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Besides the truncation of the protein caused by the xav etfdh mutation, we also found that the overall abundance of etfdh mRNA in xav is dramatically reduced.
Though our probe-level analysis suggested only limited overlap in the genes affected by the Cln3Δex7/8 and Cln6nclf mutation, we also sought to examine the data at a pathways level.
In just these 9 clones, we isolated mutations that were uncovered in each previous folding-enhanced variant of GFP: V163A is a cycle 3 mutation (we also found M153K) [2], F64L is an enhanced GFP mutation [15], A206V and Y145F are superfolder mutations [7] and our most common mutation, N105Y, is found at the same residue as the N105T superfolder mutation.
Since the patient is heterozygous for the K16N mutation we also analysed the equimolar mixtures of both peptides (Aβ mix).
Three had c.592G>A (p.Glu198Lys), the mutation we also identified and the other had c.602C>G (p.Pro201Arg), which is also located in the substrate specificity loop.
For each mutation we also state the frequency of all samples, the frequency of all samples with at least one mutation, as well as the relative frequency at the given position.
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Therefore, in addition to point mutations we also generated "shuffled" proteins by swapping the EH domain between AtEHD1 and AtEHD2.
Other than somatic mtDNA mutations, we also detected few germ line mtDNA sequence variants in the tumors and corresponding mucosal biopsies of all the patients (Table 3).
Because cancers other than breast, ovary, pancreas, and prostate, and melanoma could be related to BRCA1/2 mutations, we also estimated risks of mortality with censoring at time of diagnosis of any cancer (Table 3 and Figure 3).
As control mutations, we also included a representative BPES type I-causing variant (FOXL2-I80T), whish is associated with granulosa cell dysfunction, and a representative BPES type II-causing variant (FOXL2-N109K), whish is associated with normal ovarian function, to compare the molecular behavior of the FOXL2-C134W cancer-associated variant with BPES-causing mutations.
To create complete networks that can be tied specifically to CTD mutations, we also identified all proteins in the database that exhibit "first interactions" with the CTD, and determined where they intersect with "direct interactions" of differentially regulated genes from each of the phenotype sub-networks described above.
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