Pattern analysis of stem cell differentiation during <Emphasis Type="Italic">in vitro Arabidopsis</Emphasis> organogenesis

Plant somatic cells have the capability to switch their cell fates from differentiated to undifferentiated status under proper culture conditions, which is designated as totipotency. As a result, plant cells can easily regenerate new tissues or organs from a wide variety of explants. However, the mechanism by which plant cells have such remarkable regeneration ability is still largely unknown. In this study, we used a set of meristem-specific marker genes to analyze the patterns of stem cell differentiation in the processes of somatic embryogenesis as well as shoot or root organogenesis in vitro. Our studies furnish preliminary and important information on the patterns of the de novo stem cell differentiation during various types of in vitro organogenesis.     

Engineering lysine‐rich caleosins as carrier proteins to render biotin as a hapten on artificial oil bodies for antibody production

It has been demonstrated that caleosin alone is sufficient to stabilize artificial oil bodies. A series of recombinant caleosins, mutated with 3, 5, 8, 11, 13, 15, and 17 extra Lys residues and over‐expressed in Escherichia coli, were used as carrier proteins to render biotin as a hapten on the surface of artificial oil bodies for antibody production. Biotinylation levels of the recombinant caleosins were step‐wisely elevated as the number of extra Lys residues increased, and the biotinylated Lys residues were identified by mass spectrometric analysis. Polyclonal antibodies against biotin were successfully generated in rats injected with artificial oil bodies constituted with each of the biotinylated caleosins. Moreover, those generated via the biotinylated caleosins with eight or more extra Lys residues no longer recognized caleosin. It appears that engineered Lys‐rich caleosins are suitable carrier proteins for the production of antibodies against small molecules. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011     

Rapid determination of substrate specificity of Clostridium histolyticum beta-collagenase using an immobilized peptide library.

The molecular basis of the substrate specificity of Clostridium histolyticum beta-collagenase was investigated using a combinatorial method. An immobilized positional peptide library, which contains 24,000 sequences, was constructed with a 7-hydroxycoumarin-4-propanoyl (Cop) fluorescent group attached at the N terminus of each sequence. This immobilized peptide library was incubated with C. histolyticum beta-collagenase, releasing fluorogenic fragments in the solution phase. The relative substrate specificity (k(cat)/K(m)) for each member of the library was determined by measuring fluorescence intensity in the solution phase. Edman sequencing was used to assign structure to subsites of active substrate mixtures. Collectively, the substrate preference for subsites (P(3)-P(4)') of C. histolyticum beta-collagenase was determined. The last position on the C-terminal side in which the identity of the amino acids affects the activity of the enzyme is P(4)', and an aromatic side chain is preferred in this position. The optimal P(1)'-P(3)' extended substrate sequence is P(1)'-Gly/Ala, P(2)'-Pro/Xaa, and P(3)'-Lys/Arg/Pro/Thr/Ser. The Cop group in either the P(2) or P(3) position is required for a high substrate activity with C. histolyticum beta-collagenase. S(2) and S(3) sites of the protease play a dominant role in fixing the substrate specificity. The immobilized peptide library proved to be a powerful approach for assessing the substrate specificity of C. histolyticum beta-collagenase, so it may be applied to the study of other proteases of interest.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Solution structure of the N‐terminal domain of DC‐UbP/UBTD2 and its interaction with ubiquitin

DC‐UbP/UBTD2 is a ubiquitin (Ub) domain‐containing protein first identified from dendritic cells, and is implicated in ubiquitination pathway. The solution structure and backbone dynamics of the C‐terminal Ub‐like (UbL) domain were elucidated in our previous work. To further understand the biological function of DC‐UbP, we then solved the solution structure of the N‐terminal domain of DC‐UbP (DC‐UbP_N) and studied its Ub binding properties by NMR techniques. The results show that DC‐UbP_N holds a novel structural fold and acts as a Ub‐binding domain (UBD) but with low affinity. This implies that the DC‐UbP protein, composing of a combination of both UbL and UBD domains, might play an important role in regulating protein ubiquitination and delivery of ubiquitinated substrates in eukaryotic cells.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.