Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution

Comprehensive analysis of m6A methylome alterations after azacytidine plus venetoclax treatment for acute myeloid leukemia by nanopore sequencing

N6 adenosine methylation (m6A), one of the most prevalent internal modifications on mammalian RNAs, regulates RNA transcription, stabilization, and splicing. Growing evidence has focused on the functional role of m6A regulators on acute myeloid leukemia (AML). However, the global m6A levels after azacytidine (AZA) plus venetoclax (VEN) treatment in AML patients remain unclear. In our present study, bone marrow (BM) sample pairs (including pre-treatment [AML] and post-treatment [complete remission (CR)] samples) were harvested from three AML patients who had achieved CR after AZA plus VEN treatment for Nanopore direct RNA sequencing. Notably, the amount of m6A sites and the m6A levels in CR BMs was significantly lower than those in the AML BMs. Such a significant reduction in the m6A levels was also detected in AZA-treated HL-60 cells. Thirteen genes with decreased m6A and expression levels were identified, among which three genes (HPRT1, SNRPC, and ANP32B) were closely related to the prognosis of AML. Finally, we speculated the mechanism via which m6A modifications affected the mRNA stability of these three genes. In conclusion, we illustrated for the first time the global landscape of m6A levels in AZA plus VEN treated AML (CR) patients and revealed that AZA had a significant demethylation effect at the RNA level in AML patients. In addition, we identified new biomarkers for AZA plus VEN-treated AML via Nanopore sequencing technology in RNA epigenetics.

Massively parallel approaches for characterizing noncoding functional variation in human evolution

The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.

Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

Interpretation of molecular autopsy findings in 45 sudden unexplained death cases: from coding region to untranslated region

Sudden unexplained death (SUD) can affect apparently healthy adolescents and young adults with no prior clinical symptoms and no clear diagnostic findings at autopsy. Although primary cardiac arrhythmias have been shown to be the direct cause of death in the majority of SUD cases, the genetic predisposition contributing to SUD remains incompletely understood. Currently, molecular autopsy is considered to be an effective diagnostic tool in the multidisciplinary management of SUD, but the analysis focuses mainly on the coding region and the significance of many identified variants remains unclear. Recent studies have demonstrated the strong association between human disease and genetic variants in untranslated regions (UTRs), highlighting the potential role of UTR variants in the genetic predisposition to SUD. In this study, we searched for UTR variants with likely functional effects in the exome data of 45 SUD cases. Among 244 genes associated with cardiac diseases, three candidate variants with high confidence of pathogenicity were identified in the UTRs of SCO2, CALM2 and TBX3 based on a rigorous filtering strategy. A functional assay further validated the effect of these candidate variants on gene transcriptional activity. In addition, the constraint metrics, intolerance indexes, and dosage sensitivity scores of genes affected by the candidate variants were considered when estimating the consequence of aberrant gene expression. In conclusion, our study presents a practical strategy for UTR variant prioritization and functional annotation, which could improve the interpretation of molecular autopsy findings in SUD cohorts.

Alu insertion-mediated dsRNA structure formation with pre-existing Alu elements as a disease-causing mechanism

We previously identified a homozygous Alu insertion variant (Alu_Ins) in the 3'-untranslated region (3'-UTR) of SPINK1 as the cause of severe infantile isolated exocrine pancreatic insufficiency. Although we established that Alu_Ins leads to the complete loss of SPINK1 mRNA expression, the precise mechanisms remained elusive. Here, we aimed to elucidate these mechanisms through a hypothesis-driven approach. Initially, we speculated that, owing to its particular location, Alu_Ins could independently disrupt mRNA 3' end formation and/or affect other post-transcriptional processes such as nuclear export and translation. However, employing a 3'-UTR luciferase reporter assay, Alu_Ins was found to result in only an ∼50% reduction in luciferase activity compared to wild type, which is insufficient to account for the severe pancreatic deficiency in the Alu_Ins homozygote. We then postulated that double-stranded RNA (dsRNA) structures formed between Alu elements, an upstream mechanism regulating gene expression, might be responsible. Using RepeatMasker, we identified two Alu elements within SPINK1's third intron, both oriented oppositely to Alu_Ins. Through RNAfold predictions and full-length gene expression assays, we investigated orientation-dependent interactions between these Alu repeats. We provide compelling evidence to link the detrimental effect of Alu_Ins to extensive dsRNA structures formed between Alu_Ins and pre-existing intronic Alu sequences, including the restoration of SPINK1 mRNA expression by aligning all three Alu elements in the same orientation. Given the widespread presence of Alu elements in the human genome and the potential for new Alu insertions at almost any locus, our findings have important implications for detecting and interpreting Alu insertions in disease genes.

