Extending the allelic spectrum at noncoding risk loci of orofacial clefting

Genome-wide association studies (GWAS) have generated unprecedented insights into the genetic etiology of orofacial clefting (OFC). The moderate effect sizes of associated noncoding risk variants and limited access to disease-relevant tissue represent considerable challenges for biological interpretation of genetic findings. As rare variants with stronger effect sizes are likely to also contribute to OFC, an alternative approach to delineate pathogenic mechanisms is to identify private mutations and/or an increased burden of rare variants in associated regions. This report describes a framework for targeted resequencing at selected noncoding risk loci contributing to nonsyndromic cleft lip with/without cleft palate (nsCL/P), the most frequent OFC subtype. Based on GWAS data, we selected three risk loci and identified candidate regulatory regions (CRRs) through the integration of credible SNP information, epigenetic data from relevant cells/tissues, and conservation scores. The CRRs (total 57 kb) were resequenced in a multiethnic study population (1061 patients; 1591 controls), using single-molecule molecular inversion probe technology. Combining evidence from in silico variant annotation, pedigree- and burden analyses, we identified 16 likely deleterious rare variants that represent new candidates for functional studies in nsCL/P. Our framework is scalable and represents a promising approach to the investigation of additional congenital malformations with multifactorial etiology

Inhibitory activity of a standardized elderberry liquid extract against clinically-relevant human respiratory bacterial pathogens and influenza A and B viruses

<p>Abstract</p> <p>Background</p> <p>Black elderberries (<it>Sambucus nigra </it>L.) are well known as supportive agents against common cold and influenza. It is further known that bacterial super-infection during an influenza virus (IV) infection can lead to severe pneumonia. We have analyzed a standardized elderberry extract (Rubini, BerryPharma AG) for its antimicrobial and antiviral activity using the microtitre broth micro-dilution assay against three Gram-positive bacteria and one Gram-negative bacteria responsible for infections of the upper respiratory tract, as well as cell culture experiments for two different strains of influenza virus.</p> <p>Methods</p> <p>The antimicrobial activity of the elderberry extract was determined by bacterial growth experiments in liquid cultures using the extract at concentrations of 5%, 10%, 15% and 20%. The inhibitory effects were determined by plating the bacteria on agar plates. In addition, the inhibitory potential of the extract on the propagation of human pathogenic H5N1-type influenza A virus isolated from a patient and an influenza B virus strain was investigated using MTT and focus assays.</p> <p>Results</p> <p>For the first time, it was shown that a standardized elderberry liquid extract possesses antimicrobial activity against both Gram-positive bacteria of <it>Streptococcus pyogenes </it>and group C and G <it>Streptococci</it>, and the Gram-negative bacterium <it>Branhamella catarrhalis </it>in liquid cultures. The liquid extract also displays an inhibitory effect on the propagation of human pathogenic influenza viruses.</p> <p>Conclusion</p> <p>Rubini elderberry liquid extract is active against human pathogenic bacteria as well as influenza viruses. The activities shown suggest that additional and alternative approaches to combat infections might be provided by this natural product.</p

Sex-Specific Genetic Associations for Barrett's Esophagus and Esophageal Adenocarcinoma

Acknowledgments We thank Dr Stuart MacGregor for his input on the study proposal and review of prior versions of this manuscript. We also thank all patients and controls for participating in this study. The MD Anderson controls were drawn from dbGaP (study accession: phs000187.v1.p1). Genotyping of these controls were done through the University of Texas MD Anderson Cancer Center (UTMDACC) and the Johns Hopkins University Center for Inherited Disease Research (CIDR). We acknowledge the principal investigators of this study: Christopher Amos, Qingyi Wei, and Jeffrey E. Lee. Controls from the Genome-Wide Association Study of Parkinson Disease were obtained from dbGaP (study accession: phs000196.v2.p1). This work, in part, used data from the National Institute of Neurological Disorders and Stroke (NINDS) dbGaP database from the CIDR: NeuroGenetics Research Consortium Parkinson’s disease study. We acknowledge the principal investigators and coinvestigators of this study: Haydeh Payami, John Nutt, Cyrus Zabetian, Stewart Factor, Eric Molho, and Donald Higgins. Controls from the Chronic Renal Insufficiency Cohort (CRIC) were drawn from dbGaP (study accession: phs000524.v1.p1). The CRIC study was done by the CRIC investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Data and samples from CRIC reported here were supplied by NIDDK Central Repositories. This report was not prepared in collaboration with investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories, or the NIDDK. We acknowledge the principal investigators and the project officer of this study: Harold I Feldman, Raymond R Townsend, Lawrence J. Appel, Mahboob Rahman, Akinlolu Ojo, James P. Lash, Jiang He, Alan S Go, and John W. Kusek. The following UK hospitals participated in sample collection through the Stomach and Oesophageal Cancer Study (SOCS) collaboration network: Addenbrooke’s Hospital, University College London, Bedford Hinchingbrooke Hospital, Peterborough City Hospital, West Suffolk Norfolk and Norwich University Hospital, Churchill Hospital, John Hospital, Velindre Hospital, St Bartholomew’s Hospital, Queen’s Burton, Queen Elisabeth Hospital, Diana Princess of Wales, Scunthorpe General Hospital, Royal Devon & Exeter Hospital, New Cross Hospital, Belfast City Hospital, Good Hope Hospital, Heartlands Hospital, South Tyneside District General Hospital, Cumberland Infirmary, West Cumberland Hospital, Withybush General Hospital, Stoke Mandeville Hospital, Wycombe General Hospital, Wexham Park Hospital, Southend Hospital, Guy’s Hospital, Southampton General Hospital, Bronglais General Hospital, Aberdeen Royal Infirmary, Manor Hospital, Clatterbridge Centre for Oncology, Lincoln County Hospital, Pilgrim Hospital, Grantham & District Hospital, St Mary’s Hospital London, Croydon University Hospital, Whipps Cross University Hospital, Wansbeck General Hospital, Hillingdon Hospital, Milton Keynes General Hospital, Royal Gwent Hospital, Tameside General Hospital, Castle Hill Hospital, St Richard’s Hospital, Ipswich Hospital, St Helens Hospital, Whiston Hospital, Countess of Chester Hospital, St Mary’s Hospital IOW, Queen Alexandra Hospital, Glan Clwyd Hospital, Wrexham Maelor Hospital, Darent Valley Hospital, Royal Derby Hospital, Derbyshire Royal Infirmary, Scarborough General Hospital, Kettering General Hospital, Kidderminster General Hospital, Royal Lancaster Infirmary, Furness General Hospital, Westmorland General Hospital, James Cook University Hospital, Friarage Hospital, Stepping Hill Hospital, St George’s Hospital London, Doncaster Royal Infirmary, Maidstone Hospital, Tunbridge Hospital, Prince Charles Hospital, Hartlepool Hospital, University Hospital of North Tees, Ysbyty Gwynedd, St. Jame’s University Hospital, Leeds General Infirmary, North Hampshire Hospital, Royal Preston Hospital, Chorley and District General, Airedale General Hospital, Huddersfield Royal Infirmary, Calderdale Royal Hospital, Torbay District General Hospital, Leighton Hospital, Royal Albert Edward Infirmary, Royal Surrey County Hospital, Bradford Royal Infirmary, Burnley General Hospital, Royal Blackburn Hospital, Royal Sussex County Hospital, Freeman Hospital, Royal Victoria Infirmary, Victoria Hospital Blackpool, Weston Park Hospital, Royal Hampshire County Hospital, Conquest Hospital, Royal Bournemouth General Hospital, Mount Vernon Hospital, Lister Hospital, William Harvey Hospital, Kent and Canterbury Hospital, Great Western Hospital, Dumfries and Galloway Royal Infirmary, Poole General Hospital, St Hellier Hospital, North Devon District Hospital, Salisbury District Hospital, Weston General Hospital, University Hospital Coventry, Warwick Hospital, George Eliot Hospital, Alexandra Hospital, Nottingham University Hospital, Royal Chesterfield Hospital, Yeovil District Hospital, Darlington Memorial Hospital, University Hospital of North Durham, Bishop Auckland General Hospital, Musgrove Park Hospital, Rochdale Infirmary, North Manchester General, Altnagelvin Area Hospital, Dorset County Hospital, James Paget Hospital, Derriford Hospital, Newham General Hospital, Ealing Hospital, Pinderfields General Hospital, Clayton Hospital, Dewsbury & District Hospital, Pontefract General Infirmary, Worthing Hospital, Macclesfield Hospital, University Hospital of North Staffordshire, Salford Royal Hospital, Royal Shrewsbury Hospital, and Manchester Royal Infirmary. Conflict of interest The authors disclose no conflicts. Funding This work was primarily funded by the National Institutes of Health (NIH) (R01CA136725). The funders of the study had no role in the design, analysis, or interpretation of the data, nor in writing or publication decisions related to this article. Jing Dong was supported by a Research Training Grant from the Cancer Prevention and Research Institute of Texas (CPRIT; RP160097) and the Research and Education Program Fund, a component of the Advancing a Healthier Wisconsin endowment at the Medical College of Wisconsin (AHW). Quinn T. Ostrom was supported by RP160097. Puya Gharahkhani was supported by a grant from National Health and Medical Research Council of Australia (1123248). Geoffrey Liu was supported by the Alan B. Brown Chair in Molecular Genomics and by the CCO Chair in Experimental Therapeutics and Population Studies. The University of Cambridge received salary support for Paul D. Pharoah from the NHS in the East of England through the Clinical Academic Reserve. Brian J. Reid was supported by a grant (P01CA91955) from the NIH/National Cancer Institute (NCI). Nicholas J. Shaheen was supported by a grant (P30 DK034987) from NIH. Thomas L. Vaughan was supported by NIH Established Investigator Award K05CA124911. Michael B. Cook was supported by the Intramural Research Program of the NCI, NIH, Department of Health and Human Services. Douglas A. Corley was supported by the NIH grants R03 KD 58294, R21DK077742, and RO1 DK63616 and NCI grant R01CA136725. Carlo Maj was supported by the BONFOR-program of the Medical Faculty, University of Bonn (O-147.0002). Jesper Lagergren was supported by the United European Gastroenterology (UEG) Research Prize. David C. Whiteman was supported by fellowships from the National Health and Medical Research Council of Australia (1058522, 1155413).Peer reviewedPostprin

