Germline selection of PTPN11 (HGNC:9644) variants make a major contribution to both Noonan syndrome's high birth rate and the transmission of sporadic cancer variants resulting in fetal abnormality.

Some spontaneous germline gain-of-function mutations promote spermatogonial stem cell clonal expansion and disproportionate variant sperm production leading to unexpectedly high transmission rates for some human genetic conditions. To measure the frequency and spatial distribution of de novo mutations we divided three testes into 192 pieces each and used error-corrected deep-sequencing on each piece. We focused on PTPN11 (HGNC:9644) Exon 3 that contains 30 different PTPN11 Noonan syndrome (NS) mutation sites. We found 14 of these variants formed clusters among the testes; one testis had 11 different variant clusters. The mutation frequencies of these different clusters were not correlated with their case-recurrence rates nor were case recurrence rates of PTPN11 variants correlated with their tyrosine phosphatase levels thereby confusing PTPN11's role in germline clonal expansion. Six of the PTPN11 exon 3 de novo variants associated with somatic mutation-induced sporadic cancers (but not NS) also formed testis clusters. Further, three of these six variants were observed among fetuses that underwent prenatal ultrasound screening for NS-like features. Mathematical modeling showed that germline selection can explain both the mutation clusters and the high incidence of NS (1/1000-1/2500).

Age-dependent germline mosaicism of the most common noonan syndrome mutation shows the signature of germline selection.

Noonan syndrome (NS) is among the most common Mendelian genetic diseases (∼1/2,000 live births). Most cases (50%-84%) are sporadic, and new mutations are virtually always paternally derived. More than 47 different sites of NS de novo missense mutations are known in the PTPN11 gene that codes for the protein tyrosine phosphatase SHP-2. Surprisingly, many of these mutations are recurrent with nucleotide substitution rates substantially greater than the genome average; the most common mutation, c.922A>G, is at least 2,400 times greater. We examined the spatial distribution of the c.922A>G mutation in testes from 15 unaffected men and found that the mutations were not uniformly distributed across each testis as would be expected for a mutation hot spot but were highly clustered and showed an age-dependent germline mosaicism. Computational modeling that used different stem cell division schemes confirmed that the data were inconsistent with hypermutation, but consistent with germline selection: mutated spermatogonial stem cells gained an advantage that allowed them to increase in frequency. SHP-2 interacts with the transcriptional activator STAT3. Given STAT3's function in mouse spermatogonial stem cells, we suggest that this interaction might explain the mutant's selective advantage by means of repression of stem cell differentiation signals. Repression of STAT3 activity by cyclin D1 might also play a previously unrecognized role in providing a germline-selective advantage to spermatogonia for the recurrent mutations in the receptor tyrosine kinases that cause Apert syndrome and MEN2B. Looking at recurrent mutations driven by germline selection in different gene families can help highlight common causal signaling pathways.

Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing.

We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied. Regardless of the gene segment, the particular testis donor or the 73 different testis pieces used, the frequencies for any one of the six different mutation types were consistent. Averaging over the C>T/G>A and G>T/C>A mutation types the background mutation frequency was 2.6x10-5 per base pair, while for the four other mutation types the average background frequency was lower at 1.5x10-6 per base pair. These rates far exceed the well documented human genome average frequency per base pair (~10-8) suggesting a non-biological explanation for our data. By computational modeling and a new experimental procedure to distinguish between pre-mutagenic lesion base mismatches and a fully mutated base pair in the original DNA molecule, we argue that most of the base-dependent variation in background frequency is due to a mixture of deamination and oxidation during the first two PCR cycles. Finally, we looked at a previously studied disease mutation in the PTPN11 gene and could easily distinguish true mutations from the SSS background. We also discuss the limits and possibilities of this and other methods to measure exceptionally rare mutation frequencies, and we present calculations for other scientists seeking to design their own such experiments.

Identification and characterization of dynein genes in Tetrahymena.

We describe the protocol through which we identify and characterize dynein subunit genes in the ciliated protozoan Tetrahymena thermophila. The gene(s) of interest is found by searching the Tetrahymena genome, and it is characterized in silico including the prediction of the open reading frame and identification of likely introns. The gene is then characterized experimentally, including the confirmation of the exon-intron organization of the gene and the measurement of the expression of the gene in nondeciliated and reciliating cells. In order to understand the function of the gene product, the gene is modified-for example, deleted, overexpressed, or epitope-tagged-using the straightforward gene replacement strategies available with Tetrahymena. The effect(s) of the dynein gene modification is evaluated by examining transformants for ciliary traits including cell motility, ciliogenesis, cell division, and the engulfment of particles through the oral apparatus. The multistepped protocol enables undergraduate students to engage in short- and long-term experiments. In our laboratory during the last 6 years, more than two dozen undergraduate students have used these methods to investigate dynein subunit genes.