Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks

Although recent developments in MS have enabled the identification and quantification of hundreds of phosphorylation sites from a given biological sample, phosphoproteome analysis by MS has been plagued by inconsistent reproducibility arising from automated selection of precursor ions for fragmentation, identification, and quantification. To address this challenge, we have developed a new MS-based strategy, based on multiple reaction monitoring of stable isotope-labeled peptides, that enables highly reproducible quantification of hundreds of nodes (phosphorylation sites) within a signaling network and across multiple conditions simultaneously. We have applied this strategy to quantify temporal phosphorylation profiles of 222 tyrosine phosphorylated peptides across seven time points following EGF treatment, including 31 tyrosine phosphorylation sites not previously known to be regulated by EGF stimulation. With this approach, 88% of the signaling nodes were reproducibly quantified in four analyses, as compared with only 34% by typical information-dependent analysis. As a result of the improved reproducibility, full temporal phosphorylation profiles were generated for an additional 104 signaling nodes with the multiple reaction monitoring strategy, an 88% increase in our coverage of the signaling network. This method is broadly applicable to multiple signaling networks and to a variety of samples, including quantitative analysis of signaling networks in clinical samples. Using this approach, it should now be possible to routinely monitor the phosphorylation status of hundreds of nodes across multiple biological conditions.     

Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive brain tumor in adults and remains incurable despite multimodal intensive treatment regimens. EGFRvIII is a truncated extracellular mutant of the EGF receptor (EGFR) commonly found in GBMs that confers enhanced tumorigenic behavior. To gain a molecular understanding of the mechanisms by which EGFRvIII acts, we have performed a large-scale analysis of EGFRvIII-activated phosphotyrosine-mediated signaling pathways and thereby have identified and quantified 99 phosphorylation sites on 69 proteins. Distinct signaling responses were observed as a function of titrated EGFRvIII receptor levels with the phosphatidylinositol 3-kinase pathway being dominant over the MAPK and STAT3 pathways at a high level of EGFRvIII expression. Within this data set, the activating phosphorylation site on the c-Met receptor was found to be highly responsive to EGFRvIII levels, indicating cross-activation of the c-Met receptor tyrosine kinase by EGFRvIII. To determine the significance of this finding, we devised a combined treatment regimen that used a c-Met kinase inhibitor and either an EGFR kinase inhibitor or cisplatin. This regimen resulted in enhanced cytotoxicity of EGFRvIII-expressing cells compared with treatment with either compound alone. These results suggest that the clinical use of c-Met kinase inhibitors in combination with either EGFR inhibitors or standard chemotherapeutics might represent a previously undescribed therapeutic approach to overcome the observed chemoresistance in patients with GBMs expressing EGFRvIII.     

Emergence of complex dynamics in a simple model of signaling networks

Various physical, social, and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by 1/f scaling of their power spectra, can emerge in networks of simple signaling units. We found that, under general conditions, complex dynamics can be generated by systems fulfilling the following two requirements, (i) a "small-world" topology and (ii) the presence of noise. Our findings support two notable conclusions. First, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions i and ii are robust to some degree of degradation (i.e., they will still be able to generate 1/f dynamics).     

Strategic identity signaling in heterogeneous networks

Individuals often signal identity information to facilitate assortment with partners who are likely to share norms, values, and goals. However, individuals may also be incentivized to encrypt their identity signals to avoid detection by dissimilar receivers, particularly when such detection is costly. Using mathematical modeling, this idea has previously been formalized into a theory of covert signaling. In this paper, we provide an empirical test of the theory of covert signaling in the context of political identity signaling surrounding the 2020 US presidential elections. To identify likely covert and overt signals on Twitter, we use methods relying on differences in detection between ingroup and outgroup receivers. We strengthen our experimental predictions with additional mathematical modeling and examine the usage of selected covert and overt tweets in a behavioral experiment. We find that participants strategically adjust their signaling behavior in response to the political constitution of their audiences. These results support our predictions and point to opportunities for further theoretical development. Our findings have implications for our understanding of political communication, social identity, pragmatics, hate speech, and the maintenance of cooperation in diverse populations.

