Expression Profiling of PBMC-based Diagnostic Gene Markers Isolated from Vasculitis Patients

Vasculitis (angiitis) is a systemic autoimmune disease that often causes fatal symptoms. We aimed to isolate cDNA markers that would be useful for diagnosing not only vasculitis but also other autoimmune diseases. For this purpose, we used stepwise subtractive hybridization and cDNA microarray analyses to comprehensively isolate the genes whose expressions are augmented in peripheral blood mononuclear cells (PBMCs) pooled from vasculitis patients. Subsequently, we used quantitative real-time polymerase chain reaction (qRT–PCR) to examine the mRNA levels of each candidate gene in individual patients. These analyses indicated that seven genes exhibit remarkably augmented expression in many vasculitis patients. Of these genes, we analyzed G0/G1 switch gene 2 (G0S2) further because G0S2 expression is also enhanced in the PBMCs of patients with systemic lupus erythematodes (SLE). We generated G0S2 transgenic mice that ubiquitously overexpress human G0S2. Although we did not observe any obvious vasculitis-related histopathologic findings in these mice, these mice are unhealthy as they produce only few offspring and showed elevated serum levels of two autoimmunity-related antibodies, anti-nuclear antibody, and anti-double strand DNA antibody. Thus, our large-scale gene profiling study may help finding sensitive and specific DNA markers for diagnosing autoimmune diseases including vasculitis and SLE

Oncolytic reovirus as a combined antiviral and anti-tumour agent for the treatment of liver cancer

Objective: Oncolytic viruses (OVs) represent promising, proinflammatory cancer treatments. Here, we explored whether OV-induced innate immune responses could simultaneously inhibit HCV while suppressing hepatocellular carcinoma (HCC). Furthermore, we extended this exemplar to other models of virus-associated cancer. Design and results: Clinical grade oncolytic orthoreovirus (Reo) elicited innate immune activation within primary human liver tissue in the absence of cytotoxicity and independently of viral genome replication. As well as achieving therapy in preclinical models of HCC through the activation of innate degranulating immune cells, Reo-induced cytokine responses efficiently suppressed HCV replication both in vitro and in vivo. Furthermore, Reo-induced innate responses were also effective against models of HBV-associated HCC, as well as an alternative endogenous model of Epstein–Barr virus-associated lymphoma. Interestingly, Reo appeared superior to the majority of OVs in its ability to elicit innate inflammatory responses from primary liver tissue. Conclusions: We propose that Reo and other select proinflammatory OV may be used in the treatment of multiple cancers associated with oncogenic virus infections, simultaneously reducing both virus-associated oncogenic drive and tumour burden. In the case of HCV-associated HCC (HCV-HCC), Reo should be considered as an alternative agent to supplement and support current HCV-HCC therapies, particularly in those countries where access to new HCV antiviral treatments may be limited

Adaptation to ER Stress Is Mediated by Differential Stabilities of Pro-Survival and Pro-Apoptotic mRNAs and Proteins

The accumulation of unfolded proteins in the endoplasmic reticulum (ER) activates a signaling cascade known as the unfolded protein response (UPR). Although activation of the UPR is well described, there is little sense of how the response, which initiates both apoptotic and adaptive pathways, can selectively allow for adaptation. Here we describe the reconstitution of an adaptive ER stress response in a cell culture system. Monitoring the activation and maintenance of representative UPR gene expression pathways that facilitate either adaptation or apoptosis, we demonstrate that mild ER stress activates all UPR sensors. However, survival is favored during mild stress as a consequence of the intrinsic instabilities of mRNAs and proteins that promote apoptosis compared to those that facilitate protein folding and adaptation. As a consequence, the expression of apoptotic proteins is short-lived as cells adapt to stress. We provide evidence that the selective persistence of ER chaperone expression is also applicable to at least one instance of genetic ER stress. This work provides new insight into how a stress response pathway can be structured to allow cells to avert death as they adapt. It underscores the contribution of posttranscriptional and posttranslational mechanisms in influencing this outcome

SecA, a remarkable nanomachine

Biological cells harbor a variety of molecular machines that carry out mechanical work at the nanoscale. One of these nanomachines is the bacterial motor protein SecA which translocates secretory proteins through the protein-conducting membrane channel SecYEG. SecA converts chemically stored energy in the form of ATP into a mechanical force to drive polypeptide transport through SecYEG and across the cytoplasmic membrane. In order to accommodate a translocating polypeptide chain and to release transmembrane segments of membrane proteins into the lipid bilayer, SecYEG needs to open its central channel and the lateral gate. Recent crystal structures provide a detailed insight into the rearrangements required for channel opening. Here, we review our current understanding of the mode of operation of the SecA motor protein in concert with the dynamic SecYEG channel. We conclude with a new model for SecA-mediated protein translocation that unifies previous conflicting data

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery