HGG Advances最新文献

{"title":"Clinical and functional studies of MTOR variants in Smith-Kingsmore syndrome reveal deficits of circadian rhythm and sleep-wake behavior.","authors":"Andrew C Liu, Yang Shen, Carolyn R Serbinski, Hongzhi He, Destino Roman, Mehari Endale, Lindsey Aschbacher-Smith, Katherine A King, Jorge L Granadillo, Isabel López, Darcy A Krueger, Thomas J Dye, David F Smith, John B Hogenesch, Carlos E Prada","doi":"10.1016/j.xhgg.2024.100333","DOIUrl":"10.1016/j.xhgg.2024.100333","url":null,"abstract":"<p><p>Heterozygous de novo or inherited gain-of-function mutations in the MTOR gene cause Smith-Kingsmore syndrome (SKS). SKS is a rare autosomal dominant condition, and individuals with SKS display macrocephaly/megalencephaly, developmental delay, intellectual disability, and seizures. A few dozen individuals are reported in the literature. Here, we report a cohort of 28 individuals with SKS that represent nine MTOR pathogenic variants. We conducted a detailed natural history study and found pathophysiological deficits among individuals with SKS in addition to the common neurodevelopmental symptoms. These symptoms include sleep-wake disturbance, hyperphagia, and hyperactivity, indicative of homeostatic imbalance. To characterize these variants, we developed cell models and characterized their functional consequences. We showed that these SKS variants display a range of mechanistic target of rapamycin (mTOR) activities and respond to the mTOR inhibitor, rapamycin, differently. For example, the R1480_C1483del variant we identified here and the previously known C1483F are more active than wild-type controls and less responsive to rapamycin. Further, we showed that SKS mutations dampened circadian rhythms and low-dose rapamycin improved the rhythm amplitude, suggesting that optimal mTOR activity is required for normal circadian function. As SKS is caused by gain-of-function mutations in MTOR, rapamycin was used to treat several patients. While higher doses of rapamycin caused delayed sleep-wake phase disorder in a subset of patients, optimized lower doses improved sleep. Our study expands the clinical and molecular spectrum of SKS and supports further studies for mechanism-guided treatment options to improve sleep-wake behavior and overall health.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100333"},"PeriodicalIF":3.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biallelic NDC1 variants that interfere with ALADIN binding are associated with neuropathy and triple A-like syndrome.","authors":"Daphne J Smits, Jordy Dekker, Hannie Douben, Rachel Schot, Helen Magee, Somayeh Bakhtiari, Katrin Koehler, Angela Huebner, Markus Schuelke, Hossein Darvish, Shohreh Vosoogh, Abbas Tafakhori, Melika Jameie, Ehsan Taghiabadi, Yana Wilson, Margit Shah, Marjon A van Slegtenhorst, Evita G Medici-van den Herik, Tjakko J van Ham, Michael C Kruer, Grazia M S Mancini","doi":"10.1016/j.xhgg.2024.100327","DOIUrl":"10.1016/j.xhgg.2024.100327","url":null,"abstract":"<p><p>Nuclear pore complexes (NPCs) regulate nucleocytoplasmic transport and are anchored in the nuclear envelope by the transmembrane nucleoporin NDC1. NDC1 is essential for post-mitotic NPC assembly and the recruitment of ALADIN to the nuclear envelope. While no human disorder has been associated to one of the three transmembrane nucleoporins, biallelic variants in AAAS, encoding ALADIN, cause triple A syndrome (Allgrove syndrome). Triple A syndrome, characterized by alacrima, achalasia, and adrenal insufficiency, often includes progressive demyelinating polyneuropathy and other neurological complaints. In this report, diagnostic exome and/or RNA sequencing was performed in seven individuals from four unrelated consanguineous families with AAAS-negative triple A syndrome. Molecular and clinical studies followed to elucidate the pathogenic mechanism. The affected individuals presented with intellectual disability, motor impairment, severe demyelinating with secondary axonal polyneuropathy, alacrima, and achalasia. None of the affected individuals has adrenal insufficiency. All individuals presented with biallelic NDC1 in-frame deletions or missense variants that affect amino acids and protein domains required for ALADIN binding. No other significant variants associated with the phenotypic features were reported. Skin fibroblasts derived from affected individuals show decreased recruitment of ALADIN to the NE and decreased post-mitotic NPC insertion, confirming pathogenicity of the variants. Taken together, our results implicate biallelic NDC1 variants in the pathogenesis of polyneuropathy and a triple A-like disorder without adrenal insufficiency, by interfering with physiological NDC1 functions, including the recruitment of ALADIN to the NPC.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100327"},"PeriodicalIF":3.