Towards a Newborn Screening Common Data Model: The Utah Newborn Screening Data Model.

As newborn screening programs transition from paper-based data exchange toward automated, electronic methods, significant data exchange challenges must be overcome. This article outlines a data model that maps newborn screening data elements associated with patient demographic information, birthing facilities, laboratories, result reporting, and follow-up care to the LOINC, SNOMED CT, ICD-10-CM, and HL7 healthcare standards. The described framework lays the foundation for the implementation of standardized electronic data exchange across newborn screening programs, leading to greater data interoperability. The use of this model can accelerate the implementation of electronic data exchange between healthcare providers and newborn screening programs, which would ultimately improve health outcomes for all newborns and standardize data exchange across programs.

Prospective identification by neonatal screening of patients with guanidinoacetate methyltransferase deficiency.

INTRODUCTION: Guanidinoacetate methyltransferase (GAMT) deficiency is an inherited metabolic disorder that impairs the synthesis of creatine (CRE). Lack of CRE in the brain can cause intellectual disability, autistic-like behavior, seizures, and movement disorders. Identification at birth and immediate therapy can prevent intellectual disability and seizures. Here we report the first two cases of GAMT deficiency identified at birth by newborn screening (NBS) in Utah and New York. METHODS: NBS dried blood spots were analyzed by tandem mass spectrometry (MS/MS) using either derivatized or non-derivatized assays to detect guanidinoacetate (GUAC) and CRE. For any positive samples, a second-tier test using a more selective method, ultra-performance liquid chromatography (UPLC) combined with MS/MS, was performed to separate GUAC from potential isobaric interferences. RESULTS: NBS for GAMT deficiency began in Utah on June 1, 2015 using a derivatized method for the detection of GUAC and CRE. In May 2019, the laboratory and method transitioned to a non-derivatized method. GAMT screening was added to the New York State NBS panel on October 1, 2018 using a derivatized method. In New York, a total of 537,408 babies were screened, 23 infants were referred and one newborn was identified with GAMT deficiency. In Utah, a total of 273,902 infants were screened (195,425 with the derivatized method, 78,477 with the non-derivatized method), three infants referred and one was identified with GAMT deficiency. Mean levels of GUAC and CRE were similar between methods (Utah derivatized: GUAC = 1.20 ± 0.43 µmol/L, CRE = 238 ± 96 µmol/L; Utah non-derivatized: GUAC = 1.23 ± 0.61 µmol/L, CRE = 344 ± 150 µmol/L, New York derivatized: GUAC = 1.34 ± 0.57 µmol/L, CRE = 569 ± 155 µmol/L). With either Utah method, similar concentrations of GUAC are observed in first (collected around 1 day of age) and the second NBS specimens (routinely collected at 7-16 days of age), while CRE concentrations decreased in the second NBS specimens. Both infants identified with GAMT deficiency started therapy by 2 weeks of age and are growing and developing normally at 7 (Utah) and 4 (New York) months of age. CONCLUSIONS: Newborn screening allows for the prospective identification of GAMT deficiency utilizing elevated GUAC concentration as a marker. First-tier screening may be incorporated into existing methods for amino acids and acylcarnitines without the need for new equipment or staff. Newborn screening performed by either derivatized or non-derivatized methods and coupled with second-tier testing, has a very low false positive rate and can prospectively identify affected children. SummaryCerebral creatine deficiency syndromes caused by defects in creatine synthesis can result in intellectual disability, and are preventable if therapy is initiated early in life. This manuscript reports the identification of two infants with GAMT deficiency (one of the cerebral creatine deficiency syndromes) by newborn screening and demonstrates NBS feasibility using a variety of methods.

Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms.

Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal-complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.

Role of the isoprenyl pocket of the G protein beta gamma subunit complex in the binding of phosducin and phosducin-like protein.

Phosducin (Pdc) and phosducin-like protein (PhLP) regulate G protein-mediated signaling by binding to the betagamma subunit complex of heterotrimeric G proteins (Gbetagamma) and removing the dimer from cell membranes. The binding of Pdc induces a conformational change in the beta-propeller structure of Gbetagamma, creating a pocket between blades 6 and 7. It has been proposed that the isoprenyl group of Gbetagamma inserts into this pocket, stabilizing the Pdc.Gbetagamma structure and decreasing the affinity of the complex for the lipid bilayer. To test this hypothesis, the binding of Pdc and PhLP to several Gbetagamma dimers containing variants of the beta or gamma subunit was measured. These variants included modifications of the isoprenyl group (gamma), residues involved in the conformational change (beta), and residues lining the proposed prenyl pocket (beta). Switching prenyl groups from farnesyl to geranylgeranyl or vice versa had little effect on binding. However, alanine substitution of one residue in the beta subunit involved in the conformational change (W332) decreased binding 5-fold. Alanine substitution of certain residues within the prenyl pocket caused only minor decreases in binding, while a lysine substitution of T329 within the pocket inhibited binding 10-fold. Molecular modeling of the binding energy of the Pdc.Gbeta(1)gamma(2) complex required insertion of the geranylgeranyl group into the prenyl pocket in order to accurately predict the effects of prenyl pocket amino acid substitutions. Finally, a dimer containing a gamma subunit with no prenyl group (gamma(2)-C68S) decreased binding by nearly 20-fold. These results support the structural model in which the prenyl group escapes contact with the aqueous milieu by inserting into the prenyl pocket and stabilizing the Pdc-binding conformation of Gbetagamma.

Molecular modeling of the binding of 5-substituted 2'-deoxyuridine substrates to thymidine kinase of herpes simplex virus type-1.

In an earlier study, De Winter and Herdewijn (J. Med. Chem. 1996, 37, 4727-4737) studied the binding of various 5-substituted 2'-deoxyuridine substrates to thymidine kinase of herpes simplex virus type-1. They used a computational procedure that achieves good correlation with experimentally determined IC50 values. We applied an alternative procedure to the same deoxyuridine substrates, using only three readily calculated quantities-the binding energy, the molecular surface area, and a flexibility factor. Our simplified method achieves the same degree of correlation with the IC50 values as did the earlier procedure. We then applied this procedure to examine the binding of various 5-substituted pyrimidine 1,5-anhydrohexitol substrates to thymidine kinase.

Procedure for selecting starting conformations for energy minimization of nucleosides and nucleotides.

The purpose of this study was to carry out a thorough search of the conformational space of various adenine-containing nucleotides, applying a previously published searching procedure, known as the representative method. This method, which reduces the number of starting conformations required to explore all the important regions of conformational space, appears to be successful in finding all (or nearly all) the putative low-energy conformations of each molecule.