The Annexin A2/S100A10 Complex: The Mutualistic Symbiosis of Two Distinct Proteins.

Mutualistic symbiosis refers to the symbiotic relationship between individuals of different species in which both individuals benefit from the association. S100A10, a member of the S100 family of Ca2+-binding proteins, exists as a tight dimer and binds two annexin A2 molecules. This association forms the annexin A2/S100A10 complex known as AIIt, and modifies the distinct functions of both proteins. Annexin A2 is a Ca2+-binding protein that binds F-actin, phospholipid, RNA, and specific polysaccharides such as heparin. S100A10 does not bind Ca2+, but binds tPA, plasminogen, certain plasma membrane ion channels, neurotransmitter receptors, and the structural scaffold protein, AHNAK. S100A10 relies on annexin A2 for its intracellular survival: in the absence of annexin A2, it is rapidly destroyed by ubiquitin-dependent and independent proteasomal degradation. Annexin A2 requires S100A10 to increase its affinity for Ca2+, facilitating its participation in Ca2+-dependent processes such as membrane binding. S100A10 binds tissue plasminogen activator and plasminogen, and promotes plasminogen activation to plasmin, which is a process stimulated by annexin A2. In contrast, annexin A2 acts as a plasmin reductase and facilitates the autoproteolytic destruction of plasmin. This review examines the relationship between annexin A2 and S100A10, and how their mutualistic symbiosis affects the function of both proteins.

Survival analysis methods for analysis of hospitalization data: Application to COVID-19 patient hospitalization experience

During most of 2020, the COVID-19 pandemic gave rise to considerable and growing numbers of hospitalizations across most of the U.S. Typical COVID-19 hospitalization data, including length of stay, intensive care unit (ICU) use, mechanical ventilation (Vent), and in-hospital mortality provide clearly interpretable health care endpoints that can be compared across population strata. They capture the resources consumed for the care of COVID-19 patients, and analysis of these endpoints can be used for resource planning at the local level. Yet, hospitalization data embody novel features that require careful statistical treatment to be useful in this context. Specifically, statistical models must meet three goals: (i) They should mesh with and inform mathematical epidemiologic or agent-based models of the COVID-19 experience in the population. (ii) They need to handle administrative censoring of hospitalization experience when data are extracted and downloaded for a given patient before that patients hospitalization experience has terminated. And, (iii) models need to handle risks for competing events, the occurrence of one blocking the possibility of the other(s). For example, live discharge from the hospital "competes with" (i.e., blocks) in-hospital mortality. We have adapted approaches from the survival analysis literature to address these challenges in order to better understand and quantify the population experience in hospital with respect to length of stay, ICU, Vent use and so on. Using hospitalization data from a large U.S. metropolitan region, in this report, we show how standard techniques from survival analysis can be brought to bear to address these challenges and yield interpretable results. In the breakout/discussion, we will discuss formulation, estimation and inference, and interpretation of competing risks models.

Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including Frances ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

Selecting pharmacies for COVID-19 testing to ensure access

Rapid diagnostic testing for COVID-19 is key to guiding social distancing orders and containing emerging disease clusters by contact tracing and isolation. However, communities throughout the US do not yet have adequate access to tests. Pharmacies are already engaged in testing, but there is capacity to greatly increase coverage. Using a facility location optimization model and willingness-to-travel estimates from US National Household Travel Survey data, we find that if COVID-19 testing became available in all US pharmacies, an estimated 94% of the US population would be willing to travel to obtain a test, if warranted. Whereas the largest chain provides high coverage in densely populated states, like Massachusetts, Rhode Island, New Jersey, and Connecticut, independent pharmacies would be required for sufficient coverage in Montana, South Dakota, and Wyoming. If only 1,000 pharmacies in the US are selected to provide testing, judicious selection, using our optimization model, provides estimated access to 29 million more people than selecting pharmacies simply based on population density. COVID-19 testing through pharmacies can improve access across the US. Even if only few pharmacies offer testing, judicious selection of specific sites can simplify logistics and improve access.

Annexin A2 is a novel cellular redox regulatory protein involved in tumorigenesis.

Annexins are a structurally related family of calcium and phospholipid-binding proteins that are involved in the regulation of a wide range of molecular and cellular processes. Annexin A2 is unique among the annexins in that it possesses redox sensitive cysteine(s). The ubiquitous and abundant expression of ANXA2 in cells and its reactivity with hydrogen peroxide led us to hypothesize that this protein could play a role in cellular redox regulation. Here we show that ANXA2 protein levels are induced by hydrogen peroxide. Furthermore, depletion of ANXA2 resulted in the elevation of cellular reactive oxygen species (ROS) upon oxidative stress, increased activation of the ROS-induced pro-apoptotic kinases, JNK, p38 and Akt and elevated sensitivity to ROS-mediated cellular damage/death. ANXA2-null mice showed significantly elevated protein oxidation in the liver and lung tissues compared to WT mice. ANXA2 depleted cancer cells showed enhanced cellular protein oxidation concomitant with decreased tumor growth compared to control cancer cells and both the protein oxidation and tumor growth deficit were reversed by the antioxidant N-acetyl cysteine, indicating that ANXA2 redox regulatory function plays a major role in tumorigenesis. Ex-vivo human cancer studies showed that up-regulation of the reduced form of ANXA2 is associated with protection of the tumor proteins from oxidation. In summary, our results indicate that ANXA2 redox regulatory function plays an important role protecting cells from oxidative stress, particularly during tumorigenesis.

