Pub Date : 2026-04-01Epub Date: 2026-01-17DOI: 10.1016/j.gde.2025.102433
NR Wray , T Lin , A Li , V de Almeida , M Ziller , J Zeng
Genome-wide association studies have provided empirical evidence that common diseases have a polygenic architecture, but with differences in estimable genetic architecture parameters such as proportion of the genomic sites associated with trait variation (polygenicity), the proportion of variance explained by those common DNA variants (SNP-based heritability), and signatures of selection evident from the relationship between allele frequency and effect size. Comparisons of genetic parameters across traits highlight that psychiatric disorders and other brain-related traits are significantly more polygenic than most other common diseases. Key questions are to understand in which tissues, cell-types, and biological contexts risk variants have a functional impact. We review recent progress and consider the next generation of experimental data needed to understand the biological basis of polygenic disorders.
{"title":"Progress in understanding the biological basis of polygenic disorders","authors":"NR Wray , T Lin , A Li , V de Almeida , M Ziller , J Zeng","doi":"10.1016/j.gde.2025.102433","DOIUrl":"10.1016/j.gde.2025.102433","url":null,"abstract":"<div><div>Genome-wide association studies have provided empirical evidence that common diseases have a polygenic architecture, but with differences in estimable genetic architecture parameters such as proportion of the genomic sites associated with trait variation (polygenicity), the proportion of variance explained by those common DNA variants (SNP-based heritability), and signatures of selection evident from the relationship between allele frequency and effect size. Comparisons of genetic parameters across traits highlight that psychiatric disorders and other brain-related traits are significantly more polygenic than most other common diseases. Key questions are to understand in which tissues, cell-types, and biological contexts risk variants have a functional impact. We review recent progress and consider the next generation of experimental data needed to understand the biological basis of polygenic disorders.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"97 ","pages":"Article 102433"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-20DOI: 10.1016/j.gde.2025.102432
Yulia Yarkhunova-Kreye , Angela M Hancock
Genome-wide association studies (GWAS) have advanced our understanding of trait variation, yet persistent challenges limit their effectiveness in mapping adaptive traits. Population structure, allelic heterogeneity, trait complexity, and structural variation obscure true causal relationships. However, these factors can often be addressed through strategic simplification. Here, we review factors limiting GWAS success in natural populations and show that reducing complexity improves mapping outcomes. We examine how local population mapping, trait decomposition into endophenotypes, and incorporation of structural variation enable the identification of adaptive loci. Using Arabidopsis thaliana as a model system, we demonstrate how these approaches reveal functionally validated variants missed by traditional studies. Finally, we discuss integration strategies and emerging technologies that will advance our understanding of the genetics of adaptation.
{"title":"Finding the genetic basis of adaptation: reducing complexity to improve trait mapping","authors":"Yulia Yarkhunova-Kreye , Angela M Hancock","doi":"10.1016/j.gde.2025.102432","DOIUrl":"10.1016/j.gde.2025.102432","url":null,"abstract":"<div><div>Genome-wide association studies (GWAS) have advanced our understanding of trait variation, yet persistent challenges limit their effectiveness in mapping adaptive traits. Population structure, allelic heterogeneity, trait complexity, and structural variation obscure true causal relationships. However, these factors can often be addressed through strategic simplification. Here, we review factors limiting GWAS success in natural populations and show that reducing complexity improves mapping outcomes. We examine how local population mapping, trait decomposition into endophenotypes, and incorporation of structural variation enable the identification of adaptive loci. Using <em>Arabidopsis thaliana</em> as a model system, we demonstrate how these approaches reveal functionally validated variants missed by traditional studies. Finally, we discuss integration strategies and emerging technologies that will advance our understanding of the genetics of adaptation.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"97 ","pages":"Article 102432"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-13DOI: 10.1016/j.gde.2026.102441
Sandeep Rajkumar , Carrie E Bearden , Jonathan Sebat , Lilia M Iakoucheva
Deletions and duplications of 16p11.2 and 22q11.2, along with other copy number variants (CNVs), are strongly implicated in neurodevelopmental disorders, including autism spectrum disorder and schizophrenia. While clinical data provide valuable insights, such data are limited in uncovering precise cellular and molecular mechanisms, and animal models often lack direct human relevance. Human induced pluripotent stem cell-derived 2D neuronal cultures, 3D brain organoids, and assembloids with 16p11.2 or 22q11.2 CNVs offer complementary systems to investigate altered pathways for future clinical translation. Studies using patient-derived or clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9-engineered induced pluripotent stem cells carrying these CNVs have identified a range of phenotypes that yield mechanistic insights. This review consolidates these findings, highlighting convergent and divergent phenotypes across reciprocal CNVs, and proposes promising molecular and cellular readouts for advancing translational research.
{"title":"Convergence and divergence of molecular phenotypes in iPSC-derived models of 16p11.2 and 22q11.2 reciprocal copy number variants","authors":"Sandeep Rajkumar , Carrie E Bearden , Jonathan Sebat , Lilia M Iakoucheva","doi":"10.1016/j.gde.2026.102441","DOIUrl":"10.1016/j.gde.2026.102441","url":null,"abstract":"<div><div>Deletions and duplications of 16p11.2 and 22q11.2, along with other copy number variants (CNVs), are strongly implicated in neurodevelopmental disorders, including autism spectrum disorder and schizophrenia. While clinical data provide valuable insights, such data are limited in uncovering precise cellular and molecular mechanisms, and animal models often lack direct human relevance. Human induced pluripotent stem cell-derived 2D neuronal cultures, 3D brain organoids, and assembloids with 16p11.2 or 22q11.2 CNVs offer complementary systems to investigate altered pathways for future clinical translation. Studies using patient-derived or clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9-engineered induced pluripotent stem cells carrying these CNVs have identified a range of phenotypes that yield mechanistic insights. This review consolidates these findings, highlighting convergent and divergent phenotypes across reciprocal CNVs, and proposes promising molecular and cellular readouts for advancing translational research.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"97 ","pages":"Article 102441"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-03DOI: 10.1016/j.gde.2026.102434
W. Brad Ruzicka , Sivan Subburaju
Schizophrenia is a highly heritable neuropsychiatric disorder affecting approximately 1% of the population, yet its pathophysiology has remained elusive due to the absence of gross neuropathological changes. In recent years, advances in transcriptomic profiling of postmortem human brain tissue have begun to illuminate the disease's molecular architecture. This mini-review synthesizes findings from case-control bulk tissue and single-nucleus transcriptomics studies, revealing that schizophrenia is characterized by widespread but subtle gene expression changes concentrated in excitatory neurons within the prefrontal cortex. Downregulation of synaptic and metabolic genes emerges as a consistent theme, accompanied by secondary activation of glial populations. Single-cell resolution studies demonstrate that these transcriptional alterations are cell type-specific and heterogeneous across individuals, with upper-layer excitatory neurons showing particular vulnerability. Despite methodological challenges inherent to postmortem tissue analysis, convergent evidence across multiple large-scale consortia establishes transcriptional dysregulation as a core feature of schizophrenia pathophysiology. Future directions include expanded cohorts and additional brain regions, as well as spatial transcriptomics and isoform-level analyses to fully map the molecular landscape of this complex disorder.
