Interferons (IFNs) are signaling proteins that play fundamental roles during health and disease. Although types I, II, and III IFNs are structurally and functionally different, all IFNs signal via an intricate network of Janus kinases, named after the Roman god of time and duality. IFNs are characterized by activities that vary over time and can lead to opposing outcomes. IFNs have protective roles during bacterial, viral, and fungal infections but can also drive numerous inflammatory and autoimmune diseases. In this review, we provide an overview of the cellular and molecular mechanisms governing IFN induction and responses, emphasizing their roles in infections, tumorigenesis, and inflammatory, autoimmune, and genetic diseases, with particular attention to mucosal tissues. Overall, we spotlight how the balanced production of distinct members of the IFN families over time is necessary to exert their protective functions and the detrimental consequences for the host when this balance is lost.

Despite the evolution of hardwired homeostatic mechanisms to balance food intake with energy needs, the obesity epidemic continues to escalate globally. However, recent breakthroughs in delineating the molecular signaling pathways by which neural circuits regulate consummatory behaviors, along with transformative advances in peptide-based pharmacotherapy, are fueling the development of a new generation of safe and effective treatments for obesity. Here, we outline our current understanding of how the central nervous system controls energy homeostasis and examine how emerging insights, including those related to neuroplasticity, offer new perspectives for restoring energy balance and achieving durable weight loss. Together, these advances provide promising avenues for treating obesity and managing cardiometabolic disease.

Rapid advancements in artificial intelligence (AI), particularly large language models (LLMs) and multimodal AI, are transforming medicine through enhancements in diagnostics, patient interaction, and medical forecasting. LLMs enable conversational interfaces, simplify medical reports, and assist clinicians with decision making. Multimodal AI integrates diverse data like images and genetic data for superior performance in pathology and medical screening. AI-driven tools promise proactive, personalized healthcare through continuous monitoring and multiscale forecasting. However, challenges like bias, privacy, regulatory hurdles, and integration into healthcare systems must be addressed for widespread clinical adoption.

In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption and development of single-cell omic assays has sparked mounting enthusiasm that these technologies are poised to also enhance the precision of diagnosis, the monitoring of disease progression, and the personalization of therapeutic strategies. Despite initial forays into clinical settings, however, single-cell technologies are not yet routinely used to inform medical or surgical decision-making. Here, we identify and categorize key experimental, computational, and conceptual barriers that currently hinder the clinical deployment of single-cell omics. We focus on the potential for single-cell transcriptomics to guide clinical decision-making through the development of combinatorial biomarkers that simultaneously quantify multiple cell-type-specific pathophysiological processes. We articulate a framework to identify patient subpopulations that stand to benefit from such biomarkers, and we outline the experimental and computational requirements to derive reproducible and actionable clinical readouts from single-cell omics.

Despite promising evidence in diagnostics and therapeutics, microbiome research is not yet implemented into clinical medicine. Several initiatives, including the standardization of microbiome research, the refinement of microbiome clinical trial design, and the development of communication between microbiome researchers and clinicians, are crucial to move microbiome science toward clinical practice.

Major progress has been made in elucidating the molecular, cellular, and supracellular mechanisms underlying aging. This has spurred the birth of geroscience, which aims to identify actionable hallmarks of aging. Aging can be viewed as a process that is promoted by overactivation of gerogenes, i.e., genes and molecular pathways that favor biological aging, and alternatively slowed down by gerosuppressors, much as cancers are caused by the activation of oncogenes and prevented by tumor suppressors. Such gerogenes and gerosuppressors are often associated with age-related diseases in human population studies but also offer targets for modeling age-related diseases in animal models and treating or preventing such diseases in humans. Gerogenes and gerosuppressors interact with environmental, behavioral, and psychological risk factors to determine the heterogeneous trajectory of biological aging and disease manifestation. New molecular profiling technologies enable the characterization of gerogenic and gerosuppressive pathways, which serve as biomarkers of aging, hence inaugurating the era of precision geromedicine. It is anticipated that, pending results from randomized clinical trials and regulatory approval, gerotherapeutics will be tailored to each person based on their genetic profile, high-dimensional omics-based biomarkers of aging, clinical and digital biomarkers of aging, psychosocial profile, and past or present exposures.