Individuals constantly emit signals of their identity, consciously and unconsciously, informing others about the sort of person they are. Identity signals are any components of communication that inform receivers of the signaler’s membership (or nonmembership) in a subset of individuals (1–3). Such subsets can reflect strong social boundaries, such as “Republican” or “Democrat” in the United States, or reflect subtler intragroup variations, such as differences among Democrats regarding government regulations. In large, multicultural nations like the United States, identities such as Republican or Democrat can serve to organize like-minded communities or coalitions (4–7). Although the specific style of communication may vary with cultural context (8), identity signaling serves a key social function by enabling individuals to rapidly characterize others as similar or dissimilar (2, 3, 9). Finding similar others has many proximate psychological benefits, such as better mental health (10) and the security that results from a stronger sense of group identity (11, 12). Our emphasis here is on the role of identity signaling to facilitate social assortment: preferentially interacting with similar individuals and reaping the benefits of coordinating on norms, goals, and values (2, 3, 9, 13–15).Identity signaling is especially important in vast and diverse social communities, in which little can be assumed about strangers in the absence of identity information. This type of scenario is made all the more common in the digital age (16). In recent years, online social media has both expanded the pool of potential partners and enabled easier formation of communities across traditional, social, and geographic boundaries. This presents new challenges and opportunities for signalers to successfully find niche communities (17, 18). On the one hand, large online communities have arisen dedicated to worldviews that are otherwise rare in most local communities. An individual expressing a viewpoint that is rare in their locality can nevertheless become part of a flourishing, geographically dilute collective. On the other hand, online signaling also carries new risks that come from expanding one’s audience far beyond one’s local social network, sometimes without the signaler’s knowledge (e.g., ref. 19).Given the social importance of political identity in the United States and other countries (5, 20), we expect much identity signaling to be about political views and related coalitional affiliations. Political views are often expressed on social media using obvious signals like slogans, partisan memes, and other declarations of partisanship. However, the United States is also highly polarized (5, 6), and obvious political signals are not always advisable. Partisans often hold deeply negative feelings toward members of groups perceived as opposed or even simply different to their own (21–24). Signaling one’s political affiliation to outgroup members can therefore be costly, with costs ranging from the loss of social standing or relationship opportunities to the loss of an employment (25) or even becoming the victim of violence (26). For example, Van Duyn (27) documents a group of anti-Trump women in rural Texas who met in secret to discuss politics because they feared negative consequences for their business or marriages if their views became known. Exactly who is considered a member of one’s outgroup also varies over time and context. In the context of political identities, debates during US presidential primaries tend to be between members of the same political party, and so, a perceived outgroup may be copartisans that support different candidates or policy goals. During national presidential elections, cross-partisan differences become more salient. In both cases, the assortative benefits of overt identity signaling must be weighed against the potential costs of being identified by outgroup individuals in situations in which identification has consequences.Overt, unambiguous signals of identity are useful when individuals can sufficiently benefit from their role in supporting positive assortment—preferentially interacting with similar others. A wide literature on social tags and ethnic markers has documented and modeled the utility and likely emergence of such signals for this purpose (9, 13, 28–31). The benefits of overt identity signaling, however, must sufficiently outweigh any risks that come from alienating others or revealing oneself to be misaligned with their interests. If those risks and their associated costs are high enough, we should expect cultural or psychological processes (such as cultural evolution or strategic decision-making) to favor subtler signaling strategies that encode information in such a way that it is detectable only by those who share relevant worldviews.We refer to identity signals that are accurately received by their intended audience but obscured when received by others as covert signals (3, 32, 33). Covert signals allow individuals to reap at least some benefit from being identified by similar others, when possible, while simultaneously avoiding the costs associated with detection by dissimilar others. Covert signals work because communication often contains multiple, simultaneous layers of meaning, which are not all equally apparent to all receivers. A receiver’s background knowledge and perception of context affects whether and how those layers will be revealed (34–37). Individuals should, therefore, adjust their patterns of communication, based not only on their intended audience but also on the likely third parties that will perceive those communications (37–39). Loury (39) captures the essence of the idea: “If the significance of some words as signals of belief is known only to ‘insiders,’ their use in public allows the speaker to convey a reassuring message to some listener—‘I share your values’—without alarming the others.” A well-documented example is the routine remarks made by former US president George W. Bush concerning his opposition to the 1857 Dred Scott Supreme Court decision. While it might seem unnoteworthy to oppose a judicial decision that upheld slavery, the mention was seen by many evangelical conservatives as morally analogous to the 1973 Roe v. Wade case that upheld the right to abortion and so subtly communicated to these audiences the president’s commitment to overturning that decision (40).Recently, Smaldino and colleagues have developed a theory of covert signaling, using formal mathematical and agent-based models of cultural evolution to examine the circumstances under which overt or covert