Non-coding Regions Of Genome Research Articles

A Cautionary Tale: What if Stealth Adapted Viruses Incorporate the Covid-19 Spike Protein Coding mRNA into Their Genome?

Stealth adaptation is a generic immune evasion mechanism, which can potentially occur with all viruses [1-4]. It involves the deletion or mutation of the genes coding for the relatively few virus components typically targeted by the cellular immune system. A further characteristic of certain stealth adapted viruses is the incorporation of additional genetic sequences of cellular and bacterial origins [5-8]. These “renegade” genetic sequences can be subsequently transmitted between humans as components of infectious stealth adapted viruses. The best characterized stealth adapted virus, referred to in GenBank as stealth virus 1, is a derivative of an African green monkey simian cytomegalovirus (SCMV) [2,4,9]. It was repeatedly cultured from a patient with the chronic fatigue syndrome (CFS) [10]. A similar virus (stealth virus-2) was isolated from the cerebrospinal fluid of a comatose patient with a 4-year history of a bipolar psychosis [11]. Stealth virus-1 was cloned and the complete or partial DNA sequences of 248 clones were submitted to GenBank. Of these clones, 200 of the sequences correspond to regions of the SCMV genome. The sequences are unevenly distributed over the genome of the originating SCMV, with clear evidence of major deletions and genetic instability. Fourteen clones contained genetic sequences that were derived from non-coding regions of the human genome. These incorporated cellular sequences have also undergone genetic changes indicative of the genetically unstable stealth adapted virus genome [12].

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area.

Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000–3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.

Cell Type-Specific Annotation and Fine Mapping of Variants Associated With Brain Disorders.

Common genetic variants confer susceptibility to a large number of complex brain disorders. Given that such variants predominantly localize in non-coding regions of the human genome, there is a significant challenge to predict and characterize their functional consequences. More importantly, most available computational methods, generally defined as context-free methods, output prediction scores regarding the functionality of genetic variants irrespective of the context, i.e., the tissue or cell-type affected by a disease, limiting the ability to predict the functional consequences of common variants on brain disorders. In this study, we introduce a comparative multi-step pipeline to investigate the relative effectiveness of context-specific and context-free approaches to prioritize disease causal variants. As an experimental case, we focused on schizophrenia (SCZ), a debilitating neuropsychiatric disease for which a large number of susceptibility variants is identified from genome-wide association studies. We tested over two dozen available methods and examined potential associations between the cell/tissue-specific mapping scores and open chromatin accessibility, and provided a prioritized map of SCZ risk loci for in vitro or in-vivo functional analysis. We found extensive differences between context-free and tissue-specific approaches and showed how they may play complementary roles. As a proof of concept, we found a few sets of genes, through a consensus mapping of both categories, including FURIN to be among the top hits. We showed that the genetic variants in this gene and related genes collectively dysregulate gene expression patterns in stem cell-derived neurons and characterize SCZ phenotypic manifestations, while genes which were not shared among highly prioritized candidates in both approaches did not demonstrate such characteristics. In conclusion, by combining context-free and tissue-specific predictions, our pipeline enables prioritization of the most likely disease-causal common variants in complex brain disorders.

CRISPR/Cas9-mediated knock-in of alligator cathelicidin gene in a non-coding region of channel catfish genome

CRISPR/Cas9-based gene knockout in animal cells, particularly in teleosts, has proven to be very efficient with regards to mutation rates, but the precise insertion of exogenous DNA or gene knock-in via the homology-directed repair (HDR) pathway has seldom been achieved outside of the model organisms. Here, we succeeded in integrating with high efficiency an exogenous alligator cathelicidin gene into a targeted non-coding region of channel catfish (Ictalurus punctatus) chromosome 1 using two different donor templates (synthesized linear dsDNA and cloned plasmid DNA constructs). We also tested two different promoters for driving the gene, zebrafish ubiquitin promoter and common carp β-actin promoter, harboring a 250-bp homologous region flanking both sides of the genomic target locus. Integration rates were found higher in dead fry than in live fingerlings, indicating either off-target effects or pleiotropic effects. Furthermore, low levels of mosaicism were detected in the tissues of P1 individuals harboring the transgene, and high transgene expression was observed in the blood of some P1 fish. This can be an indication of the localization of cathelicidin in neutrophils and macrophage granules as also observed in most antimicrobial peptides. This study marks the first use of CRISPR/Cas9 HDR for gene integration in channel catfish and may contribute to the generation of a more efficient system for precise gene integration in catfish and other aquaculture species, and the development of gene-edited, disease-resistant fish.

