A new obligate CXCL4-CXCL12 heterodimer for studying chemokine heterodimer activities and mechanisms.

Chemokines form a family of proteins with critical roles in many biological processes in health and disease conditions, including cardiovascular, autoimmune diseases, infections, and cancer. Many chemokines engage in heterophilic interactions to form heterodimers, leading to synergistic activity enhancement or reduction dependent on the nature of heterodimer-forming chemokines. In mixtures, different chemokine species with diverse activities coexist in dynamic equilibrium, leading to the observation of their combined response in biological assays. To overcome this problem, we produced a non-dissociating CXCL4-CXCL12 chemokine heterodimer OHD4-12 as a new tool for studying the biological activities and mechanisms of chemokine heterodimers in biological environments. Using the OHD4-12, we show that the CXCL4-CXCL12 chemokine heterodimer inhibits the CXCL12-driven migration of triple-negative MDA-MB-231 breast cancer cells. We also show that the CXCL4-CXCL12 chemokine heterodimer binds and activates the CXCR4 receptor.

Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects.

Failure prognostics has become a central element in predictive maintenance. In this domain, the accurate determination of the remaining useful life (RUL) allows making effective maintenance and operation decisions about the assets. However, prognostics is often approached from a component point of view, and system-level prognostics, taking into account component interactions and mission profile effects, is still an underexplored area. To address this issue, we propose an online joint estimation and prediction methodology using a modeling framework based on the inoperability input-output model (IIM). This model can consider the interactions between components and also the mission profile effects on a system's degradation. To estimate the system's parameters in real-time, with a minimum of prior knowledge, an online estimation process based on the gradient descend algorithm is recursively performed when acquiring new measurements. After each update, the estimated model is used to predict the system RUL. The performance of the proposed approach is highlighted through different numerical examples. In addition, these developments are applied to a real industrial application, the Tennessee Eastman Process, in order to show their effectiveness.

An automated health indicator construction methodology for prognostics based on multi-criteria optimization.

In recent years, the development of autonomous health management systems received increasing attention from worldwide companies to improve their performances and avoid downtime losses. This can be done, in the first step, by constructing powerful health indicators (HI) from intelligent sensors for system monitoring and for making maintenance decisions. In this context, this paper aims to develop a new methodology that allows automatically choosing the pertinent measurements among various sources and also handling raw data from high-frequency sensors to extract the useful low-level features. Then, it combines these features to create the most appropriate HI following the previously defined multiple evaluation criteria. Thanks to the flexibility of the genetic programming, the proposed methodology does not require any expertise knowledge about system degradation trends but allows easily integrating this information if available. Its performance is then verified on two real application case studies. In addition, an insightful overview on HI evaluation criteria is also discussed in this paper.

CXCL12-CXCL4 heterodimerization prevents CXCL12-driven breast cancer cell migration.

Despite improvements in cancer early detection and treatment, metastatic breast cancer remains deadly. Current therapeutic approaches have very limited efficacy in patients with triple negative breast cancer. Among the many mechanisms associated that contribute to cancer progression, signaling through the CXCL12-CXCR4 is an essential step in cancer cell migration. We previously demonstrated the formation of CXCL12-CXCL4 heterodimers (Carlson et al., 2013). Here, we investigated whether CXCL12-CXCL4 heterodimers alter tumor cell migration. CXCL12 alone dose-dependently promoted the MDA-MB 231 cell migration (p < .05), which could be prevented by blocking the CXCR4 receptor. The addition of CXCL4 inhibited the CXCL12-induced cell migration (p < .05). Using NMR spectroscopy, we identified the CXCL4-CXCL12 binding interface. Moreover, we generated a CXCL4-derived peptide homolog of the binding interface that mimicked the activity of native CXCL4 protein. These results confirm the formation of CXCL12-CXCL4 heterodimers and their inhibitory effects on the migration of breast tumors cells. These findings suggest that specific peptides mimicking heterodimerization of CXCL12 might prevent breast cancer cell migration.