Whole-Exome Sequencing and Homozygosity Analysis Implicate Depolarization-Regulated Neuronal Genes in Autism

Although autism has a clear genetic component, the high genetic heterogeneity of the disorder has been a challenge for the identification of causative genes. We used homozygosity analysis to identify probands from nonconsanguineous families that showed evidence of distant shared ancestry, suggesting potentially recessive mutations. Whole-exome sequencing of 16 probands revealed validated homozygous, potentially pathogenic recessive mutations that segregated perfectly with disease in 4/16 families. The candidate genes (UBE3B, CLTCL1, NCKAP5L, ZNF18) encode proteins involved in proteolysis, GTPase-mediated signaling, cytoskeletal organization, and other pathways. Furthermore, neuronal depolarization regulated the transcription of these genes, suggesting potential activity-dependent roles in neurons. We present a multidimensional strategy for filtering whole-exome sequence data to find candidate recessive mutations in autism, which may have broader applicability to other complex, heterogeneous disorders

Somatic Mutation Profiles of MSI and MSS Colorectal Cancer Identified by Whole Exome Next Generation Sequencing and Bioinformatics Analysis

BACKGROUND: Colorectal cancer (CRC) is with approximately 1 million cases the third most common cancer worldwide. Extensive research is ongoing to decipher the underlying genetic patterns with the hope to improve early cancer diagnosis and treatment. In this direction, the recent progress in next generation sequencing technologies has revolutionized the field of cancer genomics. However, one caveat of these studies remains the large amount of genetic variations identified and their interpretation. METHODOLOGY/PRINCIPAL FINDINGS: Here we present the first work on whole exome NGS of primary colon cancers. We performed 454 whole exome pyrosequencing of tumor as well as adjacent not affected normal colonic tissue from microsatellite stable (MSS) and microsatellite instable (MSI) colon cancer patients and identified more than 50,000 small nucleotide variations for each tissue. According to predictions based on MSS and MSI pathomechanisms we identified eight times more somatic non-synonymous variations in MSI cancers than in MSS and we were able to reproduce the result in four additional CRCs. Our bioinformatics filtering approach narrowed down the rate of most significant mutations to 359 for MSI and 45 for MSS CRCs with predicted altered protein functions. In both CRCs, MSI and MSS, we found somatic mutations in the intracellular kinase domain of bone morphogenetic protein receptor 1A, BMPR1A, a gene where so far germline mutations are associated with juvenile polyposis syndrome, and show that the mutations functionally impair the protein function. CONCLUSIONS/SIGNIFICANCE: We conclude that with deep sequencing of tumor exomes one may be able to predict the microsatellite status of CRC and in addition identify potentially clinically relevant mutations

