Differences in lipogenesis and lipolysis in obese and non-obese adult human adipocytes

It has been proposed that differences in adipocyte function and/or metabolism between obese and lean individuals may manifest themselves in functional adipose tissue abnormalities that lead to metabolic disorders in obesity. We studied lipogenesis and lipolysis of omental adipocytes from obese (OB) and non-obese (NOB) humans. The specific activity of the lipogenic marker enzyme G3PDH was 50% lower in total adipocytes of OB compared to that of NOB subjects. Omental adipocytes from OB subjects also had lower basal lipolytic levels, and a lower lipolytic response to beta-adrenergic stimulus. Cholesterol depletion of adipocyte plasma membrane using methyl b-cyclodextrin caused a lipolytic effect on adipocytes of both groups together, but when obese and lean subjects were analyzed separately, the response was significant only in the obese. We present evidence of a different lipogenic and lipolytic profile in obese individuals' omental adipocytes, and propose a relevant role of plasma membrane cholesterol, where the impact of its removal in OB and NOB adipocyte lipolysis differs.     

Efficiency clustering for low-density microarrays and its application to QPCR

Background  

Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency.     

Accelerating code to function: sizing up the protein production line

High-throughput biology has been pioneered by genomics through the application of robotics to expedite DNA-sequencing projects. Advances in high-throughput protein methods are needed to drive the protein production line for high-throughput structural and functional analysis of newly discovered genes. This will require the development and application of a variety of recombinant-protein expression systems to produce the diversity of proteins from both humans and model organisms.     

Sipuleucel-T for the Treatment of Patients With Metastatic Castrate-resistant Prostate Cancer: Considerations for Clinical Practice

Sipuleucel-T treatment is associated with a significant and consistent survival benefit in patients with metastatic castrate-resistant prostate cancer. Most adverse events are infusion related, manageable, and of short duration. Early screening and diagnosis of metastatic disease is important, as the greatest survival benefit may occur in patients with a lower disease burden. The short duration of sipuleucel-T treatment facilitates the use of subsequent therapies. Sipuleucel-T is now being used in the clinic for patients with a lower disease burden. We present our own experience with the use of sipuleucel-T in the setting of a large urology practice.Key words: Metastatic castrate-resistant prostate cancer, Immunotherapy, Sipuleucel-T, Clinical practiceImmunotherapies are designed to redirect the patient’s immune system to recognize and remove cancerous cells. Sipuleucel-T was the first autologous cellular immunotherapy to be approved by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of asymptomatic or minimally symptomatic, metastatic castrate-resistant prostate cancer (mCRPC).^1,2 Other immunotherapies, such as vaccines with different actions (GVAX and PSATRICOM), anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4; ipilimumab), and anti-PD-L1 or -L2 monoclonal antibodies (pidilizumab), are in late-stage clinical development for prostate cancer and may provide clinicians and patients with additional future treatment options. This article provides clinicians with information about the best practices for sipuleucel-T treatment in the clinic, based on currently available data and our experience in a community practice.     

AIR: A batch-oriented web program package for construction of supermatrices ready for phylogenomic analyses

Background  

Large multigene sequence alignments have over recent years been increasingly employed for phylogenomic reconstruction of the eukaryote tree of life. Such supermatrices of sequence data are preferred over single gene alignments as they contain vastly more information about ancient sequence characteristics, and are thus more suitable for resolving deeply diverging relationships. However, as alignments are expanded, increasingly numbers of sites with misleading phylogenetic information are also added. Therefore, a major goal in phylogenomic analyses is to maximize the ratio of information to noise; this can be achieved by the reduction of fast evolving sites.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Ovarian activity and plasma sex steroid levels of Distichodus antonii in relation to environmental conditions in the upper basin of the Congo River

Distichodus antonii is an endemic fish species of the Congo River basin in which the stocks of wild populations are threatened by overfishing pressure. Knowledge of its reproductive biology would be useful in consideration of conservation and management options for the species. Therefore, this study investigated changes in ovarian activity and levels of steroid profiles in wild populations in relation to variation in temperature and rainfall. Adult females (n = 101, body weight of 3 183 ± 14.75 g, SE) were captured monthly over one year (2013–2014). Apart from evaluation of oocyte diameters and gonad developmental stages, gonado-, hepato-, lipososomatic indices (GSI, HSI, LSI) and plasma levels of sex steroids (testosterone-T, estradiol-17β-E2) were determined. The results suggested a synchronous development of oocytes with two annual reproductive seasons over the one-year study. Plasma T and E2 levels peaked during spawning periods likely reflecting active oogenesis. The highest values of morphosomatic indices were observed during the longest rainfall period in September, and were associated with high steroidogenic activity evidenced by increased E2 production. In addition, more vitellogenic oocytes (September and October) were observed during the latter season than during the short rainy season (in May).