IGF2BP1-Mediated Regulation of CCN1 Expression by Specific Binding to G-Quadruplex Structure in Its 3'UTR

The intricate regulation of gene expression is fundamental to the biological complexity of higher organisms, and is primarily governed by transcriptional and post-transcriptional mechanisms. The 3'-untranslated region (3'UTR) of mRNA is rich in cis-regulatory elements like G-quadruplexes (G4s), and plays a crucial role in post-transcriptional regulation. G4s have emerged as significant gene regulators, impacting mRNA stability, translation, and localization. In this study, we investigate the role of a robust parallel G4 structure situated within the 3'UTR of CCN1 mRNA in post-transcriptional regulation. This G4 structure is proximal to the stop codon of human CCN1, and evolutionarily conserved. We elucidated its interaction with the insulin-like growth factor 2 binding protein 1 (IGF2BP1), a noncanonical RNA N6-methyladenosine (m6A) modification reader, revealing a novel interplay between RNA modifications and G-quadruplex structures. Knockdown experiments and mutagenesis studies demonstrate that IGF2BP1 binds specifically to the G4 structure, modulating CCN1 mRNA stability. Additionally, we unveil the role of IGF2BP1's RNA recognition motifs in G4 recognition, highlighting this enthalpically driven interaction. Our findings offer fresh perspectives on the complex mechanisms of post-transcriptional gene regulation mediated by G4 RNA secondary structures.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.

Sleep and diurnal alternative polyadenylation sites associated with human APA-linked brain disorders

Disruption of sleep and circadian rhythms are a comorbid feature of many pathologies, and can negatively influence many health conditions, including neurodegenerative disease, metabolic illness, cancer, and various neurological disorders. Genetic association studies linking sleep and circadian disturbances with disease susceptibility have mainly focused on changes in gene expression due to mutations, such as single-nucleotide polymorphisms. The interaction between sleep and/or circadian rhythms with the use of Alternative Polyadenylation (APA) has been largely undescribed, particularly in the context of other disorders. APA is a process that generates various transcript isoforms of the same gene affecting its mRNA translation, stability, localization, and subsequent function. Here we identified unique APAs expressed in rat brain over time-of-day, immediately following sleep deprivation, and the subsequent recovery period. From these data, we performed a secondary analysis of these sleep- or time-of-day associated PASs with recently described APA-linked human brain disorder susceptibility genes.

U-rich elements drive pervasive cryptic splicing in 3' UTR massively parallel reporter assays

Non-coding RNA sequences play essential roles in orchestrating gene expression. However, the sequence codes and mechanisms underpinning post-transcriptional regulation remain incompletely understood. Here, we revisit the finding from a prior massively parallel reporter assay (MPRA) that AU-rich (U-rich) elements in 3' untranslated regions (3' UTRs) can drive upregulation or downregulation of mRNA expression depending on 3' UTR context. We unexpectedly discover that this variable regulation arises from widespread cryptic splicing, predominately from an unannotated splice donor in the coding sequence of GFP to diverse acceptor sites in reporter 3' UTRs. Splicing is activated by U-rich sequences, which function as potent position-dependent regulators of 5' and 3' splice site choice and overall splicing efficiency. Splicing has diverse impacts on reporter expression, causing both increases and decreases in reporter expression via multiple mechanisms. We further provide evidence that cryptic splicing impacts between 10 to 50% of measurements made by other published 3' UTR MPRAs. Overall, our work emphasizes U-rich sequences as principal drivers of splicing and provides strategies to minimize cryptic splicing artifacts in reporter assays.

Identification, Design, and Application of Noncoding Cis-Regulatory Elements

Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.

Arabidopsis and maize terminator strength is determined by GC content, polyadenylation motifs and cleavage probability

The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.

