An approximation approach to spatial connectivity for a data-limited endangered species with implications for habitat restoration

Among numerous concerns, restoration ecologists are routinely plagued with the problem of where to implement conservation efforts to best maintain spatial connectivity and population structure. Knowledge about connectivity within a metapopulation could offer valuable insight to address this issue and could help with the allocation of limited resources more effectively. However, direct estimation of dispersal is challenging because species can disperse widely within a landscape. Here, we developed a novel hierarchical Bayesian model to estimate spatial connectivity from occurrence data of an endangered stream fish, Topeka shiner (Notropis topeka). Our goal was to identify dispersal corridors that are centrally connected to the metapopulation that could be beneficial in decision making about future habitat restorations aimed at maintaining population structure. Connectivity modeling is data intensive and resource managers may not have the necessary data requirements; thus, we also examined the usefulness of graph theory (i.e. network centrality) as a proxy for connectivity. Model selection identified an upstream biased asymmetric dispersal pattern for the species. We were able to quantify and map connectivity and identified over 68 km of stream reaches as highly connected to the metapopulation. Probability of occurrence in dispersal corridors (i.e. streams) increased with connectivity and decreased with drainage area, highlighting the importance of conserving dispersal corridors and preferred habitat patches. Restorations in connected locations would provide critical habitat near important dispersal corridors. Betweenness centrality was positively correlated with connectivity and occurrence in restored habitat. Modeling of metapopulation connectivity and its correlation with graph theory demonstrated the usefulness of these techniques to guide conservation actions, especially in countries where data collection efforts are not common and conservation funding is limited.This article is published as Wahl, Charles F., Nika Galic, Richard Brain, Maxime Vaugeois, Michael Weber, Kevin J. Roe, Timothy Stewart et al. "An approximation approach to spatial connectivity for a data-limited endangered species with implications for habitat restoration." Biological Conservation 291 (2024): 110470. doi:10.1016/j.biocon.2024.110470. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Coreceptor affinity for MHC defines peptide specificity requirements for TCR interaction with coagonist peptide–MHC

Recent work has demonstrated that nonstimulatory endogenous peptides can enhance T cell recognition of antigen, but MHCI- and MHCII-restricted systems have generated very different results. MHCII-restricted TCRs need to interact with the nonstimulatory peptide–MHC (pMHC), showing peptide specificity for activation enhancers or coagonists. In contrast, the MHCI-restricted cells studied to date show no such peptide specificity for coagonists, suggesting that CD8 binding to noncognate MHCI is more important. Here we show how this dichotomy can be resolved by varying CD8 and TCR binding to agonist and coagonists coupled with computer simulations, and we identify two distinct mechanisms by which CD8 influences the peptide specificity of coagonism. Mechanism 1 identifies the requirement of CD8 binding to noncognate ligand and suggests a direct relationship between the magnitude of coagonism and CD8 affinity for coagonist pMHCI. Mechanism 2 describes how the affinity of CD8 for agonist pMHCI changes the requirement for specific coagonist peptides. MHCs that bind CD8 strongly were tolerant of all or most peptides as coagonists, but weaker CD8-binding MHCs required stronger TCR binding to coagonist, limiting the potential coagonist peptides. These findings in MHCI systems also explain peptide-specific coagonism in MHCII-restricted cells, as CD4–MHCII interaction is generally weaker than CD8–MHCI.National Institutes of Health (U.S.). Pioneer Awar

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/