identity-signaling strategies should be favored (32, 33). The theory of covert signaling provides a formalism for identity signaling in the context of third-party receivers and describes how signalers should communicate based on their likely audiences and the consequences for both successful and failed communication. The models derived from this theory make general predictions about strategies for identity signaling related to both the ability of individuals to preferentially assort with similar others and the costs of failing to assort accordingly. Covert signaling can achieve higher payoffs than overt signaling when individuals are likely to have interactions with dissimilar individuals and when those interactions incur high costs once the dissimilarity is revealed. The theory of covert signaling is consistent with a number of common signaling domains, including the use of humor as an encrypted signal of similarity (41, 42), the use of fashion to subtly signal insider status (43), political dog whistles (44, 45), and signals used by LGBTQ+ individuals (46, 47) or political dissidents (48–50) to assort without detection. It is also consistent with the fact that signals of political identity need not be obviously political in nature, as reliable associations with certain products and activities may be used as heuristics to differentiate partisans (51–53).It is likely that a great deal of online speech is covert, especially on social media platforms on which users can be personally identified, such as Twitter. Although other social media sites have more users than Twitter (54), Twitter is a particularly important forum for public discourse on current events and as such is valuable for studying covert identity signals that are likely to be both relevant and visible to diverse audiences. For example, the strategy of “subtweeting” is well documented and refers to online communications that are interpretable only to individuals who have relevant information that is not provided in the communication itself (55). As another example, a search for tweets containing the phrase “remember that scene” sent on November 9, 2016 (the day after Donald Trump was elected as US president) returned a number of candidate covert tweets concerning feelings about the election results, many from users unhappy with the outcome (Fig. 1). Each of these requires background knowledge about the cultural artifact (i.e., film) being referenced as well as an understanding of recent political events, as the relevant contextual backdrop for interpreting the analogy implied by those references.Open in a separate windowFig. 1.Potentially covert tweets related to the 2016 US presidential election. To understand each tweet, the reader must be familiar with both the ingroup conversations about the relevant political events as well as the cultural artifacts being referenced.Our data were collected during an especially salient period for political identity signaling on Twitter: the 2020 COVID-19 pandemic, in which millions of people were restricted from gathering and communicating in person, making online engagement one of the principal ways to interact with other humans. Moreover, our tweets were collected and assessed in the wake of many high-profile sociopolitical events in the United States, including the Black Lives Matter protests following the murder of George Floyd, the confirmation of Supreme Court Justice Amy Coney Barrett, and the 2020 presidential election. Opinions on these events were often polarized, making expressions of those opinions into signals of political identity.While they are likely to be common, covert signals are also inherently challenging to study empirically because, by definition, they require insider knowledge to be detected. In this paper, we introduce a theoretically motivated measurement of covertness, focusing on identity signals in the context of political speech online. Essentially, covertness was measured in terms of how people from ingroup and outgroup political groups perceived different tweets. On Twitter, cross-partisan followers are rare (56), and thus, we focused on differences between copartisan radicals and moderates. We downloaded tweets from politically engaged Twitter users with heterogeneous follower networks, thus increasing our chances to collect tweets with some covert political identity signaling according to the theory (Fig. 2A). Then, we asked ingroup and outgroup members to guess the political identity of the tweet author and to report their affective responses to the tweet. Tweets were considered to be more likely to serve as covert identity signals if there was a large difference in responses of ingroup and outgroup raters (Fig. 2B).Open in a separate windowFig. 2.Empirical process to test the theory of covert signaling: (A) selecting Twitter users who might be more likely to use covert or overt political speech and downloading their tweets, (B) rating of tweets on two dimensions and using these ratings to select a subset of covert or overt tweets, and (C) conducting a behavioral experiment with the selected covert and over tweets to test how people use them to communicate their political belief.Our paper constitutes a direct empirical test of the theory of covert signaling. Based on this theory, we predict that covert signaling will be more prevalent among 1) individuals in more heterogeneous communities or individuals with minority status and 2) individuals who face higher costs from being recognized as dissimilar. We derive a simple mathematical model of our experimental design, which yields more precise predictions concerning the relationships between covert signaling, the frequency of outgroup members in the audience, and the cost of being disliked. We test these predictions in a behavioral experiment in which participants select from a set of tweets that contain either overt or covert political signals to communicate with and be evaluated by an audience of varying partisanship (Fig. 2C). We compare signaling strategies when the outgroup audience consists of copartisan members (more or less radical) and cross-partisan members (left or right). Although previous studies have also considered the use of covert or encrypted signals (41, 43, 47, 57), our study tests predictions derived from a formal model with relatively unambiguous predictions and a clear scope of applicability (sensu ref. 58). By doing so, we can show where the existing theory fits the real world and where we need to direct our future efforts to refine the theory. Our empirical pipeline (Fig. 2) is described in more detail in the Materials and Methods.     