Genetic variation mining of the Chinese mitten crab (Eriocheir sinensis) based on transcriptome data from public databases

At present, public databases house an extensive repository of transcriptome data, with the volume continuing to grow at an accelerated pace. Utilizing these data effectively is a shared interest within the scientific community. In this study, we introduced a novel strategy that harnesses SNPs and InDels identified from transcriptome data, combined with sample metadata from databases, to effectively screen for molecular markers correlated with traits. We utilized 228 transcriptome datasets of Eriocheir sinensis from the NCBI database and employed the Genome Analysis Toolkit software to identify 96 388 SNPs and 20 645 InDels. Employing the genome-wide association study analysis, in conjunction with the gender information from databases, we identified 3456 sex-biased SNPs and 639 sex-biased InDels. The KOG and KEGG annotations of the sex-biased SNPs and InDels revealed that these genes were primarily involved in the metabolic processes of E. sinensis. Combined with SnpEff annotation and PCR experimental validation, a highly sex-biased SNP located in the Kelch domain containing 4 (Klhdc4) gene, CHR67-6415071, was found to alter the splicing sites of Klhdc4, generating two splice variants, Klhdc4_a and Klhdc4_b. Additionally, Klhdc4 exhibited robust expression across the ovaries, testes, and accessory glands. The sex-biased SNPs and InDels identified in this study are conducive to the development of unisexual cultivation methods for E. sinensis, and the alternative splicing event caused by the sex-biased SNP in Klhdc4 may serve as a potential mechanism for sex regulation in E. sinensis. The analysis strategy employed in this study represents a new direction for the rational exploitation and utilization of transcriptome data in public databases.

SpRY-mediated screens facilitate functional dissection of non-coding sequences at single-base resolution

CRISPR mutagenesis screens conducted with SpCas9 and other nucleases have identified certain cis-regulatory elements and genetic variants but at a limited resolution due to the absence of protospacer adjacent motif (PAM) sequences. Here, leveraging the broad targeting scope of the near-PAMless SpRY variant, we have demonstrated that saturated SpRY mutagenesis and base editing screens can faithfully identify functional regulatory elements and essential genetic variants for target gene expression at single-base resolution. We further extended this methodology to investigate a genome-wide association study (GWAS) locus at 10q22.1 associated with a red blood cell trait, where we identified potential enhancers regulating HK1 gene expression, despite not all of these enhancers exhibiting typical chromatin signatures. More importantly, our saturated base editing screens pinpoint multiple causal variants within this locus that would otherwise be missed by Bayesian statistical fine-mapping. Our approach is generally applicable to functional interrogation of all non-coding genomic elements while complementing other high-coverage CRISPR screens.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.

CsPbBr3 Perovskite Quantum Dots Encapsulated by a Polymer Matrix for Ultrasensitive Dynamic Imaging of Intracellular MicroRNA

Herein, CsPbBr3 perovskite quantum dots (CPB PQDs)@poly(methyl methacrylate) (PMMA) (CPB@PMMA) nanospheres were used as energy donors with high Förster resonance energy transfer (FRET) efficiency and exceptional biocompatibility for ultrasensitive dynamic imaging of tiny amounts of microRNAs in living cells. Impressively, compared with traditional homogeneous single QDs as energy donors, CPB@PMMA obtained by encapsulating numerous CPB PQDs into PMMA as energy donors could not only significantly increase the efficiency of FRET via improving the local concentration of CPB PQDs but also distinctly avoid the problem of cytotoxicity caused by divulged heavy metal ions entering living cells. Most importantly, in the presence of target miRNA-21, DNA dendrimer-like nanostructures labeled with 6-carboxy-tetramethylrhodamine (TAMRA) were generated by the exposed tether interhybridization of the Y-shape structure, which could wrap around the surface of CPB@PMMA nanospheres to remarkably bridge the distance of FRET and increase the opportunity for effective energy transfer, resulting in excellent precision and accuracy for ultrasensitive and dynamic imaging of miRNAs. As proof of concept, the proposed strategy exhibited ultrahigh sensitivity with a detection limit of 45.3 aM and distinctly distinguished drug-irritative miRNA concentration abnormalities with living cells. Hence, the proposed enzyme-free CPB@PMMA biosensor provides convincing evidence for supplying accurate information, which could be expected to be a powerful tool for bioanalysis, diagnosis, and prognosis of human diseases.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder

Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets, we demonstrate that MpraVAE significantly improves the prediction performance for MPRA regulatory variants compared to the baseline method, conventional data augmentation approaches as well as existing variant scoring methods. Taking autoimmune diseases as one example, we apply MpraVAE to perform a genome-wide prediction of regulatory variants and find that predicted regulatory variants are more enriched than background variants in enhancers, active histone marks, open chromatin regions in immune-related cell types, and chromatin states associated with promoter, enhancer activity and binding sites of cMyC and Pol II that regulate gene expression. Importantly, predicted regulatory variants are found to link immune-related genes by leveraging chromatin loop and accessible chromatin, demonstrating the importance of MpraVAE in genetic and gene discovery for complex traits.

IL18 rs360717 and rs187238 genetic variants are associated with migraine diagnosis

BACKGROUND

Migraine is a genetically determined disorder that predisposes to recurrent episodes of headache. Interleukin (IL)-18 is a pro-inflammatory cytokine that seems to play a role in migraine pathophysiology, and its genetic variants could potentially impact susceptibility to migraine.

OBJECTIVE

To investigate the association between IL18 rs360717 and rs187238 genetic variants with migraine diagnosis and its clinical characteristics.

METHODS

A case-control study was conducted with 152 people with migraine and 155 healthy controls, matched by sex, age, ethnicity, and body mass index. Clinical characteristics of migraine, as well as validated questionnaires regarding disability and impact of migraine, presence of allodynia, anxiety, depression, and hyperacusis were collected. Genotyping for IL18 rs360717 and rs187238 variants was performed using real-time polymerase chain reaction (qPCR) and TaqMan™ method.

RESULTS

The IL18 rs360717A and rs187238G alleles were associated with increased chance of being diagnosed with migraine (OR = 1.53, 95%CI 1.05-2.24, p = 0.028 and OR = 1.46, 95%CI 1.00-2.14, p = 0.049, respectively). In the dominant model, the rs360717GA + AA genotypes were also associated with a higher chance of migraine than the GG genotype (OR = 1.69, 95%CI 1.05-2.73, p = 0.030). In women, in addition to the previous associations, there was also an effect of the variants on the chance of migraine in the codominant models and dominant models. Furthermore, among women, there was an influence on the prevalence of postdrome perception with rs360717GA + AA (OR = 3.04, 95%CI 1.10-8.42, p = 0.032) and rs187238CG + GG (OR = 2.97, 95%CI 1.08-8.21, p = 0.035).

CONCLUSION

IL18 rs360717 and rs187238 variants were associated with migraine diagnosis and postdrome symptoms, especially in women.

SIGNIFICANCE

This study has demonstrated that IL18 rs360717 and rs187238 variants play a role in migraine, influencing the chance of being diagnosed with migraine, particularly among women. There are prospects that IL18 variants could be considered potential genetic biomarkers for migraine.

Pumilio-1 mediated translational control of claudin-5 at the blood-brain barrier

Claudin-5 is one of the most essential tight junction proteins at the blood-brain barrier. A single nucleotide polymorphism rs10314 is located in the 3'-untranslated region of claudin-5 and has been shown to be a risk factor for schizophrenia. Here, we show that the pumilio RNA-binding protein, pumilio-1, is responsible for rs10314-mediated claudin-5 regulation. The RNA sequence surrounding rs10314 is highly homologous to the canonical pumilio-binding sequence and claudin-5 mRNA with rs10314 produces 25% less protein due to its inability to bind to pumilio-1. Pumilio-1 formed cytosolic granules under stress conditions and claudin-5 mRNA appeared to preferentially accumulate in these granules. Added to this, we observed granular pumilio-1 in endothelial cells in human brain tissues from patients with psychiatric disorders or epilepsy with increased/accumulated claudin-5 mRNA levels, suggesting translational claudin-5 suppression may occur in a brain-region specific manner. These findings identify a key regulator of claudin-5 translational processing and how its dysregulation may be associated with neurological and neuropsychiatric disorders.

