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Objective: Even though a vaccine for malaria infections has been under intense study for many years, it has resisted several different lines of attack attempted by biologists. More than half of Plasmodium proteins still remain uncharacterized and therefore cannot be used in clinical trials. The task is further complicated by the metamorphic life-cycle of the parasite, which allows for rapid evolutionary changes and diversity among related strains, thus making precise targeting of the appropriate proteins for vaccination a technical challenge. We propose an automated method for predicting functions for the malaria parasite, which capitalizes on the importance of the intraerythrocytic developmental cycle data and expression changes during its five phases, as determined computationally by our segmentation algorithm., Materials and Methods: Our method combines temporal gene expression profiles with protein-protein interaction data, sequence similarity scores, and metabolic pathway information to produce a set of predicted protein functions that can be used as targets for vaccine development. We use a Bayesian approach, which assigns a probability of having (or not having) a particular function to each protein, given the various sources of evidence. In our method, each data source is represented by either a functional linkage graph or a categorical feature vector., Results and Conclusions: The methods are tested on Plasmodium falciparum, the species responsible for the deadliest malaria infections. The algorithm was able to assign meaningful functions to 628 out of 1439 previously unannotated proteins, which are first-choice candidates for experimental vaccine research. We conclude that analyzing time-course gene expression profiles in separate phases leads to much higher prediction accuracy when compared with Pearson correlation coefficients computed across the time course as a whole. Additionally, we demonstrate that temporal expression profiles alone are able to improve the predictive power of the integrated data.

A key issue in cancer biology is whether oncogenic transformation of different cell types of origin within an adult tissue gives rise to distinct tumour subtypes that differ in their prognosis and/or treatment response. We now show that initiation of prostate tumours in basal or luminal epithelial cells in mouse models results in tumours with distinct molecular signatures that are predictive of human patient outcomes. Furthermore, our analysis of untransformed basal cells reveals an unexpected assay dependence of their stem cell properties in sphere formation and transplantation assays versus genetic lineage tracing during prostate regeneration and adult tissue homeostasis. Although oncogenic transformation of basal cells gives rise to tumours with luminal phenotypes, cross-species bioinformatic analyses indicate that tumours of luminal origin are more aggressive than tumours of basal origin, and identify a molecular signature associated with patient outcome. Our results reveal the inherent plasticity of basal cells, and support a model in which different cells of origin generate distinct molecular subtypes of prostate cancer.

Simple Summary: Almost 90% of prostate cancer metastases occur in bone. The arrival of tumor cells to the homing organ requires a metabolic adaptation to different nutrients and oxygen availability in the new microenvironment to fulfill sustained growth and proliferation rates. In this study, we characterized the alterations in tumor metabolism occurring in human prostate cancer cells when they interact with bone cells. We hypothesized that elucidating how these cells obtain their energy and "building blocks", and that identifying key determinants underlying this phenomenon, could be a promising strategy to halt disease progression. Accordingly, we discovered five genes related to the metabolism of lipids that play a critical role in the survival of metastatic patients, and we established a communication axis between tumor and bone cells that includes bone-secreted collagen and the tumor Protein Kinase A, which drives the early metabolic reprogramming of metastatic cells. Metastatic prostate cancer (PCa) cells soiling in the bone require a metabolic adaptation. Here, we identified the metabolic genes fueling the seeding of PCa in the bone niche. Using a transwell co-culture system of PCa (PC3) and bone progenitor cells (MC3T3 or Raw264.7), we assessed the transcriptome of PC3 cells modulated by soluble factors released from bone precursors. In a Principal Component Analysis using transcriptomic data from human PCa samples (GSE74685), the altered metabolic genes found in vitro were able to stratify PCa patients in two defined groups: primary PCa and bone metastasis, confirmed by an unsupervised clustering analysis. Thus, the early transcriptional metabolic profile triggered in the in vitro model has a clinical correlate in human bone metastatic samples. Further, the expression levels of five metabolic genes (VDR, PPARA, SLC16A1, GPX1 and PAPSS2) were independent risk-predictors of death in the SU2C-PCF dataset and a risk score model built using this lipid-associated signature was able to discriminate a subgroup of bone metastatic PCa patients with a 23-fold higher risk of death. This signature was validated in a PDX pre-clinical model when comparing MDA-PCa-183 growing intrafemorally vs. subcutaneously, and appears to be under the regulatory control of the Protein Kinase A (PKA) signaling pathway. Secretome analyses of conditioned media showcased fibronectin and type-1 collagen as critical bone-secreted factors that could regulate tumoral PKA. Overall, we identified a novel lipid gene signature, driving PCa aggressive metastatic disease pointing to PKA as a potential hub to halt progression. [ABSTRACT FROM AUTHOR]