Adrenomedullin (AM) is a peptide hormone with numerous effects in the vascular systems. AM signals through the AM1 and AM2 receptors formed by the obligate heterodimerization of a G protein-coupled receptor, the calcitonin receptor-like receptor (CLR), and receptor activity-modifying proteins 2 and 3 (RAMP2 and RAMP3), respectively. These different CLR-RAMP interactions yield discrete receptor pharmacology and physiological effects. The effective design of therapeutics that target the individual AM receptors is dependent on understanding the molecular details of the effects of RAMPs on CLR. To understand the role of RAMP2 and -3 on the activation and conformation of the CLR subunit of AM receptors, we mutated 68 individual amino acids in the juxtamembrane region of CLR, a key region for activation of AM receptors, and determined the effects on cAMP signaling. Sixteen CLR mutations had differential effects between the AM1 and AM2 receptors. Accompanying this, independent molecular modeling of the full-length AM-bound AM1 and AM2 receptors predicted differences in the binding pocket and differences in the electrostatic potential of the two AM receptors. Druggability analysis indicated unique features that could be used to develop selective small molecule ligands for each receptor. The interaction of RAMP2 or RAMP3 with CLR induces conformational variation in the juxtamembrane region, yielding distinct binding pockets, probably via an allosteric mechanism. These subtype-specific differences have implications for the design of therapeutics aimed at specific AM receptors and for understanding the mechanisms by which accessory proteins affect G protein-coupled receptor function.

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Stem cells (SCs) are unique cells that have an inherent ability to self-renew or differentiate. Both fate decisions are strongly regulated at the molecular level via intricate signaling pathways. The regulation of signaling networks promoting self-renewal or differentiation was thought to be largely governed by the action of transcription factors. However, small noncoding RNAs (ncRNAs), such as vault RNAs, and their post-transcriptional modifications (the epitranscriptome) have emerged as additional regulatory layers with essential roles in SC fate decisions. RNA post-transcriptional modifications often modulate RNA stability, splicing, processing, recognition, and translation. Furthermore, modifications on small ncRNAs allow for dual regulation of RNA activity, at both the level of biogenesis and RNA-mediated actions. RNA posttranscriptional modifications act through structural alterations and specialized RNAbinding proteins (RBPs) called writers, readers, and erasers. It is through SC-context RBPs that the epitranscriptome coordinates specific functional roles. Small ncRNA post-transcriptional modifications are today exploited by different mechanisms to facilitate SC translational studies. One mechanism readily being studied is identifying how SC-specific RBPs of small ncRNAs regulate fate decisions. Another common practice of using the epitranscriptome for regenerative applications is using naturally occurring post-transcriptional modifications on synthetic RNA to generate induced pluripotent SCs. Here, we review exciting insights into how small ncRNA posttranscriptional modifications control SC fate decisions in development and disease. We hope, by illustrating how essential the epitranscriptome and their associated proteome are in SCs, they would be considered as novel tools to propagate SCs for regenerative medicine.

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.