Deciphering the molecular landscape of human peripheral nerves: implications for diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is a prevalent complication of diabetes mellitus that is caused by metabolic toxicity to peripheral axons. We aimed to gain deep mechanistic insight into the disease process using bulk and spatial RNA sequencing on tibial and sural nerves recovered from lower leg amputations in a mostly diabetic population. First, our approach comparing mixed sensory and motor tibial and purely sensory sural nerves shows key pathway differences in affected nerves, with distinct immunological features observed in sural nerves. Second, spatial transcriptomics analysis of sural nerves reveals substantial shifts in endothelial and immune cell types associated with severe axonal loss. We also find clear evidence of neuronal gene transcript changes, like PRPH, in nerves with axonal loss suggesting perturbed RNA transport into distal sensory axons. This motivated further investigation into neuronal mRNA localization in peripheral nerve axons generating clear evidence of robust localization of mRNAs such as SCN9A and TRPV1 in human sensory axons. Our work gives new insight into the altered cellular and transcriptomic profiles in human nerves in DPN and highlights the importance of sensory axon mRNA transport as an unappreciated potential contributor to peripheral nerve degeneration.

Decoding polygenic diseases: advances in noncoding variant prioritization and validation

Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.

Methods for Functional Characterization of Genetic Polymorphisms of Non-Coding Regulatory Regions of the Human Genome

Currently, numerous associations between genetic polymorphisms and various diseases have been characterized through the Genome-Wide Association Studies. Majority of the clinically significant polymorphisms are localized in non-coding regions of the genome. While modern bioinformatic resources make it possible to predict molecular mechanisms that explain influence of the non-coding polymorphisms on gene expression, such hypotheses require experimental verification. This review discusses the methods for elucidating molecular mechanisms underlying dependence of the disease pathogenesis on specific genetic variants within the non-coding sequences. A particular focus is on the methods for identification of transcription factors with binding efficiency dependent on polymorphic variations. Despite remarkable progress in bioinformatic resources enabling prediction of the impact of polymorphisms on the disease pathogenesis, there is still the need for experimental approaches to investigate this issue.

High-throughput assays to assess variant effects on disease

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms

Alternative splicing events are a major causal mechanism for complex traits, but they have been understudied due to the limitation of short-read sequencing. Here, we generate a full-length isoform annotation of human immune cells from an individual by long-read sequencing for 29 cell subsets. This contains a number of unannotated transcripts and isoforms such as a read-through transcript of TOMM40-APOE in the Alzheimer's disease locus. We profile characteristics of isoforms and show that repetitive elements significantly explain the diversity of unannotated isoforms, providing insight into the human genome evolution. In addition, some of the isoforms are expressed in a cell-type specific manner, whose alternative 3'-UTRs usage contributes to their specificity. Further, we identify disease-associated isoforms by isoform switch analysis and by integration of several quantitative trait loci analyses with genome-wide association study data. Our findings will promote the elucidation of the mechanism of complex diseases via alternative splicing.

Predicting functional UTR variants by integrating region-specific features

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

Non-coding DNA variants for risk in lupus

Systemic Lupus Erythematosus (SLE) is a multifactorial autoimmune disease that arises from a dynamic interplay between genetics and environmental triggers. The advent of sophisticated genomics technology has catalyzed a shift in our understanding of disease etiology, spotlighting the pivotal role of non-coding DNA variants in SLE pathogenesis. In this review, we present a comprehensive examination of the non-coding variants associated with SLE, shedding light on their role in influencing disease risk and progression. We discuss the latest methodological advancements that have been instrumental in the identification and functional characterization of these genomic elements, with a special focus on the transformative power of CRISPR-based gene-editing technologies. Additionally, the review probes into the therapeutic opportunities that arise from modulating non-coding regions associated with SLE. Through an exploration of the complex network of non-coding DNA, this review aspires to decode the genetic puzzle of SLE and set the stage for groundbreaking gene-based therapeutic interventions and the advancement of precision medicine strategies tailored to SLE management.

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants

Massively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results.

