The hypothalamus orchestrates energy homeostasis via specialized neuronal populations and glial cells that integrate interoceptive signals, such as leptin, glucose, and fatty acids, through key intracellular energy sensors, like AMP-activated protein kinase (AMPK). This enables the hypothalamus to coordinate behavioral, autonomic, and neuroendocrine responses that regulate adipose mass and metabolism. This foundational Neuroendocrinology paradigm, forged through centuries of research, drives the mission of this Special Issue. From the cellular complexity of hypothalamic circuits and their hormonal regulation, to sex dimorphism and clinical relevance, as well as cutting-edge advances, such as single-cell hypothalamic mapping, circuit dynamics, and emerging therapeutic frontiers, this collection offers a comprehensive roadmap to the past, present and especially the future of hypothalamic regulation of energy homeostasis.
Abstract Transgenic mouse models expressing predefined T-cell receptors (TCRs) have been instrumental in advancing our understanding of T-cell biology. However, these traditional models rely on random genomic insertion of large constructs, require labor-intensive embryo manipulation, and frequently result in aberrant TCR expression and phenotypes. These limitations render traditional models insufficient to meet the mounting demands for rapid and precise model systems to evaluate TCR specificities. In this study, we developed a streamlined method that uses adeno-associated virus (AAV) and CRISPR/Cas9-mediated genome editing to precisely integrate pre-rearranged TCRα/β sequences into the mouse TCRβ (Trb) locus, enabling the rapid generation of TCR knock-in mice with physiological TCR expression and functional T-cell differentiation upon antigenic challenge. This approach bypasses the need for screening multiple founders for faithful TCR expression, enhancing the versatility and utility of monoclonal TCR mice in basic immunology and preclinical research, such as in the fields of cancer immunotherapy and vaccine development.
A hallmark of intelligence is proficiency in solving new problems, including those that substantially differ from previously seen problems. Problem solving in turn depends on the goal-directed generation of novel ideas and behaviours1, which has been proposed to involve internal representations of discrete units (or symbols) that can be recombined into numerous possible composite representations1-7. Although this view has been influential in cognitive-level explanations of behaviour, definitive evidence for a neuronal substrate of symbols has remained elusive. Here we identify a neural population that encodes action symbols-recombinable representations of discrete units of motor behaviour-in a specific area of the frontal cortex. In macaque monkeys performing a drawing-like task, we found behavioural evidence that action elements (strokes) exhibit three crucial features that indicate an underlying symbolic representation: (1) invariance over low-level motor parameters; (2) categorical structure, which reflects discrete action types; and (3) recombination into novel sequences. Based on simultaneous neural recordings across eight regions of the motor, premotor and prefrontal cortex, we identified population activity specifically in the ventral premotor cortex that encodes planned actions in a manner that also reflects invariance, categorical structure and recombination. These findings reveal a neural representation of action symbols localized to the ventral premotor cortex and a putative neural substrate for symbolic operations.
Darwinian evolution of immunoglobulin genes within germinal centers (GCs) underlies the progressive increase in antibody affinity following antigen exposure. Whereas the cellular mechanics of how competition between B cells increases affinity are well established, the evolutionary dynamics of this process are less clear. We developed an experimental evolution model in which we "replay" over one hundred monoclonal GC reactions, assigning affinities to each cell using deep mutational scanning. Our data reveal how GCs achieve predictable outcomes by means of noisy but persistent selection on an affinity landscape whose exploration is heavily constrained by somatic hypermutation biases. We infer a fitness landscape that quantitatively recapitulates the affinity maturation trajectory of our clone and find that apparent features of GC selection, such as permissiveness to low-affinity lineages and rapid plateauing of affinity, are likely artifacts of survivorship biases that distort our view of how B cell affinity progresses over time.