Towards the structuredness of non-coding regions of chloroplast genomes

A triplet frequency dictionary is stipulated to be a structure of non-coding DNA fragments of chloroplast genomes. We retrieved the ensembles of the non-coding regions from 391 terrestrial plants and studied the distribution of the fragments in the Euclidean metric space. An interplay between the cluster composition of the set of the dictionaries and the taxonomy of the genome bearers was analyzed.

The Effects of Single Nucleotide Polymorphisms in Cancer RNAi Therapies.

Simple SummaryDespite the recent progress in RNAi delivery of siRNA-based therapeutics for cancer therapy, the presence of single nucleotide polymorphisms (SNPs) in the general population could dramatically reduce the effectiveness of RNAi therapy. Their ubiquitous presence can also lead to unpredictable and adverse side effects. Because both SNPs and somatic mosaicisms have also been implicated in a number of human diseases including cancer, however, these specific changes offer the ability to selectively and efficiently target cancer cells. Here, we discuss how SNPs influence the development and success of novel anticancer RNAi therapies.Tremendous progress in RNAi delivery methods and design has allowed for the effective development of siRNA-based therapeutics that are currently under clinical investigation for various cancer treatments. This approach has the potential to revolutionize cancer therapy by providing the ability to specifically downregulate or upregulate the mRNA of any protein of interest. This exquisite specificity, unfortunately, also has a downside. Genetic variations in the human population are common because of the presence of single nucleotide polymorphisms (SNPs). SNPs lead to synonymous and non-synonymous changes and they occur once in every 300 base pairs in both coding and non-coding regions in the human genome. Much less common are the somatic mosaicism variations associated with genetically distinct populations of cells within an individual that is derived from postzygotic mutations. These heterogeneities in the population can affect the RNAi’s efficacy or more problematically, which can lead to unpredictable and sometimes adverse side effects. From a more positive viewpoint, both SNPs and somatic mosaicisms have also been implicated in human diseases, including cancer, and these specific changes could offer the ability to effectively and, more importantly, selectively target the cancer cells. In this review, we discuss how SNPs in the human population can influence the development and success of novel anticancer RNAi therapies and the importance of why SNPs should be carefully considered.

Efficient replacement of long DNA fragments via non-homologous end joining at non-coding regions.

Genomic DNA replacement for achieving sophisticated genetic manipulation is implemented currently through homogenous recombination/homology-dependent repair (HR/HDR). Here we report a novel DNA fragment replacement method that is mediated by non-homologous end joining (NHEJ)-dependent DNA repair at two sites of CRISPR/Cas9-induced double-strand breaks at non-coding genomic regions flanking the exons of targeted genes. We demonstrated this method by generating three conditional alleles of zebrafish. Functional assays proved that the genomic sequence between two inserted loxP sites was deleted by the Cre recombinase, and the phenotype after Cre-induced excision was comparable to previously reported mutants or morphants. We further extended the application by making two EGFP reporter lines, in which the EGFP expression patterns were consistent with those of endogenous genes. Furthermore, combining double-fluorescence expression donor vectors, we showed that the DNA replacement efficiency of this NHEJ-mediated approach was around 3 times larger than that of HR/HDR-mediated approach. Together, we provides a feasible strategy for genomic DNA replacement in zebrafish, which can be applicable for other organisms as well.

Homozygous deletions implicate non-coding epigenetic marks in Autism spectrum disorder

More than 98% of the human genome is made up of non-coding DNA, but techniques to ascertain its contribution to human disease have lagged far behind our understanding of protein coding variations. Autism spectrum disorder (ASD) has been mostly associated with coding variations via de novo single nucleotide variants (SNVs), recessive/homozygous SNVs, or de novo copy number variants (CNVs); however, most ASD cases continue to lack a genetic diagnosis. We analyzed 187 consanguineous ASD families for biallelic CNVs. Recessive deletions were significantly enriched in affected individuals relative to their unaffected siblings (17% versus 4%, p < 0.001). Only a small subset of biallelic deletions were predicted to result in coding exon disruption. In contrast, biallelic deletions in individuals with ASD were enriched for overlap with regulatory regions, with 23/28 CNVs disrupting histone peaks in ENCODE (p < 0.009). Overlap with regulatory regions was further demonstrated by comparisons to the 127-epigenome dataset released by the Roadmap Epigenomics project, with enrichment for enhancers found in primary brain tissue and neuronal progenitor cells. Our results suggest a novel noncoding mechanism of ASD, describe a powerful method to identify important noncoding regions in the human genome, and emphasize the potential significance of gene activation and regulation in cognitive and social function.