Comprehensive analysis of m6 A methylome and transcriptome by Nanopore sequencing in clear cell renal carcinoma

N6 -methyladenosine (m6 A) is the most prevalent epigenetic modification on eukaryotic messenger RNAs. Recent studies have focused on elucidating the key role of m6 A modification patterns in tumor progression. However, the relationship between m6 A and transcriptional regulation remains elusive. Nanopore technology enables the quantification of m6 A levels at each genomic site. In this study, a pair of tumor tissues and adjacent normal tissues from clear cell renal cell carcinoma (ccRCC) surgical samples were collected for Nanopore direct RNA sequencing. We identified 9644 genes displaying anomalous m6 A modifications, with 5343 genes upregulated and 4301 genes downregulated. Among these, 5224 genes were regarded as dysregulated genes, encompassing abnormal regulation of both m6 A modification and RNA expression. Gene Set Enrichment Analysis revealed an enrichment of these genes in pathways related to renal system progress and fatty acid metabolic progress. Furthermore, the χ2 test demonstrated a significant association between the levels of m6 A in dysregulated genes and their transcriptional expression levels. Additionally, we identified four obesity-associated genes (FTO, LEPR, ADIPOR2, and NPY5R) among the dysregulated genes. Further analyses using public databases revealed that these four genes were all related to the prognosis and diagnosis of ccRCC. This study introduced the novel approach of employing conjoint analysis of m6 A modification and RNA expression based on Nanopore sequencing to explore potential disease-related genes. Our work demonstrates the feasibility of the application of Nanopore sequencing technology in RNA epigenetic regulation research and identifies new potential therapeutic targets for ccRCC.

Dissecting the biology of feeding and eating disorders

Feeding and eating disorders (FEDs) are heterogenous and characterized by varying patterns of dysregulated eating and weight. Genome-wide association studies (GWASs) are clarifying their underlying biology and their genetic relationship to other psychiatric and metabolic/anthropometric traits. Genetic research on anorexia nervosa (AN) has identified eight significant loci and uncovered genetic correlations implicating both psychiatric and metabolic/anthropometric risk factors. Careful explication of these metabolic contributors may be key to developing effective and enduring treatments for devastating, life-altering, and frequently lethal illnesses. We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomics and propose a path that translates variants to genes, genes to pathways, and pathways to metabolic outcomes to advance the science and eventually treatment of FEDs.

Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows

BACKGROUND

Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM.

RESULTS

In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect.

CONCLUSION

Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.

The role of secondary structures in the functioning of 3' untranslated regions of mRNA: A review of functions of 3' UTRs' secondary structures and hypothetical involvement of secondary structures in cytoplasmic polyadenylation in Drosophila

3' untranslated regions (3' UTRs) of mRNAs have many functions, including mRNA processing and transport, translational regulation, and mRNA degradation and stability. These different functions require cis-elements in 3' UTRs that can be either sequence motifs or RNA structures. Here we review the role of secondary structures in the functioning of 3' UTRs and discuss some of the trans-acting factors that interact with these secondary structures in eukaryotic organisms. We propose potential participation of 3'-UTR secondary structures in cytoplasmic polyadenylation in the model organism Drosophila melanogaster. Because the secondary structures of 3' UTRs are essential for post-transcriptional regulation of gene expression, their disruption leads to a wide range of disorders, including cancer and cardiovascular diseases. Trans-acting factors, such as STAU1 and nucleolin, which interact with 3'-UTR secondary structures of target transcripts, influence the pathogenesis of neurodegenerative diseases and tumor metastasis, suggesting that they are possible therapeutic targets.

Structured 3' UTRs destabilize mRNAs in plants

BACKGROUND

RNA secondary structure (RSS) can influence the regulation of transcription, RNA processing, and protein synthesis, among other processes. 3' untranslated regions (3' UTRs) of mRNA also hold the key for many aspects of gene regulation. However, there are often contradictory results regarding the roles of RSS in 3' UTRs in gene expression in different organisms and/or contexts.

RESULTS

Here, we incidentally observe that the primary substrate of miR159a (pri-miR159a), when embedded in a 3' UTR, could promote mRNA accumulation. The enhanced expression is attributed to the earlier polyadenylation of the transcript within the hybrid pri-miR159a-3' UTR and, resultantly, a poorly structured 3' UTR. RNA decay assays indicate that poorly structured 3' UTRs could promote mRNA stability, whereas highly structured 3' UTRs destabilize mRNA in vivo. Genome-wide DMS-MaPseq also reveals the prevailing inverse relationship between 3' UTRs' RSS and transcript accumulation in the transcriptomes of Arabidopsis, rice, and even human. Mechanistically, transcripts with highly structured 3' UTRs are preferentially degraded by 3'-5' exoribonuclease SOV and 5'-3' exoribonuclease XRN4, leading to decreased expression in Arabidopsis. Finally, we engineer different structured 3' UTRs to an endogenous FT gene and alter the FT-regulated flowering time in Arabidopsis.

