No abstract

Nuclear egress is a common herpesviral process regulating nucleocytoplasmic capsid release. For human cytomegalovirus (HCMV), the nuclear egress complex (NEC) is determined by the pUL50-pUL53 core that regulates multicomponent assembly with NEC-associated proteins and capsids. Recently, NEC crystal structures were resolved for α-, β- and γ-herpesviruses, revealing profound structural conservation, which was not mirrored, however, by primary sequence and binding properties. The NEC binding principle is based on hook-into-groove interaction through an N-terminal hook-like pUL53 protrusion that embraces an α-helical pUL50 binding groove. So far, pUL50 has been considered as the major kinase-interacting determinant and massive phosphorylation of pUL50-pUL53 was assigned to NEC formation and functionality. Here, we addressed the question of phenotypical changes of ORF-UL50-mutated HCMVs. Surprisingly, our analyses did not detect a predominant replication defect for most of these viral mutants, concerning parameters of replication kinetics (qPCR), viral protein production (Western blot/CoIP) and capsid egress (confocal imaging/EM). Specifically, only the ORF-UL50 deletion rescue virus showed a block of genome synthesis during late stages of infection, whereas all phosphosite mutants exhibited marginal differences compared to wild-type or revertants. These results (i) emphasize a rate-limiting function of pUL50 for nuclear egress, and (ii) demonstrate that mutations in all mapped pUL50 phosphosites may be largely compensated. A refined mechanistic concept points to a multifaceted nuclear egress regulation, for which the dependence on the expression and phosphorylation of pUL50 is discussed.

Background: In Dunnigan-type familial partial lipodystrophy (FPLD) patients are born with normal fat distribution, but subcutaneous fat from extremities and gluteal regions are lost during puberty. The abnormal fat distribution leads to the development of metabolic syndrome (MetS), a cluster of phenotypes including hyperglycemia, dyslipidemia, hypertension, and visceral obesity. The study of FPLD as a monogenic model of MetS may uncover genetic risk factors of the common MetS which affects ~30% of adult North Americans. Two molecular forms of FPLD have been identified including FPLD2, resulting from heterozygous mutations in the LMNA gene, and FPLD3, resulting from both heterozygous dominant negative and haploinsufficiency mutations in the PPARG gene. However, many patients with clinically diagnosed FPLD have no mutation in either LMNA or PPARG, suggesting the involvement of additional genes in FPLD etiology. Methods: Here, we report the results of an Affymetrix 10K GeneChip microarray genome-wide linkage analysis study of a German kindred displaying the FPLD phenotype and no known lipodystrophy-causing mutations. Results: The investigation identified three chromosomal loci, namely 1q, 3p, and 9q, with non-parametric logarithm of odds (NPL) scores >2.7. While not meeting the criteria for genome-wide significance, it is interesting to note that the 1q and 3p peaks contain the LMNA and PPARG genes respectively. Conclusions: Three possible conclusions can be drawn from these results: 1) the peaks identified are spurious findings, 2) additional genes physically close to LMNA, PPARG, or within 9q, are involved in FPLD etiology, or 3) alternative disease causing mechanisms not identified by standard exon sequencing approaches, such as promoter mutations, alternative splicing, or epigenetics, are also responsible for FPLD.

Aim: The 46-gene Prolaris® cell cycle progression test provides information on the risk of prostate cancer progression. Here we developed and validated a 16-gene kit-based version. Methods: RNA was extracted from prostate cancer biopsy tissue. Amplification efficiency, minimum tumor content, repeatability, reproducibility and equivalence with the 46-gene test were evaluated. Results: Amplification efficiencies for all genes were within the acceptable range (90–110%), and samples with ≥50% tumor content were appropriate for the 16-gene test. Results were repeatable (standard deviation: 0.085) and reproducible (standard deviation: 0.115). Instrument, operator and kit lot had minimal impact on results. Cell cycle progression scores from the 46- and 16-gene tests were highly correlated (r = 0.969; bias = 0.217). Conclusion: The 16-gene test performs consistently and similarly to the 46-gene test.