Physiology最新文献

Reflecting on the diversity of the natural world, Darwin famously observed that "from so simple a beginning endless forms most beautiful and most wonderful have been, and are being evolved". However, the examples that we are able to observe in nature are a consequence of chance, constrained by selection, drift and epistasis. Here we explore how the efforts of synthetic biology to build new living systems can expand our understanding of the fundamental design principles that allow life to self-organize biological form, from cellular to organismal levels. We suggest that the ability to impose a length or timescale onto a biological activity is an essential strategy for self-organization in evolved systems and a key design target that is now being realized synthetically at all scales. By learning to integrate these strategies together, we are poised to expand on evolution's success and realize a space of synthetic forms not only beautiful but with diverse applications and transformative potential.

Over the past several decades, physiological research has undergone a progressive shift toward greater-and-greater reductionism, culminating in the rise of 'molecular physiology.' The introduction of Omic techniques, chiefly protein mass spectrometry and next-generation DNA sequencing (NGS), has further accelerated this trend, adding massive amounts of information about individual genes, mRNA transcripts, and proteins. However, the long-term goal of understanding physiological and pathophysiological processes at a whole-organism level has not been fully realized. This review summarizes the major protein mass spectrometry and NGS techniques relevant to physiology and explores the challenges of merging data from Omic methodologies with data from traditional hypothesis-driven research to broaden the understanding of physiological mechanisms. It summarizes recent progress in large-scale data integration through: 1) creation of online user-friendly Omic data resources with cross-indexing across data sets to democratize access to Omic data; 2) application of Bayesian methods to combine data from multiple Omic data sets with knowledge from hypothesis-driven studies in order to address specific physiological and pathophysiological questions; and 3) application of concepts from Natural Language Processing to probe the literature and to create user-friendly causal graphs representing physiological mechanisms. Progress in development of so-called "Large Language Models", e.g. ChatGPT, for knowledge integration is also described along with a discussion of the shortcomings of Large Language Models with regard to management and integration of physiological data.

Genomic rearrangements play an important role in shaping genetic diversity, as they enable the generation of novel structural variations through specific genome manipulation tools. These variations contribute to phenotypic differences among individuals within a population, thereby serving as the foundation for natural selection and driving evolutionary processes. In recent years, Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) has emerged as a promising tool for studying genomic rearrangements. SCRaMbLE utilizes site-specific recombination mediated by loxPsym sites to induce targeted chromosomal rearrangements in yeast cells. In this review, we provide a comprehensive overview of recent advancements in optimization strategies of the SCRaMbLE system and discuss influential factors that affect its performance based on recent research findings. We demonstrate how the SCRaMbLE system can be employed for pathway engineering, phenotype improvement, genome minimization, and dissection of genotype-to-phenotype relationships. We highlight both the advantages and challenges associated with SCRaMbLE and envision its potential applications beyond yeast genetics.

Understanding neural mechanisms of memory has been one of the key questions in biology. Long-term memory specifically, allows one to travel mentally without constraints of time and space (1). A long-term memory must have gone through a series of temporal processes: encoding, consolidation, storage, and retrieval. Decades of studies have revealed cellular and molecular mechanisms underlying each process. In this paper, we will first review the emerging concept of memory engrams and technologies of engram labeling, as these methods provide a new avenue to study the molecular mechanisms for memory. Then, we will focus on DNA methylation and its role in long-term memory. Lastly, we will discuss some key remaining questions in this field and their implications in memory-related disease.

Hormones orchestrate virtually all physiological processes in animals, and enable them to adjust internal responses to meet diverse physiological demands. Studies in both vertebrates and insects have uncovered many novel hormones and dissected the physiological mechanisms they regulate, demonstrating a remarkable conservation in endocrine signaling across the tree of life. In this review, we focus on recent advances in insect research, which have provided a more integrative view of the conserved interorgan communication networks that control physiology These new insights have been driven by experimental advantages inherent to insects, which over the past decades have aligned with new technologies and sophisticated genetic tools, to transform insect genetic models into a powerful testbed for posing new questions and exploring longstanding issues in endocrine research. Here, we illustrate how insect studies have addressed classic questions in three main areas-hormonal control of growth and development, neuroendocrine regulation of ion and water balance, and hormonal regulation of behavior and metabolism- and how these discoveries have illuminated our fundamental understanding of endocrine signaling in animals. The application of integrative physiology in insect systems to questions in endocrinology and physiology is expanding, and is poised to be a crucible of discovery, revealing fundamental mechanisms of hormonal regulation that underlie animal adaptations to their environments.

As life expectancy increases globally, the prevalence and severity of age-related disease has risen, significantly impacting patients' quality of life and increasing dependency on the health care system. Age-related diseases share several pathological commonalities, and emerging evidence suggests that targeting these biological processes ameliorate multiple age-related diseases. Immune aging plays a critical role in the pathogenesis of age-related diseases, given its involvement not only in controlling infection and cancer but also in facilitating tissue homeostasis and repair. Aging causes compositional and functional changes in both innate and adaptive immune cells, thereby significantly contributing to the pathogenesis of age-related disease and systemic low-grade inflammation, termed as "inflammaging." This review article aims to describe the current understanding of immune aging and its impact on age-related diseases with particular emphasis on kidney and autoimmune disease. Additionally, this review highlights tertiary lymphoid structures (TLS) as a hallmark of immune aging, exploring their roles in inflammation, tissue damage and potential therapeutic targeting.

Recent developments in artificial intelligence (AI) may significantly alter physiological research and healthcare delivery. Whereas AI applications in medicine have historically been trained for specific tasks, recent technological advances have produced models trained on more diverse datasets with much higher parameter counts. These new, "foundation" models raise the possibility that more flexible AI tools can be applied to a wider set of healthcare tasks than in the past. This review describes how these newer models differ from conventional task-specific AI, which relies heavily on focused datasets and narrow, specific applications. By examining the integration of AI into diagnostic tools, personalized treatment strategies, biomedical research, and healthcare administration, we highlight how these newer models are revolutionizing predictive healthcare analytics and operational workflows. In addition, we address ethical and practical considerations associated with the use of foundation models by highlighting emerging trends, calling for changes to existing guidelines, and emphasizing the importance of aligning AI with clinical goals to ensure its responsible and effective use.

Hematopoietic stem cells (HSCs) possess the capacity for self-renewal and the sustained production of all mature blood cell lineages. It has been well established that a metabolic rewiring controls the switch of HSCs from a self-renewal state to a more differentiated state, but it is only recently that we have appreciated the importance of metabolic pathways in regulating the commitment of progenitors to distinct hematopoietic lineages. In the context of erythroid differentiation, an extensive network of metabolites, including amino acids, sugars, nucleotides, fatty acids, vitamins, and iron, is required for red blood cell (RBC) maturation. In this review, we highlight the multifaceted roles via which metabolites regulate physiological erythropoiesis as well as the effects of metabolic perturbations on erythroid lineage commitment and differentiation. Of note, the erythroid differentiation process is associated with an exceptional breadth of solute carrier (SLC) metabolite transporter upregulation. Finally, we discuss how recent research, revealing the critical impact of metabolic reprogramming in diseases of disordered and ineffective erythropoiesis, has created opportunities for the development of novel metabolic-centered therapeutic strategies.