{"title":"Decoding schizophrenia through postmortem human brain transcriptomics","authors":"W. Brad Ruzicka , Sivan Subburaju","doi":"10.1016/j.gde.2026.102434","DOIUrl":"10.1016/j.gde.2026.102434","url":null,"abstract":"<div><div>Schizophrenia is a highly heritable neuropsychiatric disorder affecting approximately 1% of the population, yet its pathophysiology has remained elusive due to the absence of gross neuropathological changes. In recent years, advances in transcriptomic profiling of postmortem human brain tissue have begun to illuminate the disease's molecular architecture. This mini-review synthesizes findings from case-control bulk tissue and single-nucleus transcriptomics studies, revealing that schizophrenia is characterized by widespread but subtle gene expression changes concentrated in excitatory neurons within the prefrontal cortex. Downregulation of synaptic and metabolic genes emerges as a consistent theme, accompanied by secondary activation of glial populations. Single-cell resolution studies demonstrate that these transcriptional alterations are cell type-specific and heterogeneous across individuals, with upper-layer excitatory neurons showing particular vulnerability. Despite methodological challenges inherent to postmortem tissue analysis, convergent evidence across multiple large-scale consortia establishes transcriptional dysregulation as a core feature of schizophrenia pathophysiology. Future directions include expanded cohorts and additional brain regions, as well as spatial transcriptomics and isoform-level analyses to fully map the molecular landscape of this complex disorder.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"97 ","pages":"Article 102434"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The genetic architecture of psychiatric disorders is highly complex, with genome-wide association studies implicating thousands of risk loci. A central challenge is that most of these variants are located in noncoding regions, making it difficult to elucidate their regulatory consequences within the brain’s intricate cellular landscape. The recent convergence of advanced artificial intelligence, particularly deep learning (DL), has catalyzed a paradigm shift by providing powerful tools to address this gap. This review traces the evolution of DL in genomics, beginning with task-specific models. We then examine the transformative impact of foundation models, pretrained neural networks that learn the ‘language’ of biology, including genomic language models, single-cell foundation models, and large language models originally trained on natural language. Finally, we survey applications to key problems in psychiatric genomics. We hope this review provides a comprehensive overview of recent advances in DL for genomics and serves as a bridge to help researchers in psychiatric genomics more effectively understand and apply these frontier methods to guide the development of novel therapeutic strategies for psychiatric disorders.
{"title":"Deep learning for psychiatric genomics: from tools to applications","authors":"Junhao Liu, Siwei Xu, Dongbo Sun, Chaoyang Wang, Jing Zhang","doi":"10.1016/j.gde.2026.102442","DOIUrl":"10.1016/j.gde.2026.102442","url":null,"abstract":"<div><div>The genetic architecture of psychiatric disorders is highly complex, with genome-wide association studies implicating thousands of risk loci. A central challenge is that most of these variants are located in noncoding regions, making it difficult to elucidate their regulatory consequences within the brain’s intricate cellular landscape. The recent convergence of advanced artificial intelligence, particularly deep learning (DL), has catalyzed a paradigm shift by providing powerful tools to address this gap. This review traces the evolution of DL in genomics, beginning with task-specific models. We then examine the transformative impact of foundation models, pretrained neural networks that learn the ‘language’ of biology, including genomic language models, single-cell foundation models, and large language models originally trained on natural language. Finally, we survey applications to key problems in psychiatric genomics. We hope this review provides a comprehensive overview of recent advances in DL for genomics and serves as a bridge to help researchers in psychiatric genomics more effectively understand and apply these frontier methods to guide the development of novel therapeutic strategies for psychiatric disorders.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"97 ","pages":"Article 102442"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1016/j.gde.2026.102463
Ashutosh Tripathy, Gamze Cigdem Gelincik, Ana Pardo-Saganta
The adult lung maintains tissue integrity through adaptable regenerative programs that rely on the plasticity of epithelial progenitor cells across airway and alveolar compartments. Rather than following a linear stem-cell hierarchy, regeneration is driven by context-dependent lineage behaviors shaped by local signaling pathways, metabolic state, epigenetic regulation, and biophysical cues. Basal cells are the main stem cells of the airway epithelium, whereas alveolar type 2 cells act as the stem cells in the alveoli and regenerate the gas-exchange surface through defined transitional states. These processes are regulated by main signaling networks, including Wnt/β-catenin, Notch, fibroblast growth factor, bone morphogenetic protein / SMAD family proteins, and Hippo-Yes-assciated protein / transcriptional co-activator with PDZ-binding motif, which integrate niche-derived signals and mechanical inputs to control stem/progenitor activation and fate decisions. Disruption or persistence of these signaling networks leads to inefficient or aberrant repair and contributes to chronic lung disease. This review summarizes recent findings in cellular and molecular mechanisms of lung regeneration and highlights how controlled epithelial plasticity determines the balance between effective repair and disease-associated outcomes.
