Quantitative Proteomic Analysis in Metastatic Renal Cell Carcinoma Reveals a Unique Set of Proteins with Potential Prognostic Significance

Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant malignancies, and patients have a dismal prognosis, with a <10% five-year survival rate. The identification of markers that can predict the potential for metastases will have a great effect in improving patient outcomes. In this study, we used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify proteins that are differentially expressed in metastatic and primary RCC. We identified 1256 non-redundant proteins, and 456 of these were quantified. Further analysis identified 29 proteins that were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic and primary RCC. Dysregulated protein expressions of profilin-1 (Pfn1), 14–3-3 zeta/delta (14–3-3ζ), and galectin-1 (Gal-1) were verified on two independent sets of tissues by means of Western blot and immunohistochemical analysis. Hierarchical clustering analysis showed that the protein expression profile specific for metastatic RCC can distinguish between aggressive and non-aggressive RCC. Pathway analysis showed that dysregulated proteins are involved in cellular processes related to tumor progression and metastasis. Furthermore, preliminary analysis using a small set of tumors showed that increased expression of Pfn1 is associated with poor outcome and is a potential prognostic marker in RCC. In addition, 14–3-3ζ and Gal-1 also showed higher expression in tumors with poor prognosis than in those with good prognosis. Dysregulated proteins in metastatic RCC represent potential prognostic markers for kidney cancer patients, and a greater understanding of their involved biological pathways can serve as the foundation of the development of novel targeted therapies for metastatic RCC.Renal cell carcinoma (RCC)¹ is the most common neoplasm of the adult kidney. Worldwide incidence and mortality rates of RCC are rising each decade (1). Seventy-five percent of kidney tumors are of the clear cell (ccRCC) subtype (2). Although modern imaging techniques for abdominal screening have led to increased incidental detection of renal tumors (3), unfortunately ∼25% to 30% of patients still have metastases at presentation.The prognosis with RCC is quite variable. The greatest risk of recurrence following nephrectomy is within the first 3 to 5 years (4). The ability to predict which tumors will metastasize would have a significant effect on patient outcomes, because the likelihood of a favorable response to treatment is greater when the metastatic burden is limited, and surgical resection of a single or limited number of metastases can result in longer survival (5). Furthermore, ∼3% of patients will develop a second primary renal tumor, either synchronous or metachronous. Currently, patient prognosis is assessed based on histological parameters and a multivariate analysis developed at Memorial Sloan Kettering (6), but neither is sufficiently accurate. A more accurate assessment of prognosis is urgently needed to better guide patient management.Although surgery can be curative for localized disease, many patients eventually relapse. Metastatic RCC is one of the most treatment-resistant malignancies, with chemotherapy and radiotherapy having limited effect. The five-year survival rate for metastatic RCC is ≤10% (7). Although there has been much progress in RCC treatment with the new era of antiangiogenic therapy, the majority of patients ultimately suffer a relapse and die from progression of the cancer. A more in-depth understanding of the pathogenesis of metastasis will be a cornerstone in the development of new targeted therapies. A number of prognostic markers have previously been identified based on comparative analysis of primary and metastatic tumors, including C-reactive protein, tetraspanin 7, hypoxia-inducible factor 1 α, phos-S6, U3 small nucleolar ribonucleoprotein protein, carbonic anhydrase IX, and microvascular density (8–14). However, no biomarker has yet had an established clinical role independent of stage (15). Differential protein expression between primary RCC and normal tissues was previously studied (16–18). Also, differential expression between primary and metastatic kidney disease has been investigated at the microRNA level (19, 20). Molecular analyses hold the promise of providing a better understanding of the pathogenesis of kidney cancer (21).In this study, we aimed to elucidate the pathogenesis of RCC metastasis through proteomic analysis and to identify potential prognostic markers for kidney cancer. We performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS to identify proteins that were dysregulated in metastatic RCC relative to primary RCC. Differential expressions of selected biologically interesting proteins—profilin-1 (Pfn1), 14–3-3 zeta/delta (14–3-3ζ), and galectin-1 (Gal-1)—were validated on two independent sets of tumors by means of western blot (WB) analysis and immunohistochemistry (IHC). Hierarchical clustering analysis showed that differential protein expression can distinguish between aggressive and non-aggressive tumors. In order to explore the role of these dysregulated proteins in tumor progression, we performed Gene Ontology (GO) and pathway analyses. In addition, we carried out a preliminary analysis to assess the potential of Pfn1, 14–3-3ζ, and Gal-1 as prognostic markers in RCC.     

Concordance of BAI and BMI with DXA in the Newfoundland Population

Background:

Body adiposity index (BAI), indirect method proposed to predict adiposity, was developed using Mexican Americans and very little data are available regarding its validation in Caucasian populations to date.

Objective:

The study objectives were to validate the BAI with dual‐energy X‐ray absorptiometry (DXA) body fat percentage (%BF), taking into consideration the gender and adiposity status.