MutEnricher: a flexible toolset for somatic mutation enrichment analysis of tumor whole genomes

BackgroundAnalysis of somatic mutations from tumor whole exomes has fueled discovery of novel cancer driver genes. However, ~ 98% of the genome is non-coding and includes regulatory elements whose normal cellular functions can be disrupted by mutation. Whole genome sequencing (WGS), on the other hand, allows for identification of non-coding somatic variation and expanded estimation of background mutation rates, yet fewer computational tools exist for specific interrogation of this space.ResultsWe present MutEnricher, a flexible toolset for investigating somatic mutation enrichment in both coding and non-coding genomic regions from WGS data. MutEnricher contains two distinct modules for these purposes that provide customizable options for calculating sample- and feature-specific background mutation rates. Additionally, both MutEnricher modules calculate feature-level and local, or “hotspot,” somatic mutation enrichment statistics.ConclusionsMutEnricher is a flexible software package for investigating somatic mutation enrichment that is implemented in Python, is freely available, can be efficiently parallelized, and is highly configurable to researcher's specific needs. MutEnricher is available online at https://github.com/asoltis/MutEnricher.

ABC-GWAS: Functional Annotation of Estrogen Receptor-Positive Breast Cancer Genetic Variants.

Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created “Analysis of Breast Cancer GWAS” (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.

De novo evolved gene product NCYM in the pathogenesis and clinical outcome of human neuroblastomas and other cancers

NCYM is an antisense transcript of MYCN oncogene and promotes tumor progression. NCYM encodes a de novo protein whose open reading frame evolved from noncoding genomic regions in the ancestor of Homininae. Because of its topology, NCYM is always co-amplified with MYCN oncogene, and the mutual regulations between NCYM and MYCN maintain their expressions at high levels in MYCN-amplified tumors. NCYM stabilizes MYCN by inhibiting GSK3β, whereas MYCN stimulates transcription of both NCYM and MYCN. NCYM mRNA and its noncoding transcript variants MYCNOS have been shown to stimulate MYCN expression via direct binding to MYCN promoter, indicating that both coding and noncoding transcripts of NCYM induce MYCN expression. In contrast to the noncoding functions of NCYM, NCYM protein also promotes calpain-mediated cleavage of c-MYC. The cleaved product called Myc-nick inhibits cell death and promotes cancer cell migration. Furthermore, NCYM-mediated inhibition of GSK3β results in the stabilization of β-catenin, which promotes aggressiveness of bladder cancers. These MYCN-independent functions of NCYM showed their clinical significance in MYCN-non-amplified tumors, including adult tumors. This year is the 30th anniversary of the identification of NCYM/MYCNOS gene. On this special occasion, we summarize the current understanding of molecular functions and the clinical significance of NCYM and discuss future directions to achieve therapeutic strategies targeting NCYM.

Phylogeography and Genetic Diversity in a Southern North American Desert: Agave kerchovei From the Tehuacán-Cuicatlán Valley, Mexico.

The Tehuacán-Cuicatlán Valley, located at the southeast of the state of Puebla and the northeast of the state of Oaxaca in Central Mexico, south of the Trans-Mexican Volcanic Belt (TMVB), is of particular interest for understanding the evolutionary dynamics of arid and semi-arid environments, being one of the main reservoirs of biological diversity for the arid zones of North America, including the highest diversity of Agavaceae worldwide and high levels of endemism. Studying in detail the phylogeography, environmental history and population genetics of representative species will hopefully shed light on the evolutionary and ecological dynamics that generated the tremendous biodiversity and endemism of this important region in Mexico. We sequenced three non-coding regions of chloroplast genome of Agave kerchovei, a representative species of the Tehuacán Valley, generating 2,188 bp from 128 individuals sampled from eight populations throughout the species range. We used this data set to (i) characterize the levels of genetic diversity and genetic structure in A. kerchovei; (ii) predict the distribution of A. kerchovei for the present day, and to reconstruct the past geographical history of the species by constructing ecological niche models (ENM); and (iii) compare the levels of diversity in this species with those estimated for the widely distributed Agave lechuguilla. Agave kerchovei has high levels of total chloroplast genetic variation (Hd = 0.718), especially considering that it is a species with a very restricted distribution. However, intrapopulation diversity is low (zero in some populations), and genetic structure is high (FST = 0.928, GST = 0.824), which can be expected for endemic species with isolated populations. Our data suggest that Pleistocene glacial cycles have played an important role in the distribution of A. kerchovei, where the climatic variability of the region – likely associated with its topographic complexity – had a significant effect on the levels of genetic diversity and population dynamics, while the potential distribution of the species seems to be stable since the middle Holocene (6 kya). We conclude that in A. kerchovei there is a core group of populations in the Tehuacán Valley, and peripheric populations that appear to be evolving independently and thus the species is fundamentally an endemic species from the Tehuacán Valley while the populations outside the Valley appear to be in the process of incipient speciation.