{"title":"Mechanisms of lung regeneration and repair.","authors":"Ashutosh Tripathy, Gamze Cigdem Gelincik, Ana Pardo-Saganta","doi":"10.1016/j.gde.2026.102463","DOIUrl":"https://doi.org/10.1016/j.gde.2026.102463","url":null,"abstract":"<p><p>The adult lung maintains tissue integrity through adaptable regenerative programs that rely on the plasticity of epithelial progenitor cells across airway and alveolar compartments. Rather than following a linear stem-cell hierarchy, regeneration is driven by context-dependent lineage behaviors shaped by local signaling pathways, metabolic state, epigenetic regulation, and biophysical cues. Basal cells are the main stem cells of the airway epithelium, whereas alveolar type 2 cells act as the stem cells in the alveoli and regenerate the gas-exchange surface through defined transitional states. These processes are regulated by main signaling networks, including Wnt/β-catenin, Notch, fibroblast growth factor, bone morphogenetic protein / SMAD family proteins, and Hippo-Yes-assciated protein / transcriptional co-activator with PDZ-binding motif, which integrate niche-derived signals and mechanical inputs to control stem/progenitor activation and fate decisions. Disruption or persistence of these signaling networks leads to inefficient or aberrant repair and contributes to chronic lung disease. This review summarizes recent findings in cellular and molecular mechanisms of lung regeneration and highlights how controlled epithelial plasticity determines the balance between effective repair and disease-associated outcomes.</p>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"98 ","pages":"102463"},"PeriodicalIF":3.6,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-29DOI: 10.1016/j.gde.2025.102424
Tuo Shi , Xin Jin
Although there are many known risk alleles associated with adult-onset psychiatric disorders such as schizophrenia [1–4], bipolar disorder [5–7], and major depressive disorder [8−10], the mechanistic links between these risk alleles and disease pathology, especially on a circuit-level, remain unclear. In vivo pooled CRISPR screening with single‑cell readout (in vivo Perturb‑seq) has begun to fill this gap by mapping causal genes to defined cell states directly in animal tissues [11–14]. Here, we review recent developments and applications of in vivo Perturb-seq in the mouse brain and highlight the potential of utilizing human cellular systems to extend these approaches. Additionally, we discuss how in vivo Perturb-seq can couple genetic perturbation with physiological or environmental perturbations to better model psychiatric diseases with environmental triggers.
虽然有许多已知的风险等位基因与成人发病的精神疾病相关，如精神分裂症[1-4]、双相情感障碍[5-7]和重度抑郁症[8-10]，但这些风险等位基因与疾病病理之间的机制联系，特别是在回路水平上，仍不清楚。利用单细胞读数（In vivo Perturb - seq）在体内汇集CRISPR筛选已经开始填补这一空白，通过直接在动物组织中将致病基因定位到确定的细胞状态[11-14]。在这里，我们回顾了体内Perturb-seq在小鼠大脑中的最新发展和应用，并强调了利用人类细胞系统扩展这些方法的潜力。此外，我们讨论了体内扰动序列如何将遗传扰动与生理或环境扰动耦合起来，以更好地模拟具有环境触发因素的精神疾病。
{"title":"Probing neuropsychiatric disorders through in vivo CRISPR screening","authors":"Tuo Shi , Xin Jin","doi":"10.1016/j.gde.2025.102424","DOIUrl":"10.1016/j.gde.2025.102424","url":null,"abstract":"<div><div>Although there are many known risk alleles associated with adult-onset psychiatric disorders such as schizophrenia [1–4], bipolar disorder [5–7], and major depressive disorder [8−10], the mechanistic links between these risk alleles and disease pathology, especially on a circuit-level, remain unclear. <em>In vivo</em> pooled CRISPR screening with single‑cell readout (<em>in vivo</em> Perturb‑seq) has begun to fill this gap by mapping causal genes to defined cell states directly in animal tissues [11–14]. Here, we review recent developments and applications of <em>in vivo</em> Perturb-seq in the mouse brain and highlight the potential of utilizing human cellular systems to extend these approaches. Additionally, we discuss how <em>in vivo</em> Perturb-seq can couple genetic perturbation with physiological or environmental perturbations to better model psychiatric diseases with environmental triggers.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"96 ","pages":"Article 102424"},"PeriodicalIF":3.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-08DOI: 10.1016/j.gde.2025.102425
Sebastian H Heesen , Man-Hsin Chang , Michael C Wehr , Moritz J Rossner
The success of comprehensive genome-wide association studies has substantiated the multigenetic origin of most mental disorders, including schizophrenia, bipolar disorder, and major depression. Non-coding genetic variants are enriched mainly in regulatory regions of genes expressed in excitatory and inhibitory neurons and converge particularly on cellular pathways implicated in neurodevelopment and synaptic functions. Given the molecular and cellular complexity of mental disorders, classical ‘single-drug-target-based’ drug discovery has largely failed in delivering novel pharmacological treatment options. We believe that drug development for complex disorders requires a paradigm shift toward a ‘phenotype or pathway focused’ approach, which integrates multi-parametric assay technologies and stem technology to perform screening and lead compound validation with dramatically enhanced contextual specificity. Moreover, many existing drugs used to treat mental disorders display polypharmacological actions. Therefore, there is a demand for developing assay technologies capable of dissecting the complex modes of action of novel drug candidates in a cost-effective manner. Here, we review technological progress across various fields that hold promise in delivering future breakthrough treatments for mental disorders.