Twenty years ago, histone lysine demethylases (KDMs) were discovered. Since their discovery, they have been increasingly studied and shown to be important across species, development, and diseases. Considerable advances have been made toward understanding their (1) enzymology, (2) role as critical components of biological complexes, (3) role in normal cellular processes and functions, (4) implications in pathological conditions, and (5) therapeutic potential. This Review covers these key relationships related to the KDM field with the awareness that numerous laboratories have contributed to this field. The current knowledge coupled with future insights will shape our understanding about cell function, development, and disease onset and progression, which will allow for novel biomarkers to be identified and for optimal therapeutic options to be developed for KDM-related diseases in the years ahead.

The advent of semi-dwarf crop varieties and fertilizers during the Green Revolution boosted yields and food security. However, unintended consequences such as environmental pollution and greenhouse gas emissions underscore the need for strategies to mitigate these impacts. Manipulating rhizosphere microbiomes, an aspect overlooked during crop domestication, offers a pathway for sustainable agriculture. We propose that modulating plant microbiomes can help establish "climate-smart crops" that improve yield and reduce negative impacts on the environment. Our proposed framework integrates plant genotype, root exudates, and microbes to optimize nutrient cycling, improve stress resilience, and expedite carbon sequestration. Integrating unselected ecological traits into crop breeding can promote agricultural sustainability, illuminating the nexus between plant genetics and ecosystem functioning.

Microbiome research has expanded significantly in the last two decades, yet translating findings into clinical applications remains challenging. This perspective discusses the persistent issue of correlational studies in microbiome research and proposes an iterative method leveraging in silico, in vitro, ex vivo, and in vivo studies toward successful preclinical and clinical trials. The evolution of research methodologies, including the shift from small cohort studies to large-scale, multi-cohort, and even "meta-cohort" analyses, has been facilitated by advancements in sequencing technologies, providing researchers with tools to examine multiple health phenotypes within a single study. The integration of multi-omics approaches-such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics-provides a comprehensive understanding of host-microbe interactions and serves as a robust hypothesis generator for downstream in vitro and in vivo research. These hypotheses must then be rigorously tested, first with proof-of-concept experiments to clarify the causative effects of the microbiota, and then with the goal of deep mechanistic understanding. Only following these two phases can preclinical studies be conducted with the goal of translation into the clinic. We highlight the importance of combining traditional microbiological techniques with big-data approaches, underscoring the necessity of iterative experiments in diverse model systems to enhance the translational potential of microbiome research.

Cardiomyopathies are primary disorders of the heart muscle. Three key phenotypes have been defined, based on morphology and arrhythmia burden: hypertrophic cardiomyopathy (HCM), with thickened heart muscle and diastolic dysfunction; dilated cardiomyopathy (DCM), with left ventricular enlargement and systolic dysfunction; and arrhythmogenic cardiomyopathy (ACM), with right, left, or biventricular involvement and arrhythmias out of proportion to systolic dysfunction. Genetic discoveries of the molecular basis of disease are paving the way for greater precision in diagnosis and management and revealing mechanisms that account for distinguishing clinical features. This deeper understanding has propelled the development of new treatments for cardiomyopathies: disease-specific, mechanistically based medicines that counteract pathophysiology, and emergent gene therapies that aim to intercept disease progression and restore cardiac physiology. Together, these discoveries have advanced fundamental insights into cardiac biology and herald a new era for patients with cardiomyopathy.

RNA plays a central role in protein biosynthesis and performs diverse regulatory and catalytic functions, making it essential for all processes of life. Like DNA, RNA is constantly subjected to damage from endogenous and environmental sources. However, while the DNA damage response has been extensively studied, it was long assumed that RNA lesions are relatively inconsequential due to the transient nature of most RNA molecules. Here, we review recent studies that challenge this view by revealing complex RNA damage responses that determine survival when cells are exposed to nucleic acid-damaging agents and promote the resolution of RNA lesions.