Comparative analyses of 32 complete plastomes of Tef (Eragrostis tef ) accessions from Ethiopia: phylogenetic relationships and mutational hotspots.

Eragrostis tef is an important cereal crop in Ethiopia with excellent storage properties, high–quality food, and the unique ability to thrive in extreme environmental conditions. However, the application of advanced molecular tools for breeding and conservation of these species is extremely limited. Therefore, developing chloroplast genome resources and high-resolution molecular markers are valuable to E. tef population and biogeographic studies. In the current study, we assembled and compared the complete plastomes of 32 E. tef accessions. The size of the plastomes ranged from 134,349 to 134,437 bp with similar GC content (∼38.3%). Genomes annotations revealed 112 individual genes, including 77 protein-coding, 31 tRNA, and 4 rRNA genes. Comparison of E. tef plastomes revealed a low degree of intraspecific sequence variations and no structural differentiations. Furthermore, we found 34 polymorphic sites (13 cpSSRs, 12 InDels, and 9 SNPs) that can be used as valuable DNA barcodes. Among them, the majority (88%) of the polymorphic sites were identified in the noncoding genomic regions. Nonsynonymous (ka) and synonymous (ks) substitution analysis showed that all PCGs were under purifying selection (ka/ks <1). The phylogenetic analyses of the whole plastomes and polymorphic region sequences were able to distinguish the accession from the southern population, indicating its potential to be used as a super-barcode. In conclusion, the newly generated plastomes and polymorphic markers developed here could be a useful genomic resource in molecular breeding, population genetics and the biogeographical study of E. tef.

Exploring human genomic diversity with gnomAD.

A collection of seven articles from the gnomAD consortium, published in Nature, Nature Medicine and Nature Communications, showcases analyses of global human genetic variation in coding and non-coding genomic regions across this data set.

Applications of probability and statistics in cancer genomics

BackgroundThe past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression. Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine.ResultsIn this review we introduce cancer genomics from a probabilistic and statistical perspective. We start from (1) functional classification of genes into oncogenes and tumor suppressor genes, then (2) demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes, followed by (3) tumor purity analysis, which in turn leads to (4) the concept of ploidy and clonality, that is next connected to (5) tumor evolution under treatment pressure, which yields insights into cancer drug resistance. We also discuss future challenges including the non‐coding genomic regions, integrative analysis of genomics and epigenomics, as well as early cancer detection.ConclusionWe believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field of cancer genomics and personalized medicine.

Positive epistasis between viral polymerase and the 3′ untranslated region of its genome reveals the epidemiologic fitness of dengue virus

Dengue virus (DENV) is a global health threat, causing repeated epidemics throughout the tropical world. While low herd immunity levels to any one of the four antigenic types of DENV predispose populations to outbreaks, viral genetic determinants that confer greater fitness for epidemic spread is an important but poorly understood contributor of dengue outbreaks. Here we report that positive epistasis between the coding and noncoding regions of the viral genome combined to elicit an epidemiologic fitness phenotype associated with the 1994 DENV2 outbreak in Puerto Rico. We found that five amino acid substitutions in the NS5 protein reduced viral genomic RNA (gRNA) replication rate to achieve a more favorable and relatively more abundant subgenomic flavivirus RNA (sfRNA), a byproduct of host 5'-3' exoribonuclease activity. The resulting increase in sfRNA relative to gRNA levels not only inhibited type I interferon (IFN) expression in infected cells through a previously described mechanism, but also enabled sfRNA to compete with gRNA for packaging into infectious particles. We suggest that delivery of sfRNA to new susceptible cells to inhibit type I IFN induction before gRNA replication and without the need for further de novo sfRNA synthesis could form a "preemptive strike" strategy against DENV.

ParSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.