CONCLUSIONS

We conclude that highly structured 3' UTRs typically cause reduced accumulation of the harbored transcripts in Arabidopsis. This pattern extends to rice and even mammals. Furthermore, our study provides a new strategy of engineering the 3' UTRs' RSS to modify plant traits in agricultural production and mRNA stability in biotechnology.

Functional 3'-UTR Variants Identify Regulatory Mechanisms Impacting Alcohol Use Disorder and Related Traits

Although genome-wide association studies (GWAS) have identified loci associated with alcohol consumption and alcohol use disorder (AUD), they do not identify which variants are functional. To approach this, we evaluated the impact of variants in 3' untranslated regions (3'-UTRs) of genes in loci associated with substance use and neurological disorders using a massively parallel reporter assay (MPRA) in neuroblastoma and microglia cells. Functionally impactful variants explained a higher proportion of heritability of alcohol traits than non-functional variants. We identified genes whose 3'UTR activities are associated with AUD and alcohol consumption by combining variant effects from MPRA with GWAS results. We examined their effects by evaluating gene expression after CRISPR inhibition of neuronal cells and stratifying brain tissue samples by MPRA-derived 3'-UTR activity. A pathway analysis of differentially expressed genes identified inflammation response pathways. These analyses suggest that variation in response to inflammation contributes to the propensity to increase alcohol consumption.

Regulatory features aid interpretation of 3'UTR variants

Our ability to determine the clinical impact of variants in 3' untranslated regions (UTRs) of genes remains poor. We provide a thorough analysis of 3' UTR variants from several datasets. Variants in putative regulatory elements, including RNA-binding protein motifs, eCLIP peaks, and microRNA sites, are up to 16 times more likely than variants not in these elements to have gene expression and phenotype associations. Variants in regulatory motifs result in allele-specific protein binding in cell lines and allele-specific gene expression differences in population studies. In addition, variants in shared regions of alternatively polyadenylated isoforms and those proximal to polyA sites are more likely to affect gene expression and phenotype. Finally, pathogenic 3' UTR variants in ClinVar are up to 20 times more likely than benign variants to fall in a regulatory site. We incorporated these findings into RegVar, a software tool that interprets regulatory elements and annotations for any 3' UTR variant and predicts whether the variant is likely to affect gene expression or phenotype. This tool will help prioritize variants for experimental studies and identify pathogenic variants in individuals.

Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows

Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.

Identification of sleep and circadian alternative polyadenylation sites associated with APA-linked human brain disorders

Sleep and circadian rhythm disruptions are comorbid features of many pathologies and can negatively influence numerous health conditions, including degenerative diseases, metabolic illnesses, cancer, and various neurological disorders. Genetic association studies linking sleep and circadian disturbances with disease susceptibility have mainly focused on changes in gene expression due to mutations, such as single-nucleotide polymorphisms. Thus, associations between sleep and/or circadian rhythm and alternative polyadenylation (APA), particularly in the context of other health challenges, are largely undescribed. APA is a process that generates various transcript isoforms from the same gene, resulting in effects on mRNA translation, stability, localization, and subsequent function. Here, we have identified unique APAs in rat brain that exhibit time-of-day-dependent oscillations in expression as well as APAs that are altered by sleep deprivation and the subsequent recovery period. Genes affected by APA usage include Mapt/Tau, Ntrk2, Homer1A, Sin3band Sorl. Sorl1 has two APAs which cycle with a 24 h period, one additional APA cycles with a 12 h period and one more that is reduced during recovery sleep. Finally, we compared sleep- or circadian-associated APAs with recently described APA-linked brain disorder susceptibility genes and found 46 genes in common.