Solute carrier (SLC) proteins play critical roles in maintaining cellular and organismal homeostasis by transporting small molecules and ions. Despite a growing body of research over the past decade, physiological substrates and functions of many SLCs remain elusive. This perspective outlines key challenges in studying SLC biology and proposes an evidence-based framework for defining SLC substrates. To accelerate the deorphanization process, we explore systematic technologies, including human genetics, biochemistry, and computational and structural approaches. Finally, we suggest directions to better understand SLC functions beyond substrate identification in physiology and disease.

The incidence of early-onset colorectal cancer (EO-CRC) is surging, and by 2030, one-third of all CRCs will occur before the commonly recommended screening age of 50 years. The time required for EO-CRC to reach the metastatic stage is unknown, yet this knowledge is critical to tailor early-diagnosis screening strategies. Here, we discuss how defining a key biological feature of EO-CRC may be central to protecting young adults from an alarming and probably unprecedented tumor epidemic.

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.

The ribosome, together with its tRNA substrates, links genotype to phenotype by translating the genetic information carried by mRNA into protein. During the past half-century, the structure and mechanisms of action of the ribosome have emerged from mystery and confusion. It is now evident that the ribosome is an ancient RNA-based molecular machine of staggering structural complexity and that it is fundamentally similar in all living organisms. The three central functions of protein synthesis-decoding, catalysis of peptide bond formation, and translocation of mRNA and tRNA-are based on elegant mechanisms that evolved from the properties of RNA, the founding macromolecule of life. Moreover, all three of these functions (and even life itself) seem to proceed in defiance of entropy. Protein synthesis thus appears to exploit both the energy of GTP hydrolysis and peptide bond formation to constrain the directionality and accuracy of events taking place on the ribosome.

Since the introduction of the central dogma of molecular biology in 1958, various RNA species have been discovered. Messenger RNAs transmit genetic instructions from DNA to make proteins, a process facilitated by housekeeping non-coding RNAs (ncRNAs) such as small nuclear RNAs (snRNAs), ribosomal RNAs (rRNAs), and transfer RNAs (tRNAs). Over the past four decades, a wide array of regulatory ncRNAs have emerged as crucial players in gene regulation. In celebration of Cell's 50th anniversary, this Review explores our current understanding of the most extensively studied regulatory ncRNAs-small RNAs and long non-coding RNAs (lncRNAs)-which have profoundly shaped the field of RNA biology and beyond. While small RNA pathways have been well documented with clearly defined mechanisms, lncRNAs exhibit a greater diversity of mechanisms, many of which remain unknown. This Review covers pivotal events in their discovery, biogenesis pathways, evolutionary traits, action mechanisms, functions, and crosstalks among ncRNAs. We also highlight their roles in pathophysiological contexts and propose future research directions to decipher the unknowns of lncRNAs by leveraging lessons from small RNAs.

Every cell must solve the problem of how to fold its genome. We describe how the folded state of chromosomes is the result of the combined activity of multiple conserved mechanisms. Homotypic affinity-driven interactions lead to spatial partitioning of active and inactive loci. Molecular motors fold chromosomes through loop extrusion. Topological features such as supercoiling and entanglements contribute to chromosome folding and its dynamics, and tethering loci to sub-nuclear structures adds additional constraints. Dramatically diverse chromosome conformations observed throughout the cell cycle and across the tree of life can be explained through differential regulation and implementation of these basic mechanisms. We propose that the first functions of chromosome folding are to mediate genome replication, compaction, and segregation and that mechanisms of folding have subsequently been co-opted for other roles, including long-range gene regulation, in different conditions, cell types, and species.

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

Behavior is tightly synchronized with bodily physiology. Internal needs from the body drive behavior selection, while optimal behavior performance requires a coordinated physiological response. Internal state is dynamically represented by the nervous system to influence mood and emotion, and body-brain signals also direct responses to external sensory cues, enabling the organism to adapt and pursue its goals within an ever-changing environment. In this review, we examine the anatomy and function of the brain-body connection, manifested across local, reflex, and central regulation levels. We explore these hierarchical loops in the context of the immune system, specifically through the lens of immunoception, and discuss the impact of its dysregulation on human health.

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.