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Familial severe skeletal Class II malocclusion with gingival hyperplasia caused by a complex structural rearrangement at the KCNJ2-KCNJ16 locus.","authors":"Reza Maroofian, Alistair T Pagnamenta, Alireza Navabazam, Ron Schwessinger, Hannah E Roberts, Maria Lopopolo, Mohammadreza Dehghani, Mohammad Yahya Vahidi Mehrjardi, Alireza Haerian, Mojtaba Soltanianzadeh, Mohammad Hadi Noori Kooshki, Samantha J L Knight, Kerry A Miller, Simon J McGowan, Nicolas Chatron, Andrew T Timberlake, Uirá Souto Melo, Stefan Mundlos, David Buck, Stephen R F Twigg, Jenny C Taylor, Andrew O M Wilkie, Eduardo Calpena","doi":"10.1016/j.xhgg.2024.100352","DOIUrl":"10.1016/j.xhgg.2024.100352","url":null,"abstract":"<p><p>The aim of this work was to identify the underlying genetic cause in a four-generation family segregating an unusual phenotype comprising a severe form of skeletal Class II malocclusion with gingival hyperplasia. SNP array identified a copy number gain on chromosome 1 (chr1); however, this chromosomal region did not segregate correctly in the extended family. Exome sequencing also failed to identify a candidate causative variant but highlighted co-segregating genetic markers on chr17 and chr19. Short- and long-read genome sequencing allowed us to pinpoint and characterize at nucleotide-level resolution a chromothripsis-like complex rearrangement (CR) inserted into the chr17 co-segregating region at the KCNJ2-SOX9 locus. The CR involved the gain of five different regions from chr1 that are shuffled, chained, and inserted as a single block (∼828 kb) at chr17q24.3. The inserted sequences contain craniofacial enhancers that are predicted to interact with KCNJ2/KCNJ16 through neo-topologically associating domain (TAD) formation to induce ectopic activation. Our findings suggest that the CR inserted at chr17q24.3 is the cause of the severe skeletal Class II malocclusion with gingival hyperplasia in this family and expands the panoply of phenotypes linked to variation at the KCNJ2-SOX9 locus. In addition, we highlight a previously overlooked potential role for misregulation of the KCNJ2/KCNJ16 genes in the pathomechanism of gingival hyperplasia associated with deletions and other rearrangements of the 17q24.2-q24.3 region (MIM 135400).</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100352"},"PeriodicalIF":3.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11465088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models.","authors":"Pratik Ramprasad, Nidhi Pai, Wei Pan","doi":"10.1016/j.xhgg.2024.100347","DOIUrl":"10.1016/j.xhgg.2024.100347","url":null,"abstract":"<p><p>Artificial intelligence (AI)/deep learning (DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals, and narrows the performance gap with an elastic net regression approach that uses just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may eventually predict molecular phenotypes from DNA sequences with an accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100347"},"PeriodicalIF":3.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11416237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Harding and Martinez.","authors":"Dominique L Brooks, Kiran Musunuru, Xiao Wang","doi":"10.1016/j.xhgg.2024.100307","DOIUrl":"10.1016/j.xhgg.2024.100307","url":null,"abstract":"","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":"5 3","pages":"100307"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11153232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeted sequencing for hereditary breast and ovarian cancer in BRCA1/2-negative families reveals complex genetic architecture and phenocopies.","authors":"Jocelyn N Plowman, Evanjalina J Matoy, Lavanya V Uppala, Samantha B Draves, Cynthia J Watson, Bridget A Sefranek, Mark L Stacey, Samuel P Anderson, Michael A Belshan, Elizabeth E Blue, Chad D Huff, Yusi Fu, Holly A F Stessman","doi":"10.1016/j.xhgg.2024.100306","DOIUrl":"10.1016/j.xhgg.2024.100306","url":null,"abstract":"<p><p>Approximately 20% of breast cancer cases are attributed to increased family risk, yet variation in BRCA1/2 can only explain 20%-25% of cases. Historically, only single gene or single variant testing were common in at-risk family members, and further sequencing studies were rarely offered after negative results. In this study, we applied an efficient and inexpensive targeted sequencing approach to provide molecular diagnoses in 245 human samples representing 134 BRCA mutation-negative (BRCAX) hereditary breast and ovarian cancer (HBOC) families recruited from 1973 to 2019 by Dr. Henry Lynch. Sequencing identified 391 variants, which were functionally annotated and ranked based on their predicted clinical impact. Known pathogenic CHEK2 breast cancer variants were identified in five BRCAX families in this study. While BRCAX was an inclusion criterion for this study, we still identified a pathogenic BRCA2 variant (p.Met192ValfsTer13) in one family. A portion of BRCAX families could be explained by other hereditary cancer syndromes that increase HBOC risk: Li-Fraumeni syndrome (gene: TP53) and Lynch syndrome (gene: MSH6). Interestingly, many families carried additional variants of undetermined significance (VOUSs) that may further modify phenotypes of syndromic family members. Ten families carried more than one potential VOUS, suggesting the presence of complex multi-variant families. Overall, nine BRCAX HBOC families in our study may be explained by known likely pathogenic/pathogenic variants, and six families carried potential VOUSs, which require further functional testing. To address this, we developed a functional assay where we successfully re-classified one family's PMS2 VOUS as benign.