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

De novo variants of CSNK2B cause a new intellectual disability-craniodigital syndrome by disrupting the canonical Wnt signaling pathway

CSNK2B encodes for casein kinase II subunit beta (CK2b), the regulatory subunit of casein kinase II (CK2), which is known to mediate diverse cellular pathways. Variants in this gene have been recently identified as a cause of Poirier-Bienvenu neurodevelopmental syndrome (POBINDS), but functional evidence is sparse. Here, we report five unrelated individuals: two of them manifesting POBINDS, while three are identified to segregate a new intellectual disability-craniodigital syndrome (IDCS), distinct from POBINDS. The three IDCS individuals carried two different de novo missense variants affecting the same codon of CSNK2B. Both variants, NP_001311.3; p.Asp32His and NP_001311.3; p.Asp32Asn, lead to an upregulation of CSNK2B expression at transcript and protein level, along with global dysregulation of canonical Wnt signaling. We found impaired interaction of the two key players DVL3 and b-catenin with mutated CK2b. The variants compromise the kinase activity of CK2 as evident by a marked reduction of phosphorylated b-catenin and consequent absence of active b-catenin inside nuclei of the patient-derived lymphoblastoid cell lines (LCLs). In line with these findings, whole-transcriptome profiling of patient-derived LCLs harboring the NP_001311.3; p.Asp32His variant confirmed a marked difference in expression of genes involved in the Wnt signaling pathway. In addition, whole-phosphoproteome analysis of the LCLs of the same subject showed absence of phosphorylation for 313 putative CK2 substrates, enriched in the regulation of nuclear b-catenin and transcription of the target genes. Our findings suggest that discrete variants in CSNK2B cause dominant-negative perturbation of the canonical Wnt signaling pathway, leading to a new craniodigital syndrome distinguishable from POBINDS

PHIP-associated Chung-Jansen syndrome: Report of 23 new individuals

In 2016 and 2018, Chung, Jansen and others described a new syndrome caused by haploinsufficiency of PHIP (pleckstrin homology domain interacting protein, OMIM *612,870) and mainly characterized by developmental delay (DD), learning difficulties/intellectual disability (ID), behavioral abnormalities, facial dysmorphism and obesity (CHUJANS, OMIM #617991). So far, PHIP alterations appear to be a rare cause of DD/ID. “Omics” technologies such as exome sequencing or array analyses have led to the identification of distinct types of alterations of PHIP, including, truncating variants, missense substitutions, splice variants and large deletions encompassing portions of the gene or the entire gene as well as adjacent genomic regions. We collected clinical and genetic data of 23 individuals with PHIP-associated Chung-Jansen syndrome (CHUJANS) from all over Europe. Follow-up investigations (e.g. Sanger sequencing, qPCR or Fluorescence-in-situ-Hybridization) and segregation analysis showed either de novo occurrence or inheritance from an also (mildly) affected parent. In accordance with previously described patients, almost all individuals reported here show developmental delay (22/23), learning disability or ID (22/23), behavioral abnormalities (20/23), weight problems (13/23) and characteristic craniofacial features (i.e. large ears/earlobes, prominent eyebrows, anteverted nares and long philtrum (23/23)). To further investigate the facial gestalt of individuals with CHUJANS, we performed facial analysis using the GestaltMatcher approach. By this, we could establish that PHIP patients are indistinguishable based on the type of PHIP alteration (e.g. missense, loss-of-function, splice site) but show a significant difference to the average face of healthy individuals as well as to individuals with Prader-Willi syndrome (PWS, OMIM #176270) or with a CUL4B-alteration (Intellectual developmental disorder, X-linked, syndromic, Cabezas type, OMIM #300354). Our findings expand the mutational and clinical spectrum of CHUJANS. We discuss the molecular and clinical features in comparison to the published individuals. The fact that some variants were inherited from a mildly affected parent further illustrates the variability of the associated phenotype and outlines the importance of a thorough clinical evaluation combined with genetic analyses for accurate diagnosis and counselling

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p