Sensitivity of cancer cells to truncated diphtheria toxin.

BACKGROUND: Diphtheria toxin (DT) has been utilized as a prospective anti-cancer agent for the targeted delivery of cytotoxic therapy to otherwise untreatable neoplasia. DT is an extremely potent toxin for which the entry of a single molecule into a cell can be lethal. DT has been targeted to cancer cells by deleting the cell receptor-binding domain and combining the remaining catalytic portion with targeting proteins that selectively bind to the surface of cancer cells. It has been assumed that "receptorless" DT cannot bind to and kill cells. In the present study, we report that "receptorless" recombinant DT385 is in fact cytotoxic to a variety of cancer cell lines. METHODS: In vitro cytotoxicity of DT385 was measured by cell proliferation, cell staining and apoptosis assays. For in vivo studies, the chick chorioallantoic membrane (CAM) system was used to evaluate the effect of DT385 on angiogenesis. The CAM and mouse model system was used to evaluate the effect of DT385 on HEp3 and Lewis lung carcinoma (LLC) tumor growth, respectively. RESULTS: Of 18 human cancer cell lines tested, 15 were affected by DT385 with IC(50) ranging from 0.12-2.8 microM. Furthermore, high concentrations of DT385 failed to affect growth arrested cells. The cellular toxicity of DT385 was due to the inhibition of protein synthesis and induction of apoptosis. In vivo, DT385 diminished angiogenesis and decreased tumor growth in the CAM system, and inhibited the subcutaneous growth of LLC tumors in mice. CONCLUSION: DT385 possesses anti-angiogenic and anti-tumor activity and may have potential as a therapeutic agent.

Tsetse.org

It presents informations about tsetse flies, general information on the biology and control of tsetse, bait technologies such as traps, targets and attractants, socioeconomic questions, environmental questions and impact, insecticide-treated cattle.

Uncovering the topology of configuration space networks.

The configuration space network (CSN) of a dynamical system is an effective approach to represent the ensemble of configurations sampled during a simulation and their dynamic connectivity. To elucidate the connection between the CSN topology and the underlying free-energy landscape governing the system dynamics and thermodynamics, an analytical solution is provided to explain the heavy tail of the degree distribution, neighbor connectivity, and clustering coefficient. This derivation allows us to understand the universal CSN topology observed in systems ranging from a simple quadratic well to the native state of the beta3s peptide and a two-dimensional lattice heteropolymer. Moreover, CSNs are shown to fall in the general class of complex networks described by the fitness model.

S100A10, annexin A2, and annexin a2 heterotetramer as candidate plasminogen receptors.

The defining characteristic of a tumor cell is its ability to escape the constraints imposed by neighboring cells, invade the surrounding tissue and metastasize to distant sites. This invasive property of tumor cells is dependent on activation of proteinases at the cell surface. The serine proteinase plasmin is one of the key proteinases that participate in the pericellular proteolysis associated with the invasive program of tumor cells. The assembly of plasminogen and tissue plasminogen activator at the endothelial cell surface or on the fibrin clot provides a focal point for plasmin generation and therefore plays an important role in maintaining blood fluidity and promoting fibrinolysis. S100A10, a member of the S100 family of Ca2+-binding proteins, is a dimeric protein composed of two 11 kDa subunits. Typically, S100A10 is found in most cells bound to its annexin A2 ligand as the heterotetrameric (S100A10)2(annexin A2)2 complex, AIIt. In addition to an intracellular distribution, S100A10 is present on the extracellular surface of many cells. The carboxyl-terminal lysines of S100A10 bind tPA and plasminogen resulting in the stimulation of tPA-dependent plasmin production. Carboxypeptidases cleave the carboxyl-terminal lysines of S100A10, resulting in a loss of binding and activity. Plasmin binds to S100A10 at a distinct site and the formation of the S100A10-plasmin complex stimulates plasmin autoproteolysis thereby providing a highly localized transient pulse of plasmin activity at the cell surface. The binding of tPA and plasmin to S100A10 also protects against inhibition by physiological inhibitors, PAI-1 and alpha2-antiplasmin, respectively. S100A10 also colocalizes plasminogen with the uPA-uPAR complex thereby localizing and stimulating uPA-dependent plasmin formation to the surface of cancer cells. The loss of S100A10 from the extracellular surface of cancer cells results in a significant loss in plasmin generation. In addition, S100A10 knock-down cells demonstrate a dramatic loss in extracellular matrix degradation and invasiveness as well as reduced metastasis. Annexin A2 plays an important role in plasminogen regulation by controlling the levels of extracellular S100A10 and by acting as a plasmin reductase. The mechanism by which annexin A2 regulates the extracellular levels of S100A10 is unknown. This review highlights the important part that S100A10 plays in plasmin regulation and the role this protein plays in cancer cell invasiveness and metastasis.