Fatal familial insomnia (FFI) and a subtype of familial Creutzfeldt-Jakob disease (CJD), two clinically and pathologically distinct diseases, are linked to the same mutation at codon 178 (Asn178) of the prion protein gene. The possibility that a second genetic component modified the phenotypic expression of the Asn178 mutation was investigated. FFI and the familial CJD subtype segregated with different genotypes determined by the Asn178 mutation and the methionine-valine polymorphism at codon 129. The Met129, Asn178 allele segregated with FFI in all 15 affected members of five kindreds whereas the Val129, Asn178 allele segregated with the familial CJD subtype in all 15 affected members of six kindreds. Thus, two distinct disease phenotypes linked to a single pathogenic mutation can be determined by a common polymorphism.

The PRNP gene, encoding the amyloid precursor protein that is centrally involved in Creutzfeldt-Jakob disease (CJD), has an unstable region of five variant tandem octapeptide coding repeats between codons 51 and 91. We screened a total of 535 individuals for the presence of extra repeats in this region, including patients with sporadic and

Genetic coding is generally thought to have required ribozymes whose functions were taken over by polypeptide aminoacyl-tRNA synthetases (aaRS). Two discoveries about aaRS and their interactions with tRNA substrates now furnish a unifying rationale for the opposite conclusion: that the key processes of the Central Dogma of molecular biology emerged simultaneously and naturally from simple origins in a peptide•RNA partnership, eliminating the epistemological utility of a prior RNA world. First, the two aaRS classes likely arose from opposite strands of the same ancestral gene, implying a simple genetic alphabet. The resulting inversion symmetries in aaRS structural biology would have stabilized the initial and subsequent differentiation of coding specificities, rapidly promoting diversity in the proteome. Second, amino acid physical chemistry maps onto tRNA identity elements, establishing reflexive, nanoenvironmental sensing in protein aaRS. Bootstrapping of increasingly detailed coding is thus intrinsic to polypeptide aaRS, but impossible in an RNA world. These notions underline the following concepts that contradict gradual replacement of ribozymal aaRS by polypeptide aaRS: 1) aaRS enzymes must be interdependent; 2) reflexivity intrinsic to polypeptide aaRS production dynamics promotes bootstrapping; 3) takeover of RNA-catalyzed aminoacylation by enzymes will necessarily degrade specificity; and 4) the Central Dogma’s emergence is most probable when replication and translation error rates remain comparable. These characteristics are necessary and sufficient for the essentially de novo emergence of a coupled gene–replicase–translatase system of genetic coding that would have continuously preserved the functional meaning of genetically encoded protein genes whose phylogenetic relationships match those observed today.

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Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work. OPEN ACCESSCitation: Wills P, Meyer FG (2020) Metrics for graph comparison: A practitioner's guide. PLoS ONE 15(2): e0228728. https://doi.org/10.