BackgroundSeveral prediction problems in computational biology and genomic medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: thus, the prediction of deleterious variants is a challenging, highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data.ResultsTo overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemble approach and oversampling and undersampling techniques to deal with imbalanced data, and parallel computational techniques to both manage big genomic data and substantially speed up the computation. The synergy between Bayesian optimization techniques and the parallel nature of parSMURF enables efficient and user-friendly automatic tuning of the hyper-parameters of the algorithm, and allows specific learning problems in genomic medicine to be easily fit. Moreover, by using MPI parallel and machine learning ensemble techniques, parSMURF can manage big data by partitioning them across the nodes of a high-performance computing cluster. Results with synthetic data and with single-nucleotide variants associated with Mendelian diseases and with genome-wide association study hits in the non-coding regions of the human genome, involhing millions of examples, show that parSMURF achieves state-of-the-art results and an 80-fold speed-up with respect to the sequential version.ConclusionsparSMURF is a parallel machine learning tool that can be trained to learn different genomic problems, and its multiple levels of parallelization and high scalability allow us to efficiently fit problems characterized by big and imbalanced genomic data. The C++ OpenMP multi-core version tailored to a single workstation and the C++ MPI/OpenMP hybrid multi-core and multi-node parSMURF version tailored to a High Performance Computing cluster are both available at https://github.com/AnacletoLAB/parSMURF.

CScape-somatic: distinguishing driver and passenger point mutations in the cancer genome.

MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.

The T1D-associated lncRNA Lnc13 modulates human pancreatic β cell inflammation by allele-specific stabilization of STAT1 mRNA

The vast majority of type 1 diabetes (T1D) genetic association signals lie in noncoding regions of the human genome. Many have been predicted to affect the expression and secondary structure of long noncoding RNAs (lncRNAs), but the contribution of these lncRNAs to the pathogenesis of T1D remains to be clarified. Here, we performed a complete functional characterization of a lncRNA that harbors a single nucleotide polymorphism (SNP) associated with T1D, namely, Lnc13 Human pancreatic islets harboring the T1D-associated SNP risk genotype in Lnc13 (rs917997*CC) showed higher STAT1 expression than islets harboring the heterozygous genotype (rs917997*CT). Up-regulation of Lnc13 in pancreatic β-cells increased activation of the proinflammatory STAT1 pathway, which correlated with increased production of chemokines in an allele-specific manner. In a mirror image, Lnc13 gene disruption in β-cells partially counteracts polyinosinic-polycytidylic acid (PIC)-induced STAT1 and proinflammatory chemokine expression. Furthermore, we observed that PIC, a viral mimetic, induces Lnc13 translocation from the nucleus to the cytoplasm promoting the interaction of STAT1 mRNA with (poly[rC] binding protein 2) (PCBP2). Interestingly, Lnc13-PCBP2 interaction regulates the stability of the STAT1 mRNA, sustaining inflammation in β-cells in an allele-specific manner. Our results show that the T1D-associated Lnc13 may contribute to the pathogenesis of T1D by increasing pancreatic β-cell inflammation. These findings provide information on the molecular mechanisms by which disease-associated SNPs in lncRNAs influence disease pathogenesis and open the door to the development of diagnostic and therapeutic approaches based on lncRNA targeting.

Structural variations in a non-coding region at 1q32.1 are responsible for the NYS7 locus in two large families

Congenital motor nystagmus (CMN) is characterized by early-onset bilateral ocular oscillations without other ocular deficits. To date, mutations in only one gene have been identified to be responsible for CMN, i.e., FRMD7 for X-linked CMN. Four loci for autosomal dominant CMN, including NYS7 (OMIM 614826), have been mapped but the causative genes have yet to be identified. NYS7 was mapped to 1q32.1 based on independent genome-wide linkage scan on two large families with CMN. In this study, mutations in all known protein-coding genes, both intronic sequence with predicted effect and coding sequence, in the linkage interval were excluded by whole-genome sequencing. Then, long-read genome sequencing based on the Nanopore platform was performed with a sample from each of the two families. Two deletions with an overlapping region of 775,699 bp, located in a region without any known protein-coding genes, were identified in the two families in the linkage region. The two deletions as well as their breakpoints were confirmed by Sanger sequencing and co-segregated with CMN in the two families. The 775,699 bp deleted region contains uncharacterized non-protein-coding expressed sequences and pseudogenes but no protein-coding genes. However, Hi-C data predicted that the deletions span two topologically associated domains and probably lead to a change in the 3D genomic architecture. These results provide novel evidence of a strong association between structural variations in non-coding genomic regions and human hereditary diseases like CMN with a potential mechanism involving changes in 3D genome architecture, which provides clues regarding the molecular pathogenicity of CMN.