{"title":"Revitalizing psychopharmacology in the GWAS era: the potential of barcoded screening in drug discovery","authors":"Sebastian H Heesen , Man-Hsin Chang , Michael C Wehr , Moritz J Rossner","doi":"10.1016/j.gde.2025.102425","DOIUrl":"10.1016/j.gde.2025.102425","url":null,"abstract":"<div><div>The success of comprehensive genome-wide association studies has substantiated the multigenetic origin of most mental disorders, including schizophrenia, bipolar disorder, and major depression. Non-coding genetic variants are enriched mainly in regulatory regions of genes expressed in excitatory and inhibitory neurons and converge particularly on cellular pathways implicated in neurodevelopment and synaptic functions. Given the molecular and cellular complexity of mental disorders, classical ‘single-drug-target-based’ drug discovery has largely failed in delivering novel pharmacological treatment options. We believe that drug development for complex disorders requires a paradigm shift toward a ‘phenotype or pathway focused’ approach, which integrates multi-parametric assay technologies and stem technology to perform screening and lead compound validation with dramatically enhanced contextual specificity. Moreover, many existing drugs used to treat mental disorders display polypharmacological actions. Therefore, there is a demand for developing assay technologies capable of dissecting the complex modes of action of novel drug candidates in a cost-effective manner. Here, we review technological progress across various fields that hold promise in delivering future breakthrough treatments for mental disorders.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"96 ","pages":"Article 102425"},"PeriodicalIF":3.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.gde.2025.102427
Deevitha Balasubramanian , Margarita Masoura , Yad Ghavi-Helm
Transcription is classically known to be regulated by two key elements, promoters and enhancers. While these remain central to gene regulation, it is now clear that additional regulatory sequences fine-tune enhancer function and transcriptional output. In this review, we focus on two such recently described sequences, promoter-proximal elements and enhancer-like modulators, highlighting representative examples of their function and their proposed mechanisms of action. We further discuss the implications of these discoveries on the current definitions of promoters and enhancers, and highlight an emerging theme that such elements do not fall into discrete classes but instead function along a regulatory continuum. Recognizing this continuum and appreciating transcriptional control as an interconnected network of elements will be essential for understanding gene regulation in complex genomes.
{"title":"Cooperativity between regulatory elements acts as a modulator of enhancer function","authors":"Deevitha Balasubramanian , Margarita Masoura , Yad Ghavi-Helm","doi":"10.1016/j.gde.2025.102427","DOIUrl":"10.1016/j.gde.2025.102427","url":null,"abstract":"<div><div>Transcription is classically known to be regulated by two key elements, promoters and enhancers. While these remain central to gene regulation, it is now clear that additional regulatory sequences fine-tune enhancer function and transcriptional output. In this review, we focus on two such recently described sequences, promoter-proximal elements and enhancer-like modulators, highlighting representative examples of their function and their proposed mechanisms of action. We further discuss the implications of these discoveries on the current definitions of promoters and enhancers, and highlight an emerging theme that such elements do not fall into discrete classes but instead function along a regulatory continuum. Recognizing this continuum and appreciating transcriptional control as an interconnected network of elements will be essential for understanding gene regulation in complex genomes.</div></div>","PeriodicalId":50606,"journal":{"name":"Current Opinion in Genetics & Development","volume":"96 ","pages":"Article 102427"},"PeriodicalIF":3.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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