Differential Responses in the Growth Hormone-Insulin-Like Growth Factor-1 Axis Following Simulated Military Operational Stress

Click the PDF icon to download the abstract

Simulated Military Operational Stress Negatively Impacts Psychomotor Vigilance and Neurocognitive Biomarkers in Men and Women

Click the PDF icon to download the abstract

Impact of Operational Stress on Motor Evoked Potentials in Military Personnel

Click the PDF icon to download the abstract

Normalization Removes Differences in Contractile Properties and Corticospinal Excitability Between Single- and Multi-Joint Exercises

Click the PDF icon to download the abstract

Similar Corticospinal Excitability in Military Men and Women During Simulated Operational Stress

Click the PDF icon to download the abstrac

Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data

<p>Abstract</p> <p>Background</p> <p>Developing the right drugs for the right patients has become a mantra of drug development. In practice, it is very difficult to identify subsets of patients who will respond to a drug under evaluation. Most of the time, no single diagnostic will be available, and more complex decision rules will be required to define a sensitive population, using, for instance, mRNA expression, protein expression or DNA copy number. Moreover, diagnostic development will often begin with in-vitro cell-line data and a high-dimensional exploratory platform, only later to be transferred to a diagnostic assay for use with patient samples. In this manuscript, we present a novel approach to developing robust genomic predictors that are not only capable of generalizing from in-vitro to patient, but are also amenable to clinically validated assays such as qRT-PCR.</p> <p>Methods</p> <p>Using our approach, we constructed a predictor of sensitivity to dacetuzumab, an investigational drug for CD40-expressing malignancies such as lymphoma using genomic measurements of cell lines treated with dacetuzumab. Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor. In this way, we constructed several predictors that we validated on an independent DLBCL patient dataset. Similar analyses were performed on genomic measurements of breast cancer cell lines and patients to construct a predictor of estrogen receptor (ER) status.</p> <p>Results</p> <p>The best dacetuzumab sensitivity predictors involved ten or fewer genes and accurately classified lymphoma patients by their survival and known prognostic subtypes. The best ER status classifiers involved one or two genes and led to accurate ER status predictions more than 85% of the time. The novel method we proposed performed as well or better than other methods evaluated.</p> <p>Conclusions</p> <p>We demonstrated the feasibility of combining feature selection techniques with classification methods to develop assays using cell line genomic measurements that performed well in patient data. In both case studies, we constructed parsimonious models that generalized well from cell lines to patients.</p

Activation of TORC1 transcriptional coactivator through MEKK1-induced phosphorylation

CREB is a prototypic bZIP transcription factor and a master regulator of glucose metabolism, synaptic plasticity, cell growth, apoptosis, and tumorigenesis. Transducers of regulated CREB activity (TORCs) are essential transcriptional coactivators of CREB and an important point of regulation on which various signals converge. In this study, we report on the activation of TORC1 through MEKK1-mediated phosphorylation. MEKK1 potently activated TORC1, and this activation was independent of downstream effectors MEK1/MEK2, ERK2, JNK, p38, protein kinase A, and calcineurin. MEKK1 induced phosphorylation of TORC1 both in vivo and in vitro. Expression of the catalytic domain of MEKK1 alone in cultured mammalian cells sufficiently caused phosphorylation and subsequent activation of TORC1. MEKK1 physically interacted with TORC1 and stimulated its nuclear translocation. An activation domain responsive to MEKK1 stimulation was mapped to amino acids 431-650 of TORC1. As a physiological activator of CREB, interleukin 1α triggered MEKK1-dependent phosphorylation of TORC1 and its consequent recruitment to the cAMP response elements in the interleukin 8 promoter. Taken together, our findings suggest a new mechanism for regulated activation of TORC1 transcriptional coactivator and CREB signaling. © 2008 by The American Society for Cell Biology.published_or_final_versio

Rasd1 Modulates the Coactivator Function of NonO in the Cyclic AMP Pathway

All living organisms exhibit autonomous daily physiological and behavioural rhythms to help them synchronize with the environment. Entrainment of circadian rhythm is achieved via activation of cyclic AMP (cAMP) and mitogen-activated protein kinase signaling pathways. NonO (p54nrb) is a multifunctional protein involved in transcriptional activation of the cAMP pathway and is involved in circadian rhythm control. Rasd1 is a monomeric G protein implicated to play a pivotal role in potentiating both photic and nonphotic responses of the circadian rhythm. In this study, we have identified and validated NonO as an interacting partner of Rasd1 via affinity pulldown, co-immunoprecipitation and indirect immunofluorescence studies. The GTP-hydrolysis activity of Rasd1 is required for the functional interaction. Functional interaction of Rasd1-NonO in the cAMP pathway was investigated via reporter gene assays, chromatin immunoprecipitation and gene knockdown. We showed that Rasd1 and NonO interact at the CRE-site of specific target genes. These findings reveal a novel mechanism by which the coregulator activity of NonO can be modulated