Design and Methods:

A total of 2,601 subjects (Male 662, Female 1939) from our Complex Diseases in the Newfoundland population: Environment and Genetics (CODING) study participated in this investigation. Pearson correlations, with the entire cohort along with men and women separately, were used to compare the correlation of both BAI and BMI with %BF. Additionally, the concordance between BAI and BMI with %BF were also performed among normal‐weight (NW), overweight (OW), and obese (OB) groups. Adiposity status was determined by the Bray Criteria according to DXA %BF.

Results:

BAI performs better than BMI in our Caucasian population by: (1) reflecting the gender difference in total %BF between women and men, (2) correlating better with DXA %BF than BMI when women and men are combined, and (3) performing better in NW and OW subjects for both the sexes. However, BAI performs less effectively than BMI in OB men and women.

Conclusion:

In summary, the BAI method is a better estimate of adiposity than BMI in non‐OB subjects in our Caucasian population. A measurement sensitive to the changes in adiposity for both men and women is suggested to be incorporated into the present BAI equation to increase accuracy.     

Mapping of Alternaria and Pleospora concentrations in Central Italy using meteorological forecast and neural network estimator

Airborne particles (pollens and fungal spores) are recognized as important causes of allergies and many other pathologies whose main symptoms are usually associated with respiratory problems. In addition, these particles seem to be responsible for clinical symptoms of oculorhinitis and bronchial asthma. Many authors showed how pollen and spore concentrations are critically linked to meteorological conditions, while other studies investigated the possibility to estimate these concentrations through meteorological parameters. So, many different approaches have been proposed, and one of the most sophisticated is based on the use of a complex artificial neural network architecture. Once the neural device is calibrated using simultaneous time series of observed meteorological parameters and airborne biological particles, it is straightforward to use the Neural Network to predict spore concentrations using operational Limited Area Meteorological Model. In a previous work, it has been shown that the MM5 meteorological model developed by National Center for Atmospheric Research and Pennsylvania State University can be coupled with the above-cited neural predictor to provide a good prediction of Alternaria and Pleospora spore in the location of L’Aquila (Central Italy). Following the same approach, this work aims to provide the mapping of spore concentration over a wide area covered by high-resolution meteorological prediction in Central Italy. The complex patterns of fungal spore concentrations in selected areas will be described, and the high temporal variability of such fields will be discussed as well. The possibility to infer useful information from the predicted pattern of spore concentrations is discussed, as an example it appears that for people suffering from allergy to fungal spores is more comfortable to spend summertime close to the east coast of Italian Peninsula respect to the west coast. A further step of this work may easily lead to an operational use of the model for supporting the clinical management of allergies and for establishing a preventive strategy in agriculture to avoid unsafe and useless pollution of atmosphere, crops and fields.     

Microbial Life in the Underworld: Biogenicity in Secondary Mineral Formations

One unresolved issue in geomicrobiology is the involvement of microbial activity in the formation of secondary mineral deposits, or speleothems, in caves. Although there is extensive literature demonstrating the importance of bacteria in the precipitation of calcite in noncave environments, the role that these organisms play within caves remains unclear. Evidence in support of microbial involvement in deposition of speleothems has often not been compelling. Following the "Rules for the Hunt" first proposed by Schopf and Walter to determine whether structures in rock were biogenic in origin, we propose a similar set of guidelines for evaluation of microbial association with cave features. We also illustrate methods that may help unravel the complex problem of microorganism involvement in secondary mineral deposition in caves.     

Terrestrial support of pelagic consumers: patterns and variability revealed by a multilake study

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Long‐term effects of climatic–hydrological drivers on macroinvertebrate richness and composition in two Mediterranean streams

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A class of pyrrole derivatives endowed with analgesic/anti-inflammatory activity

We report the synthesis and bio-pharmacological evaluation of a class of pyrrole derivatives featuring a small appendage fragment (carbaldehyde, oxime, nitrile) on the central core. Compound 1c proved to be extremely effective in vivo, showing an interesting anti-nociceptic profile that is comparable to reference compounds already marketed, hence representing a great stimulus for a further improvement of this class of molecules.     

Global agricultural intensification during climate change: a role for genomics

Agriculture is now facing the ‘perfect storm’ of climate change, increasing costs of fertilizer and rising food demands from a larger and wealthier human population. These factors point to a global food deficit unless the efficiency and resilience of crop production is increased. The intensification of agriculture has focused on improving production under optimized conditions, with significant agronomic inputs. Furthermore, the intensive cultivation of a limited number of crops has drastically narrowed the number of plant species humans rely on. A new agricultural paradigm is required, reducing dependence on high inputs and increasing crop diversity, yield stability and environmental resilience. Genomics offers unprecedented opportunities to increase crop yield, quality and stability of production through advanced breeding strategies, enhancing the resilience of major crops to climate variability, and increasing the productivity and range of minor crops to diversify the food supply. Here we review the state of the art of genomic‐assisted breeding for the most important staples that feed the world, and how to use and adapt such genomic tools to accelerate development of both major and minor crops with desired traits that enhance adaptation to, or mitigate the effects of climate change.