Oligodontia is the congenital absence of six or more teeth and comprises the more severe forms of tooth agenesis. Many genes have been implicated in the etiology of tooth agenesis, which is highly variable in its clinical presentation. The purpose of this study was to identify associations between genetic mutations and clinical features of oligodontia patients. An online systematic search of papers published from January 1992 to June 2021 identified 381 oligodontia cases meeting the eligibility criteria of causative gene mutation, phenotype description, and radiographic records. Additionally, ten families with oligodontia were recruited and their genetic etiologies were determined by whole-exome sequence analyses. We identified a novel mutation in WNT10A (c.99_105dup) and eight previously reported mutations in WNT10A (c.433 G > A; c.682 T > A; c.318 C > G; c.511.C > T; c.321 C > A), EDAR (c.581 C > T), and LRP6 (c.1003 C > T, c.2747 G > T). Collectively, 20 different causative genes were implicated among those 393 cases with oligodontia. For each causative gene, the mean number of missing teeth per case and the frequency of teeth missing at each position were calculated. Genotype-phenotype correlation analysis indicated that molars agenesis is more likely linked to PAX9 mutations, mandibular first premolar agenesis is least associated with PAX9 mutations. Mandibular incisors and maxillary lateral incisor agenesis are most closely linked to EDA mutations.
Humans ; Phenotype ; Wnt Proteins

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.