports focused on epigenome profiling at the genome-wide or specific genic regions in response to developmental processes, environmental stressors, nutrition, and disease pathogens. The bulk of available data mainly characterized the epigenetic markers in tissues/organs or in relation to traits and detection of epigenetic regulatory mechanisms underlying livestock phenotype diversity. However, available data is inadequate to support gainful exploitation of epigenetic processes for improved animal health and productivity management. Increased research effort, which is vital to elucidate how epigenetic mechanisms affect the health and productivity of livestock, is currently limited due to several factors including lack of adequate analytical tools. In this review, we (1) summarize available evidence of the impacts of epigenetic processes on livestock production and health traits, (2) discuss the application of epigenetics data in livestock production, and (3) present gaps in livestock epigenetics research. Knowledge of the epigenetic factors influencing livestock health and productivity is vital for the management and improvement of livestock productivity.

Proposed O-GalNAc/Gal Glycosylation Pathways in Blood Group O and Non-O Blood Group Phenotypes During Plasmodium falciparum Infections. A Hypothesis Regarding how ABO(H) Blood Group Phenotype Formation Drives Evolution

The coevolution of species drives diversity in animals and plants and contributes to natural selection, whereas in host–parasite coevolution, a parasite may complete an incomplete evolutionary/developmental function by utilizing the host cell’s machinery. Analysis of related older data suggests that Plasmodium falciparum (P. falciparum), the pathogen of malaria tropica, cannot survive outside its human host because it is unable to perform the evolutionarily first protein glycosylation of serologically A-like, O-GalNAcα1-Ser/Thr-R, Tn antigen (“T nouvelle”) formation, owing to its inability for synthesizing the amino sugar N-acetyl-d-galactosamine (GalNAc). Nevertheless, this parasite breaks the species barrier via hijacking the host's A-like/Tn formation through abundantly expressed serine residues and creating hybrid A-like/Tn structures, associated with the arising of the germline-encoded nonimmune polyreactive immunoglobulin M (IgM), exerting the highly anti-A/B/H-aggressive isoagglutinin avtivities. In the human, this nonimmune antibody molecule physiologically undergoes the ABO(H) blood group phenotype formation, occurring on the surfaces of red blood cells (RBC), epithelial and endothelia cells as well as on plasma proteins by identical glycosylation, performed by the ABO(H) allelic, specific glycotransferases in a single enzymatic step, reducing and/or removing anti-A/B/H-reactive IgM, or isoagglutinin activities. ABO(H) phenotype diversity, this way glycosidically linked to humoral immunity, becomes exposed to the evolution.

oposSOM-Browser: an interactive tool to explore omics data landscapes in health science

Background oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. Results These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular ‘portrait landscapes’, of associated phenotype diversity, and signalling pathway activation patterns. Conclusion The oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at www.izbi.uni-leipzig.de/opossom-browser.

Estimation Of The Genetic Parameters Of Eight Soybean Varieties In The Wasegi Village At Prafi District Manokwari Regency

This study aims to estimate genetic parameters including genetic diversity coefficient, phenotypic diversity coefficient, heritability value, and the correlation between the character of plants from eight soybean varieties. The research was conducted from August to December 2017, in the Wasafi Village of Prafi District, Manokwari Regency. The study was designed using Randomized Block Design (RBD) with 8 treatments of soybean varieties. Each treatment was repeated 4 times, to obtain 32 experimental units. The data obtained were analyzed using ANOVA and if it had a significant effect, it was further tested using the Duncan Multiple Range Test (DMRT) at the 95% level, through the Costat program. The results of ANOVA analysis were used to obtain Variable Partition values, Genetic Diversity Coefficient (GDC), Phenotype Diversity Coefficient (PDC), and Heritability. Correlations between characters were also analyzed using the Costat program. Estimation results of genetic parameters indicate that the characters selected for selection criteria are based on the value of GDC, PDC, and high heritability are number of branches, number of fertile books, number of filled pods, and number of total pods. The characters that show a positive correlation with the character of the results are plant height, number of trifoliate leaves, harvest age, number of filled pods, number of empty pods, total pods, and number of seeds per plant, while characters that show a negative correlation to a seed weight character are flowering age.