Objectives: Critical illnesses caused by undiagnosed genetic conditions are challenging in PICUs. Whole-exome sequencing is a powerful diagnostic tool but usually costly and often fail to arrive at a final diagnosis in a short period. We assessed the feasibility of our whole-exome sequencing as a tool to improve the efficacy of rare diseases diagnosis for pediatric patients with severe illness. Design: Observational analysis. Method: We employed a fast but standard whole-exome sequencing platform together with text mining-assisted variant prioritization in PICU setting over a 1-year period. Setting: A tertiary referral Children’s Hospital in Taiwan. Patients: Critically ill PICU patients suspected of having a genetic disease and newborns who were suspected of having a serious genetic disease after newborn screening were enrolled. Interventions: None. Measurements and Main Results: Around 50,000 to 100,000 variants were obtained for each of the 40 patients in 5 days after blood sampling. Eleven patients were immediately found be affected by previously reported mutations after searching mutation databases. Another seven patients had a diagnosis among the top five in a list ranked by text mining. As a whole, 21 patients (52.5%) obtained a diagnosis in 6.2 ± 1.1 working days (range, 4.3–9 d). Most of the diagnoses were first recognized in Taiwan. Specific medications were recommended for 10 patients (10/21, 47.6%), transplantation was advised for five, and hospice care was suggested for two patients. Overall, clinical management was altered in time for 81.0% of patients who had a molecular diagnosis. Conclusions: The current whole-exome sequencing algorithm, balanced in cost and speed, uncovers genetic conditions in infants and children in PICU, which helps their managements in time and promotes better utilization of PICU resources.

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

BackgroundThe novel gene HA117 is a multidrug resistance (MDR) gene expressed by all-trans retinoic acid-resistant HL-60 cells. In the present study, we compared the multidrug resistance of the HA117 with that of the classical multidrug resistance gene 1 (MDR1) in breast cancer cell line 4T1.MethodsTransduction of the breast cancer cell line 4T1 with adenoviral vectors encoding the HA117 gene and the green fluorescence protein gene (GFP) (Ad-GFP-HA117), the MDR1 and GFP (Ad-GFP-MDR1) or GFP (Ad-GFP) was respectively carried out. The transduction efficiency and the multiplicity of infection (MOI) were detected by fluorescence microscope and flow cytometry. The transcription of HA117 gene and MDR1 gene were detected by reverse transcription polymerase chain reaction (RT-PCR). Western blotting analysis was used to detect the expression of P-glycoprotein (P-gp) but the expression of HA117 could not be analyzed as it is a novel gene and its antibody has not yet been synthesized. The drug-excretion activity of HA117 and MDR1 were determined by daunorubicin (DNR) efflux assay. The drug sensitivities of 4T1/HA117 and 4T1/MDR1 to chemotherapeutic agents were detected by Methyl-Thiazolyl-Tetrazolium (MTT) assay.ResultsThe transducted efficiency of Ad-GFP-HA117 and Ad-GFP-MDR1 were 75%-80% when MOI was equal to 50. The transduction of Ad-GFP-HA117 and Ad-GFP-MDR1 could increase the expression of HA117 and MDR1. The drug resistance index to Adriamycin (ADM), vincristine (VCR), paclitaxel (Taxol) and bleomycin (BLM) increased to19.8050, 9.0663, 9.7245, 3.5650 respectively for 4T1/HA117 and 24.2236, 11.0480, 11.3741, 0.9630 respectively for 4T1/MDR1 as compared to the control cells. There were no significant differences in drug sensitivity between 4T1/HA117 and 4T1/MDR1 for the P-gp substrates (ADM, VCR and Taxol) (P < 0.05), while the difference between them for P-gp non-substrate (BLM) was statistically significant (P < 0.05). DNR efflux assay confirmed that the multidrug resistance mechanism of HA117 might not be similar to that of MDR1.ConclusionsThese results confirm that HA117 is a strong MDR gene in both HL-60 and 4T1 cells. Furthermore, our results indicate that the MDR mechanism of the HA117 gene may not be similar to that of MDR1.

The novel gene HA117 is a multidrug resistance (MDR) gene in all-trans retinoic acid resistance HL-60 cells. The transduction of adenovirus vectors encoding HA117 conferred breast cancer cell line 4T1 MDR not only to MRP1 substrate drugs but also to MRP1 non-substrate drugs and the MDR strength of HA117 was similar to that of multidrug resistance-associated protein-1 (MRP1) for MRP1 substrate, but HA117 had no daunorubicin-excretion function.