Arabidopsis and Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability

The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection

To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3'/5')-degradation of rRNA, mRNA and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.

miRNA-381 regulates renal cancer stem cell properties and sunitinib resistance via targeting SOX4

Cancer stem cells (CSCs) are crucial in the pathogenesis of human cancers. Existing studies reported that microRNA (miRNA) modulates the stemness of CSCs. We discovered that renal cell CSCs have suppressed miR-381. Suppression of miR-381 promotes renal cell tumorigenesis and CSC-like properties. Furthermore, the forced expression of miR-381 prevents the renal cell tumorigenesis and CSC-like properties. Mechanistically, renal cell CSCs have been found to interact with SOX4 through miR-381 directly. miR-381 inhibits renal cell CSC-like properties and tumorigenesis via downregulating SOX4. Examination of the patient-derived xenografts (PDX) and patient cohorts reveals that miR-381 may be able to forecast the advantages of Sunitinib in RCC patients. Moreover, the introduction of SOX4 could reverse the sensitivity of miR-381 overexpression RCC cells to Sunitinib-induced cell apoptosis. These results indicated that miR-381 is critical in renal cell CSC-like properties and tumorigenesis, making it the ideal therapeutic target for RCC.

Design and deep learning of synthetic B-cell-specific promoters

Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.

A Cre-dependent massively parallel reporter assay allows for cell-type specific assessment of the functional effects of non-coding elements in vivo

The function of regulatory elements is highly dependent on the cellular context, and thus for understanding the function of elements associated with psychiatric diseases these would ideally be studied in neurons in a living brain. Massively Parallel Reporter Assays (MPRAs) are molecular genetic tools that enable functional screening of hundreds of predefined sequences in a single experiment. These assays have not yet been adapted to query specific cell types in vivo in a complex tissue like the mouse brain. Here, using a test-case 3'UTR MPRA library with genomic elements containing variants from autism patients, we developed a method to achieve reproducible measurements of element effects in vivo in a cell type-specific manner, using excitatory cortical neurons and striatal medium spiny neurons as test cases. This targeted technique should enable robust, functional annotation of genetic elements in the cellular contexts most relevant to psychiatric disease.

Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants

Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights regarding the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, and their target genes remain largely unknown. We implemented a massively parallel variant annotation pipeline (MVAP) to perform SCZ variant-to-function mapping at scale in disease-relevant neural cell types. This approach identified 620 functional variants (1.7%) that operate in a highly developmental context and neuronal-activity-dependent manner. Multimodal integration of epigenomic and CRISPRi screening data enabled us to link these functional variants to target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a multistage prioritization strategy to map functional single-nucleotide polymorphism (SNP)-to-gene-to-endophenotype relations and offer biological insights into the context-dependent molecular processes modulated by SCZ-associated genetic variation.

Members of the CUGBP Elav-like family of RNA-binding proteins are expressed in distinct populations of primary sensory neurons

Primary sensory dorsal root ganglia (DRG) neurons are diverse, with distinct populations that respond to specific stimuli. Previously, we observed that functionally distinct populations of DRG neurons express mRNA transcript variants with different 3' untranslated regions (3'UTRs). 3'UTRs harbor binding sites for interaction with RNA-binding proteins (RBPs) for transporting mRNAs to subcellular domains, modulating transcript stability, and regulating the rate of translation. In the current study, analysis of publicly available single-cell RNA-sequencing data generated from adult mice revealed that 17 3'UTR-binding RBPs were enriched in specific populations of DRG neurons. This included four members of the CUG triplet repeat (CUGBP) Elav-like family (CELF): CELF2 and CELF4 were enriched in peptidergic, CELF6 in both peptidergic and nonpeptidergic, and CELF3 in tyrosine hydroxylase-expressing neurons. Immunofluorescence studies confirmed that 60% of CELF4+ neurons are small-diameter C fibers and 33% medium-diameter myelinated (likely Aδ) fibers and showed that CELF4 is distributed to peripheral termini. Coexpression analyses using transcriptomic data and immunofluorescence revealed that CELF4 is enriched in nociceptive neurons that express GFRA3, CGRP, and the capsaicin receptor TRPV1. Reanalysis of published transcriptomic data from macaque DRG revealed a highly similar distribution of CELF members, and reanalysis of single-nucleus RNA-sequencing data derived from mouse and rat DRG after sciatic injury revealed differential expression of CELFs in specific populations of sensory neurons. We propose that CELF RBPs may regulate the fate of mRNAs in populations of nociceptors, and may play a role in pain and/or neuronal regeneration following nerve injury.