Herpesvirus-encoded GPCRs rewire cellular signaling

Viral G-protein-coupled receptors (vGPCRs) are chemokine receptor homologues encoded by the Herpes- and Capripoxviridae. They are thought to have been hijacked from the host genome during the course of evolution. These vGPCRs play different roles in the viral lifecycle and associated pathologies. Three members of the Herpesviridae, Kaposi sarcoma-associated herpesvirus (KSHV), Epstein-Barr virus (EBV) and human cytomegalovirus (HCMV) are capable of setting up persistent latent infections in humans. Two of the herpesviruses, KSHV and EBV, are associated with cancer, while HCMV may have an oncomodulary effect.     

Modular organization of cellular networks

We investigated the organization of interacting proteins and protein complexes into networks of modules. A network-clustering method was developed to identify modules. This method of network-structure determination was validated by clustering known signaling-protein modules and by identifying module rudiments in exclusively high-throughput protein-interaction data with high error frequencies and low coverage. The signaling network controlling the yeast developmental transition to a filamentous form was clustered. Abstraction of a modular network-structure model identified module-organizer proteins and module-connector proteins. The functions of these proteins suggest that they are important for module function and intermodule communication.     

Microdynamics in stationary complex networks

Many complex systems, including networks, are not static but can display strong fluctuations at various time scales. Characterizing the dynamics in complex networks is thus of the utmost importance in the understanding of these networks and of the dynamical processes taking place on them. In this article, we study the example of the US airport network in the time period 1990–2000. We show that even if the statistical distributions of most indicators are stationary, an intense activity takes place at the local (“microscopic”) level, with many disappearing/appearing connections (links) between airports. We find that connections have a very broad distribution of lifetimes, and we introduce a set of metrics to characterize the links'' dynamics. We observe in particular that the links that disappear have essentially the same properties as the ones that appear, and that links that connect airports with very different traffic are very volatile. Motivated by this empirical study, we propose a model of dynamical networks, inspired from previous studies on firm growth, which reproduces most of the empirical observations both for the stationary statistical distributions and for the dynamical properties.     

