Many genes that are required at specific points in the cell cycle exhibit cell cycle–dependent expression. In the early-diverging model eukaryote and important human pathogen Trypanosoma brucei, regulation of gene expression in the cell cycle and other processes is almost entirely post-transcriptional. Here, we show that the T. brucei RNA-binding protein PUF9 stabilizes certain transcripts during S-phase. Target transcripts of PUF9—LIGKA, PNT1 and PNT2—were identified by affinity purification with TAP-tagged PUF9. RNAi against PUF9 caused an accumulation of cells in G2/M phase and unexpectedly destabilized the PUF9 target mRNAs, despite the fact that most known Puf-domain proteins promote degradation of their target mRNAs. The levels of the PUF9-regulated transcripts were cell cycle dependent, peaking in mid- to late- S-phase, and this effect was abolished when PUF9 was targeted by RNAi. The sequence UUGUACC was over-represented in the 3′ UTRs of PUF9 targets; a point mutation in this motif abolished PUF9-dependent stabilization of a reporter transcript carrying the PNT1 3′ UTR. LIGKA is involved in replication of the kinetoplast, and here we show that PNT1 is also kinetoplast-associated and its over-expression causes kinetoplast-related defects, while PNT2 is localized to the nucleus in G1 phase and redistributes to the mitotic spindle during mitosis. PUF9 targets may constitute a post-transcriptional regulon, encoding proteins involved in temporally coordinated replicative processes in early G2 phase.

Isoenzymes of phosphoglycerate kinase in Trypanosoma brucei are differentially expressed in its two main life stages. This study addresses how the organism manages to make sufficient amounts of the isoenzyme with the correct localization, which processes (transcription, splicing, and RNA degradation) control the levels of mRNAs, and how the organism regulates the switch in isoform expression. For this, we combined new quantitative measurements of phosphoglycerate kinase mRNA abundance, RNA precursor stability, trans splicing, and ribosome loading with published data and made a kinetic computer model. For the analysis of regulation we extended regulation analysis. Although phosphoglycerate kinase mRNAs are present at surprisingly low concentrations (e.g. 12 molecules per cell), its protein is highly abundant. Substantial control of mRNA and protein levels was exerted by both mRNA synthesis and degradation, whereas splicing and precursor degradation had little control on mRNA and protein concentrations. Yet regulation of mRNA levels does not occur by transcription, but by adjusting mRNA degradation. The contribution of splicing to regulation is negligible, as for all cases where splicing is faster than RNA precursor degradation.The flux through a metabolic pathway depends on the kinetic characteristics and concentrations of the constituent enzymes, on the levels of co-enzymes, and on their compartmentation. The concentrations of the enzymes in turn depend on the rates of transcription, processing, nuclear export, translation, degradation of the mRNA, and protein processing and degradation. Because each of these levels can in principle be regulated, the challenge is how to analyze the behavior of such a complex system in terms of the underlying processes.Metabolic control analysis (MCA) 4 and extensions that include gene expression is a powerful approach for the analysis of complex biochemical networks (1-4). In MCA, the control exerted by an enzyme on a concentration of any substance X (5) is quantified by its concentration control coefficient, which is defined as the percentage increase of the steady-state concentration of X that results from a 1% activation of the enzyme of interest. The sum of the concentration control coefficients of all the enzymes in the network is 0. This reflects that (i) activation of some enzymes increases the concentration of X, whereas activation of other enzymes should reduce the concentration of X (these enzymes have negative concentration control coefficients), and (ii) the positive controls together are equal to the negative controls. The principles of MCA do not only apply to metabolic pathways; they can also be used and extended to dissect the control distribution in regulatory pathways beyond steady state (for example see Refs.6 and 7) or gene expression cascades (for example see e.g. Ref. 8). Earlier MCA, as also applied to trypanosomes, has looked more at the control of fluxes, showing that usually flux control coefficients are smaller than 1, because several enzymes partially...

Purpose: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches.Experimental Design: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme.Results: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression.Conclusions: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer. Clin Cancer Res; 16(1); 88-98. ©2010 AACR.The last decades have shown an incidental increase of patients diagnosed with renal cell carcinoma (RCC) with clear cell RCC (ccRCC) being the most frequent and aggressive subtype (1, 2). Patients with local tumors have a significantly better outcome as these cancers can be treated with radical or partial nephrectomy, whereas the 5-year survival rate of metastatic ccRCC remains poor. However, even in patients with organ-confined ccRCC, the 10-year cancer-specific death rates vary from 10% to 38% in pT1a and pT2 tumors, respectively (3).To date, the best available predictor of the postoperative clinical course of localized RCCs is tumor stage at presentation (4, 5). Nevertheless, a significant difference of outcome exists within the same stage. Additional prognostic parameters are routinely used to refine prognosis in RCC patients. After tumor stage, the second most important prognostic parameter is the nuclear differentiation grade (6). Three-and four-tired grading systems are commonly applied for tumor grading. More than 50% of ccRCCs are classified as moderately differentiated, implying an intermediate risk of tumor recurrence (7), which is not informative for the clinician to stratify therapy. Therefo...