Novel pathogenic<em> EIF2S3</em> missense variants causing clinically variable <em>MEHMO</em> syndrome with impaired eIF2γ translational function, and literature review.

Rare pathogenicEIF2S3missense and terminal deletion variants cause the X-linked intellectual disability (ID) syndrome MEHMO, or a milder phenotype including pancreatic dysfunction and hypopituitarism. We present two unrelated male patients who carry novelEIF2S3pathogenic missense variants (p.(Thr144Ile) and p.(Ile159Leu)) thereby broadening the limited genetic spectrum and underscoring clinically variable expressivity of MEHMO. While the affected male with p.(Thr144Ile) presented with severe motor delay, severe microcephaly, moderate ID, epileptic seizures responsive to treatments, hypogenitalism, central obesity, facial features, and diabetes, the affected male with p.(Ile159Leu) presented with moderate ID, mild motor delay, microcephaly, epileptic seizures resistant to treatment, central obesity, and mild facial features. Both variants are located in the highly conserved guanine nucleotide binding domain of theEIF2S3encoded eIF2 gamma subunit of the heterotrimeric translation initiation factor 2 (eIF2) complex. Further, we investigated both variants in a structural model and in yeast. The reduced growth rates and lowered fidelity of translation with increased initiation at non-AUG codons observed for both mutants in these studies strongly support pathogenicity of the variants

Report of Observations of Mars in 1939, IV

Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98 % and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models