A novel host-proteome signature for distinguishing between acute bacterial and viral infections.

Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P<0.001), as well as routinely used clinical parameters and their combinations (P<0.001). It remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse.

Antigen-dependent integration of opposing proximal TCR-signaling cascades determines the functional fate of T lymphocytes.

T cell anergy is a key tolerance mechanism to mitigate unwanted T cell activation against self by rendering lymphocytes functionally inactive following Ag encounter. Ag plays an important role in anergy induction where high supraoptimal doses lead to the unresponsive phenotype. How T cells "measure" Ag dose and how this determines functional output to a given antigenic dose remain unclear. Using multiparametric phospho-flow and mass cytometry, we measured the intracellular phosphorylation-dependent signaling events at a single-cell resolution and studied the phosphorylation levels of key proximal human TCR activation- and inhibition-signaling molecules. We show that the intracellular balance and signal integration between these opposing signaling cascades serve as the molecular switch gauging Ag dose. An Ag density of 100 peptide-MHC complexes/cell was found to be the transition point between dominant activation and inhibition cascades, whereas higher Ag doses induced an anergic functional state. Finally, the neutralization of key inhibitory molecules reversed T cell unresponsiveness and enabled maximal T cell functions, even in the presence of very high Ag doses. This mechanism permits T cells to make integrated "measurements" of Ag dose that determine subsequent functional outcomes.

Mixed alkanethiol monolayers on submicrometric gold patterns: a controlled platform for studying cell-ligand interactions.

Nanoscale organization of surface ligands often has a critical effect on cell-surface interactions. We have developed an experimental system that allows a high degree of control over the 2-D spatial distribution of ligands. As a proof of concept, we used the developed system to study how T-cell activation is independently affected by antigen density and antigen amount per cell. Arrays of submicrometer gold islands at varying surface coverage were defined on silicon by electron beam lithography (EBL). The gold islands were functionalized with alkanethiol self-assembled monolayers (SAMs) containing a small antigen, 2,4,6-trinotrophenyl (TNP), at various densities. Genetically engineered T-cell hybridomas expressing TNP-specific chimeric T-cell antigen receptor (CAR) were cultured on the SAMs, and their activation was assessed by IL-2 secretion and CD69 expression. It was found that, at constant antigen density, activation increased monotonically with the amount of antigen, while at constant antigen amount activation was maximal at an intermediate antigen density, whose value was independent of the amount of antigen.

Electrically controlled molecular recognition harnessed to activate a cellular response.

Seamless embedment of electronic devices in biological systems is expected to add the outstanding computing power, memory, and speed of electronics to the biochemical toolbox of nature. Such amalgamation requires transduction of electronic signals into biochemical cues that affect cells. Inspired by biology, where pathways are directed by molecular recognition, we propose and demonstrate a generic electrical-to-biological transducer comprising a two-state electronic antigen and a chimeric cell receptor engineered to bind the antigen exclusively in its "on" state. T-cells expressing these receptors remain inactivated with the antigen in its "off" state. Switching the antigen to its "on" state by an electrical signal leads to its recognition by the T-cells and correspondingly to cell activation.

Predicting and controlling the reactivity of immune cell populations against cancer.

Heterogeneous cell populations form an interconnected network that determine their collective output. One example of such a heterogeneous immune population is tumor-infiltrating lymphocytes (TILs), whose output can be measured in terms of its reactivity against tumors. While the degree of reactivity varies considerably between different TILs, ranging from null to a potent response, the underlying network that governs the reactivity is poorly understood. Here, we asked whether one can predict and even control this reactivity. To address this we measured the subpopulation compositions of 91 TILs surgically removed from 27 metastatic melanoma patients. Despite the large number of subpopulations compositions, we were able to computationally extract a simple set of subpopulation-based rules that accurately predict the degree of reactivity. This raised the conjecture of whether one could control reactivity of TILs by manipulating their subpopulation composition. Remarkably, by rationally enriching and depleting selected subsets of subpopulations, we were able to restore anti-tumor reactivity to nonreactive TILs. Altogether, this work describes a general framework for predicting and controlling the output of a cell mixture.