Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE.
Edited by Karin Musier-Forsyth Eukaryotic ribosomal biogenesis is a high-energy-demanding and complex process that requires hundreds of transacting factors to dynamically build the highly-organized 40S and 60S subunits. Each ribonucleoprotein complex comprises specific rRNAs and ribosomal proteins that are organized into functional domains. The RNA exosome complex plays a crucial role as one of the pre-60S-processing factors, because it is the RNase responsible for processing the 7S pre-rRNA to the mature 5.8S rRNA. The yeast pre-60S assembly factor Nop53 has previously been shown to associate with the nucleoplasmic pre-60S in a region containing the "foot" structure assembled around the 3 end of the 7S pre-rRNA. Nop53 interacts with 25S rRNA and with several 60S assembly factors, including the RNA exosome, specifically, with its catalytic subunit Rrp6 and with the exosome-associated RNA helicase Mtr4. Nop53 is therefore considered the adaptor responsible for recruiting the exosome complex for 7S processing. Here, using proteomics-based approaches in budding yeast to analyze the effects of Nop53 on the exosome interactome, we found that the exosome binds pre-ribosomal complexes early during the ribosome maturation pathway. We also identified interactions through which Nop53 modulates exosome activity in the context of 60S maturation and provide evidence that in addition to recruiting the exosome, Nop53 may also be important for positioning the exosome during 7S processing. On the basis of these findings, we propose that the exosome is recruited much earlier during ribosome assembly than previously thought, suggesting the existence of additional interactions that remain to be described.
Meningiomas are among the most common primary tumors of the central nervous system (cnS) and originate from the arachnoid or meningothelial cells of the meninges. Surgery is the first option of treatment, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Reports indicate many differences in meningiomas from male versus female patients; for example, incidence is higher in females, whereas males usually develop the malignant and more aggressive type. With this as motivation, we used shotgun proteomics to compare the proteomic profile of grade I meningioma biopsies of male and female patients. Our results listed several differentially abundant proteins between the two groups; some examples are S100-A4 and proteins involved in RNA splicing events. For males, we identified enriched pathways for cell-matrix organization and for females, pathways related to RNA transporting and processing. We believe our findings contribute to the understanding of the molecular differences between grade I meningiomas of female and male patients. Meningioma is a high incidence tumor that typically emerges at the arachnoid cap or meningothelial cells of the meninges 1 , commonly from intracranial, intraspinal, or orbital locations, and are usually benign, slow-growing tumors 2. Resonance imaging and molecular markers are frequently used for preliminary diagnosis; yet, surgical removal of the tumor is necessary for histological diagnostic confirmation and improved life quality 1,2. When surgery for total removal of the tumor is not feasible, adjuvant radiation may be used 1. The World Health Organization (WHO) classifies meningiomas according to their histopathological characteristics, mitotic count, and brain invasion pattern in (i) grade I, also known as benign meningiomas (BMs, about 80% of termed cases); (ii) grade II, or atypical meningiomas (AMs, 17% of termed cases); and (iii) grade III, the malignant meningiomas (MMs, 3% of termed cases) 3. Although this classification is valid in terms of prognosis, it lacks information about tumor aggressiveness and recurrence rates 2. Controversially, most recurrent meningiomas correspond to BMs; their metabolic phenotype indicates an aggressive metabolism, resembling that of AM 4. Female patients present approximately double the incidence of meningiomas compared to men 1. Interestingly, the main risk factor for meningiomas is related to hormonal changes as these tumors present hormones receptors (i.e., progesterone and estrogen) 5,6. Moreover, association between meningiomas and breast cancer, mainly due to similar hormonal signaling and genetic predisposition, has also been reported 5. The fact that women diagnosed with breast cancer are more likely to develop meningioma may also justify the higher incidence of this tumor in females 7,8. In general, women usually develop the benign form while males develop the malignant type of meningioma, the aggressive grade III 6. Besides gender, other risk factors associated with this disease include...
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