PCR-based cancer diagnosis requires detection of rare mutations in k-ras, p53 or other genes. The assumption has been that mutant and wild-type sequences amplify with near equal efficiency, so that they are eventually present in proportions representative of the starting material. Work on factor IX suggests that this assumption is invalid for one case of near-sequence identity. To test the generality of this phenomenon and its relevance to cancer diagnosis, primers distant from point mutations in p53 and k-ras were used to amplify wild-type and mutant sequences from these genes. A substantial bias against PCR amplification of mutants was observed for two regions of the p53 gene and one region of k-ras. For k-ras and p53, bias was observed when the wild-type and mutant sequences were amplified separately or when mixed in equal proportions before PCR. Bias was present with proofreading and non-proofreading polymerases. Mutant and wild-type segments of the factor V, cystic fibrosis transmembrane conductance regulator and prothrombin genes were amplified and did not exhibit PCR bias. Therefore, the assumption of equal PCR efficiency for point mutant and wild-type sequences is invalid in several systems. Quantitative or diagnostic PCR will require validation for each locus, and enrichment strategies may be needed to optimize detection of mutants.

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

We previously described significant changes in GH-binding protein (GHBP) in pathological human pregnancy. There was a substantial elevation of GHBP in cases of noninsulin-dependent diabetes mellitus and a reduction in insulin-dependent diabetes mellitus. GHBP has the potential to modulate the proportion of free placental GH (PGH) and hence the impact on the maternal GH/insulin-like growth factor I (IGF-I) axis, fetal growth, and maternal glycemic status. The present study was undertaken to investigate the relationship among glycemia, GHBP, and PGH during pregnancy and to assess the impact of GHBP on the concentration of free PGH. We have extended the analysis of specimens to include measurements of GHBP, PGH, IGF-I, IGF-II, IGF-binding protein-1 (IGFBP-1), IGFBP-2, and IGFBP-3 and have related these to maternal characteristics, fetal growth, and glycemia. The simultaneous measurement of GHBP and PGH has for the first time allowed calculation of the free component of PGH and correlation of the free component to indexes of fetal growth and other endocrine markers. PGH, free PGH, IGF-I, and IGF-II were substantially decreased in IUGR at 28 -30 weeks gestation (K28) and 36 -38 weeks gestation (K36). The mean concentration (ϮSEM) of total PGH increased significantly from K28 to K36 (30.0 Ϯ 2.2 to 50.7 Ϯ 6.2 ng/mL; n ϭ 40), as did the concentration of free PGH (23.4 Ϯ 2.3 to 43.7 Ϯ 6.0 ng/mL; n ϭ 38). The mean percentage of free PGH was significantly less in IUGR than in normal subjects (67% vs. 79%; P Ͻ 0.01). Macrosomia was associated with an increase in these parameters that did not reach statistical significance. Multiple regression analysis revealed that PGH/IGF-I and IGFBP-3 account for 40% of the variance in birth weight. IGFBP-3 showed a significant correlation with IGF-I, IGF-II, and free and total PGH at K28 and K36. Noninsulin-dependent diabetes mellitus patients had a lower mean percentage of free PGH (65%; P Ͻ 0.01), and insulin-dependent diabetics had a higher mean percentage of free PGH (87%; P Ͻ 0.01) than normal subjects. Mean postprandial glucose at K28 correlated positively with PGH and free PGH (consistent with the hyperglycemic action of GH). GHBP correlated negatively with both postprandial and fasting glucose. Although GHBP correlated negatively with PGH (r ϭ Ϫ0.52; P Ͻ .001), free PGH and total PGH correlated very closely (r ϭ 0.98). The results are consistent with an inhibitory function for GHBP in vivo and support a critical role for placental GH and IGF-I in driving normal fetal growth. (J Clin Endocrinol

Human GH (hGH) fragments 1-43 and 44-191 have potent in vivo effects on glucose homeostasis in rodents but cannot stimulate body growth. To assess the in vitro bioactivity of these hGH fragments we tested their activity against GH-responsive FDC-P1 cell lines expressing full-length human (h), mouse (m), or rabbit (r) GH receptors (GHR). Binding specificity and affinity was tested using GHR-containing membrane preparations from three species and recombinant hGH binding protein (hGHBP). Recombinant hGH 1-43 and recombinant 44-191 stimulated proliferation of FDC-P1-hGHR cells with half-maximal effect at approximately 2000 and 100 nM, respectively, whereas intact hGH stimulates proliferation of FDC-P1-hGHR cells with ED50 of 0.02-0.03 nM. However, these fragments had minimal effect on cells expressing mGHR or rGHR. Although 44-191 did not stimulate proliferation of FDC-P1-rGHR cells, when added to these cells in the presence of 0.23 nM hGH, it antagonized hGH action in a dose-dependent manner (ED50 at 230 nM). Binding of these GH fragments was compared using membrane preparations from rabbit liver, rabbit and mouse adipose tissue, and recombinant hGHBP. Binding competition curves were consistent, with 44-191 having at least a 10-fold lower affinity for rabbit liver GHR and rabbit adipose GHR than bovine GH and a 61-fold lower affinity for hGHBP than hGH. Binding of hGH 1-43 could not be demonstrated to GHRs in rabbit liver microsomes, adipose microsomes, or to hGHBP. HGH 1-43 did not compete for insulin binding sites in adipose microsomes. In conclusion, hGH 44-191 binds with low affinity to the GHR and at supraphysiologic levels stimulates proliferation of FDC-P1-hGHR cells. At high doses, 44-191 can also antagonize GH action in FDC-P1-rGHR cells, presumably by blocking receptor dimerization. Binding of 1-43 to GHR could not be detected, and the basis for its weak in vitro mitogenic effect remains to be elucidated. The low affinity of the fragments for cloned GHRs and low biopotency in these systems suggests that the metabolic actions of these fragments are unlikely to be mediated by the cloned GHR. This raises the possibility of a separate receptor mediating metabolic effects of these fragments.

We developed Graphical Representation of Ancestral Sequence Predictions (GRASP) to infer and explore ancestral variants of protein families with more than 10,000 members. GRASP uses partial order graphs to represent homology in very large datasets, which are intractable with current inference tools and may, for example, be used to engineer proteins by identifying ancient variants of enzymes. We demonstrate that (1) across three distinct enzyme families, GRASP predicts ancestor sequences, all of which demonstrate enzymatic activity, (2) within-family insertions and deletions can be used as building blocks to support the engineering of biologically active ancestors via a new source of ancestral variation, and (3) generous inclusion of sequence data encompassing great diversity leads to less variance in ancestor sequence.GRASP is the central tool in the GRASP-suite, which is freely available at