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An ~140-kb deletion associated with feline spinal muscular atrophy implies an essential LIX1 function for motor neuron survival

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TLDR
A novel SMA gene candidate, LIX1, is identified in an approximately140-kb deletion on feline chromosome A1q in a region of conserved synteny to human chromosome 5q15, where the predicted secondary structure is compatible with a role in RNA metabolism.
Abstract
The leading genetic cause of infant mortality is spinal muscular atrophy (SMA), a clinically and genetically heterogeneous group of disorders. Previously we described a domestic cat model of autosomal recessive, juvenile-onset SMA similar to human SMA type III. Here we report results of a whole-genome scan for linkage in the feline SMA pedigree using recently developed species-specific and comparative mapping resources. We identified a novel SMA gene candidate, LIX1, in an approximately140-kb deletion on feline chromosome A1q in a region of conserved synteny to human chromosome 5q15. Though LIX1 function is unknown, the predicted secondary structure is compatible with a role in RNA metabolism. LIX1 expression is largely restricted to the central nervous system, primarily in spinal motor neurons, thus offering explanation of the tissue restriction of pathology in feline SMA. An exon sequence screen of 25 human SMA cases, not otherwise explicable by mutations at the SMN1 locus, failed to identify comparable LIX1 mutations. Nonetheless, a LIX1-associated etiology in feline SMA implicates a previously undetected mechanism of motor neuron maintenance and mandates consideration of LIX1 as a candidate gene in human SMA when SMN1 mutations are not found.

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References
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Molecular Cloning: A Laboratory Manual

TL;DR: Molecular Cloning has served as the foundation of technical expertise in labs worldwide for 30 years as mentioned in this paper and has been so popular, or so influential, that no other manual has been more widely used and influential.
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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

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TL;DR: A new greedy alignment algorithm is introduced with particularly good performance and it is shown that it computes the same alignment as does a certain dynamic programming algorithm, while executing over 10 times faster on appropriate data.
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