An information-theoretic framework for resolving community structure in complex networks

To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.     

A tool for filtering information in complex systems

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties.     

Neurotensin receptor-1 and -3 complex modulates the cellular signaling of neurotensin in the HT29 cell line

BACKGROUND & AIMS: The neuropeptide neurotensin (NT) exerts its intracellular effect by interacting with 3 different receptors. Two of these receptors (NTR1 and NTR2) belong to the G protein-coupled receptor family, whereas the third one (NTR3) is a type I receptor with a single transmembrane domain. We recently showed that the 2 structurally different receptors NTR1 and NTR3 were coexpressed in several human cancer cells on which NT exerts proliferative effects. METHODS: Here, by an immunoprecipitation approach, we provide biochemical evidence for an endogenous heterodimerization of the G protein-coupled receptor NTR1 with the NTR3 in the human adenocarcinoma cell line HT29. RESULTS: We show that both receptors are expressed and colocalized within the cell surface of HT29 cells where they already interact to form a heterodimer. The NTR1-NTR3 complex is then internalized on NT stimulation. CONCLUSIONS: The complex formed between these 2 structurally unrelated NT receptors modulates both the NT-induced phosphorylation of mitogen-activated protein kinases and the phosphoinositide (PI) turnover mediated by the NTR1.     

Country-wide rainfall maps from cellular communication networks

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km²), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.     

Understanding bistability in complex enzyme-driven reaction networks

Much attention has been paid recently to bistability and switch-like behavior that might be resident in important biochemical reaction networks. There is, in fact, a great deal of subtlety in the relationship between the structure of a reaction network and its capacity to engender bistability. In common physicochemical settings, large classes of extremely complex networks, taken with mass action kinetics, cannot give rise to bistability no matter what values the rate constants take. On the other hand, bistable behavior can be induced in those same settings by certain very simple and classical mass action mechanisms for enzyme catalysis of a single overall reaction. We present a theorem that distinguishes between those mass action networks that might support bistable behavior and those that cannot. Moreover, we indicate how switch-like behavior results from a well-studied mechanism for the action of human dihydrofolate reductase, an important anti-cancer target.     

Distribution of node characteristics in complex networks

Our enhanced ability to map the structure of various complex networks is increasingly accompanied by the possibility of independently identifying the functional characteristics of each node. Although this led to the observation that nodes with similar characteristics have a tendency to link to each other, in general we lack the tools to quantify the interplay between node properties and the structure of the underlying network. Here we show that when nodes in a network belong to two distinct classes, two independent parameters are needed to capture the detailed interplay between the network structure and node properties. We find that the network structure significantly limits the values of these parameters, requiring a phase diagram to uniquely characterize the configurations available to the system. The phase diagram shows a remarkable independence from the network size, a finding that, together with a proposed heuristic algorithm, allows us to determine its shape even for large networks. To test the usefulness of the developed methods, we apply them to biological and socioeconomic systems, finding that protein functions and mobile phone usage occupy distinct regions of the phase diagram, indicating that the proposed parameters have a strong discriminating power.     

A new decision sciences for complex systems

Models of complex systems can capture much useful information but can be difficult to apply to real-world decision-making because the type of information they contain is often inconsistent with that required for traditional decision analysis. New approaches, which use inductive reasoning over large ensembles of computational experiments, now make possible systematic comparison of alternative policy options using models of complex systems. This article describes Computer-Assisted Reasoning, an approach to decision-making under conditions of deep uncertainty that is ideally suited to applying complex systems to policy analysis. The article demonstrates the approach on the policy problem of global climate change, with a particular focus on the role of technology policies in a robust, adaptive strategy for greenhouse gas abatement.