A framework based on numerous empirical data, including protein-protein interaction reference sets, provides parameters for assessing the quality and coverage of protein-protein interaction datasets and estimation of the size of the human interactome. Braun et al., also in this issue, use the reference sets to help derive confidence scores for individual protein-protein interactions. Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ∼130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

The HEK293 human cell lineage is widely used in cell biology and biotechnology. Here we use whole-genome resequencing of six 293 cell lines to study the dynamics of this aneuploid genome in response to the manipulations used to generate common 293 cell derivatives, such as transformation and stable clone generation (293T); suspension growth adaptation (293S); and cytotoxic lectin selection (293SG). Remarkably, we observe that copy number alteration detection could identify the genomic region that enabled cell survival under selective conditions (i.c. ricin selection). Furthermore, we present methods to detect human/vector genome breakpoints and a user-friendly visualization tool for the 293 genome data. We also establish that the genome structure composition is in steady state for most of these cell lines when standard cell culturing conditions are used. This resource enables novel and more informed studies with 293 cells, and we will distribute the sequenced cell lines to this effect.

Use of the protein-protein interaction reference sets reported in this issue in Venkatesan et al. to benchmark four complementary protein-protein interaction assays, followed by the training of a logistic regression model, allows the assignment of standardized confidence scores to individual protein-protein interactions. Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

The three-signal paradigm tries to capture how the innate immune system instructs adaptive immune responses in three well-defined actions: (1) presentation of antigenic peptides in the context of MHC molecules, which allows for a specific T cell response; (2) T cell co-stimulation, which breaks T cell tolerance; and (3) secretion of polarizing cytokines in the priming environment, thereby specializing T cell immunity. The three-signal model provides an empirical framework for innate instruction of adaptive immunity, but mainly discusses STAT-dependent cytokines in T cell activation and differentiation, while the multi-faceted roles of type I IFNs and IL-1 cytokine superfamily members are often neglected. IL-1α and IL-1β are pro-inflammatory cytokines, produced following damage to the host (release of DAMPs) or upon innate recognition of PAMPs. IL-1 activity on both DCs and T cells can further shape the adaptive immune response with variable outcomes. IL-1 signaling in DCs promotes their ability to induce T cell activation, but also direct action of IL-1 on both CD4⁺and CD8⁺T cells, either alone or in synergy with prototypical polarizing cytokines, influences T cell differentiation under different conditions. The activities of IL-1 form a direct bridge between innate and adaptive immunity and could therefore be clinically translatable in the context of prophylactic and therapeutic strategies to empower the formation of T cell immunity. Understanding the modalities of IL-1 activity during T cell activation thus could hold major implications for rational development of the next generation of vaccine adjuvants.

Under normal physiological conditions, leptin and the leptin receptor (ObR) regulate the body weight by balancing food intake and energy expenditure. However, this adipocyte derived hormone also directs peripheral processes, including immunity, reproduction, and bone metabolism. Leptin therefore can act as a metabolic switch connecting the body’s nutritional status to high energy consuming processes. We provide an extensive overview of current structural insights on the leptin-ObR interface and ObR activation, coupling to signaling pathways and their negative regulation, and leptin functioning under normal and pathophysiological conditions (obesity, auto-immunity, cancer, …). We also discuss possible cross-talk with other receptor systems on the receptor (extracellular) and signaling cascade (intracellular) levels.