TorsinA Is Functionally Associated with Spermatogenesis

Quantitative phosphoproteomic analysis reveals unique cAMP signaling pools emanating from AC2 and AC6 in human airway smooth muscle cells

Human airway smooth muscle (HASM) is the primary target of ßAR agonists used to control airway hypercontractility in asthma and chronic obstructive pulmonary disease (COPD). ßAR agonists induce the production of cAMP by adenylyl cyclases (ACs), activate PKA and cause bronchodilation. Several other G-protein coupled receptors (GPCR) expressed in human airway smooth muscle cells transduce extracellular signals through cAMP but these receptors elicit different cellular responses. Some G-protein coupled receptors couple to distinct adenylyl cyclases isoforms with different localization, partly explaining this compartmentation, but little is known about the downstream networks that result. We used quantitative phosphoproteomics to define the downstream signaling networks emanating from cAMP produced by two adenylyl cyclases isoforms with contrasting localization in uman airway smooth muscle. After a short stimulus of adenylyl cyclases activity using forskolin, phosphopeptides were analyzed by LC-MS/MS and differences between cells overexpressing AC2 (localized in non-raft membranes) or AC6 (localized in lipid raft membranes) were compared to control human airway smooth muscle. The degree of AC2 and AC6 overexpression was titrated to generate roughly equal forskolin-stimulated cAMP production. 14 Differentially phosphorylated proteins (DPPs) resulted from AC2 activity and 34 differentially phosphorylated proteins resulted from AC6 activity. Analysis of these hits with the STRING protein interaction tool showed that AC2 signaling is more associated with modifications in RNA/DNA binding proteins and microtubule/spindle body proteins while AC6 signaling is associated with proteins regulating autophagy, calcium-calmodulin (Ca2+/CaM) signaling, Rho GTPases and cytoskeletal regulation. One protein, OFD1, was regulated in opposite directions, with serine 899 phosphorylation increased in the AC6 condition 1.5-fold but decreased to 0.46-fold by AC2. In conclusion, quantitative phosphoproteomics is a powerful tool for deciphering the complex signaling networks resulting from discreet signaling events that occur in cAMP compartments. Our data show key differences in the cAMP pools generated from AC2 and AC6 activity and imply that distinct cellular responses are regulated by these two compartments.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Powerful quantifiers for cancer transcriptomics

Every day, investigators find a new link between a form of cancer and a particular alteration in the sequence or/and expression level of a key gene, awarding this gene the title of “biomarker”. The clinician may choose from numerous available panels to assess the type of cancer based on the mutation or expression regulation (“transcriptomic signature”) of “driver” genes. However, cancer is not a “one-gene show” and, together with the alleged biomarker, hundreds other genes are found as mutated or/and regulated in cancer samples. Regardless of the platform, a well-designed transcriptomic study produces three independent features for each gene: Average expression level, expression variability and coordination with expression of each other gene. While the average expression level is used in all studies to identify what genes were up-/down-regulated or turn on/off, the other two features are unfairly ignored. We use all three features to quantify the transcriptomic change during the progression of the disease and recovery in response to a treatment. Data from our published microarray experiments on cancer nodules and surrounding normal tissue from surgically removed tumors prove that the transcriptomic topologies are not only different in histopathologically distinct regions of a tumor but also dynamic and unique for each human being. We show also that the most influential genes in cancer nodules [the Gene Master Regulators (GMRs)] are significantly less influential in the normal tissue. As such, “smart” manipulation of the cancer GMRs expression may selectively kill cancer cells with little consequences on the normal ones. Therefore, we strongly recommend a really personalized approach of cancer medicine and present the experimental procedure and the mathematical algorithm to identify the most legitimate targets (GMRs) for gene therapy.