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100306"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11166883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model organism pipeline provides insight into the clinical heterogeneity of TARS1 loss-of-function variants.","authors":"Rebecca Meyer-Schuman, Allison R Cale, Jennifer A Pierluissi, Kira E Jonatzke, Young N Park, Guy M Lenk, Stephanie N Oprescu, Marina A Grachtchouk, Andrzej A Dlugosz, Asim A Beg, Miriam H Meisler, Anthony Antonellis","doi":"10.1016/j.xhgg.2024.100324","DOIUrl":"10.1016/j.xhgg.2024.100324","url":null,"abstract":"<p><p>Aminoacyl-tRNA synthetases (ARSs) are ubiquitously expressed, essential enzymes that complete the first step of protein translation: ligation of amino acids to cognate tRNAs. Genes encoding ARSs have been implicated in myriad dominant and recessive phenotypes, the latter often affecting multiple tissues but with frequent involvement of the central and peripheral nervous systems, liver, and lungs. Threonyl-tRNA synthetase (TARS1) encodes the enzyme that ligates threonine to tRNA^THR in the cytoplasm. To date, TARS1 variants have been implicated in a recessive brittle hair phenotype. To better understand TARS1-related recessive phenotypes, we engineered three TARS1 missense variants at conserved residues and studied these variants in Saccharomyces cerevisiae and Caenorhabditis elegans models. This revealed two loss-of-function variants, including one hypomorphic allele (R433H). We next used R433H to study the effects of partial loss of TARS1 function in a compound heterozygous mouse model (R432H/null). This model presents with phenotypes reminiscent of patients with TARS1 variants and with distinct lung and skin defects. This study expands the potential clinical heterogeneity of TARS1-related recessive disease, which should guide future clinical and genetic evaluations of patient populations.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100324"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11284558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141493724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability.","authors":"Yuka Suzuki, Hervé Ménager, Bryan Brancotte, Raphaël Vernet, Cyril Nerin, Christophe Boetto, Antoine Auvergne, Christophe Linhard, Rachel Torchet, Pierre Lechat, Lucie Troubat, Michael H Cho, Emmanuelle Bouzigon, Hugues Aschard, Hanna Julienne","doi":"10.1016/j.xhgg.2024.100319","DOIUrl":"10.1016/j.xhgg.2024.100319","url":null,"abstract":"<p><p>Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10^-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100319"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11260573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landscape of genomic structural variations in Indian population-based cohorts: Deeper insights into their prevalence and clinical relevance.","authors":"Krithika Subramanian, Mehak Chopra, Bratati Kahali","doi":"10.1016/j.xhgg.2024.100285","DOIUrl":"10.1016/j.xhgg.2024.100285","url":null,"abstract":"<p><p>Structural variations (SV) are large (>50 base pairs) genomic rearrangements comprising deletions, duplications, insertions, inversions, and translocations. Studying SVs is important because they play active and critical roles in regulating gene expression, determining disease predispositions, and identifying population-specific differences among individuals of diverse ancestries. However, SV discoveries in the Indian population using whole-genome sequencing (WGS) have been limited. In this study, using short-read WGS having an average 42X depth of coverage, we identify and characterize 36,210 SVs from 529 individuals enrolled in population-based cohorts in India. These SVs include 24,574 deletions, 2,913 duplications, 8,710 insertions, and 13 inversions; 1.26% (456 out of 36,210) of the identified SVs can potentially impact the coding regions of genes. Furthermore, 56 of these SVs are highly intolerant to loss-of-function changes to the mapped genes, and five SVs impacting ADAMTS17, CCDC40, and RHCE are common in our study individuals. Seven rare SVs significantly impact dosage sensitivity of genes known to be associated with various clinical phenotypes. Most of the SVs in our study are rare and heterozygous. This fine-scale SV discovery in the underrepresented Indian population provides valuable insights that extend beyond Eurocentric human genetic studies.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100285"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11007539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140194674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex.","authors":"Alanna C Cote, Hannah E Young, Laura M Huckins","doi":"10.1016/j.xhgg.2024.100311","DOIUrl":"10.1016/j.xhgg.2024.100311","url":null,"abstract":"<p><p>Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a \"module-QTL\" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":" ","pages":"100311"},"PeriodicalIF":3.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11214266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}