Publications

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli.

We present SEEK (search-based exploration of expression compendia; http://seek.princeton.edu/), a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation–based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options.

Scholars and commentators have debated whether lower-threshold forms of political engagement on social media should be treated as being conducive to higher-threshold modes of political participation or a diversion from them. Drawing on an original survey of a representative sample of Italians who discussed the 2013 election on Twitter, we demonstrate that the more respondents acquire political information via social media and express themselves politically on these platforms, the more they are likely to contact politicians via e-mail, campaign for parties and candidates using social media, and attend offline events to which they were invited online. These results suggest that lower-threshold forms of political engagement on social media do not distract from higher-threshold activities, but are strongly associated with them.

...These events — and the corresponding responses on social media — illustrate what has become increasingly evident: it is almost impossible to think of a major political protest or upheaval occurring without social media being part of both the incident and the ensuing narrative. The Euromaidan protests, which culminated in the flight of President Yanukovych from Ukraine in late February 2014, are a case in point. Indeed, the Ukrainian Euromaidan protest movement may go down in history as the first truly successful social media uprising. Earlier movements labeled social media revolutions subsequently have been criticized for not having had much important activity on social media (Moldova, Arab Spring) or for having had a large social media presence but ultimately failing to make much of a long-term impact as a protest movement (Spain’s Los Indignados, Occupy Wall Street, Gezi Park in Turkey). In Ukraine, a government fell, a region was annexed, a civilian plane was shot down, and what some are calling a civil war continues to this day in the eastern part of the country. Clearly, the movement was consequential and, as we will show, social media usage was widespread and significant.

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Social media use among elites offers a useful avenue for analyzing regime response to protest, especially in countries with some degree of freedom of speech. We examine the frequency and content of Twitter usage among Venezuelan elites in the context of the 2014 protests. This analysis demonstrates that the regime sent more signals during protests but that the content of these messages addressed more topics than it did for opposition elites, especially following acts of regime suppression of opposition-sponsored protests. This observation supports theoretical predictions that noisy public information can make coordination more difficult. Regime elites produced noisy Twitter as part of an explicit strategy to prevent citizens from coordinating on revolution. This analysis of social media use by both pro- and anti-regime elites contributes to the debate over whether social media prevents or promotes regime change.

Peptomers are oligomeric molecules composed of both α-amino acids and N-substituted glycine monomers, thus creating a hybrid of peptide and peptoid units. Peptomers have been used in several applications such as antimicrobials, protease inhibitors, and antibody mimics. Despite the considerable promise of peptomers as chemically diverse molecular scaffolds, we know little about their conformational tendencies. This lack of knowledge limits the ability to implement computational approaches for peptomer design. Here we computationally evaluate the local structural propensities of the peptide–peptoid linkage. We find some general similarities between the peptide residue conformational preferences and the Ramachandran distribution of residues that precede proline in folded protein structures. However, there are notable differences. For example, several β-turn motifs are disallowed when the i+2 residue is also a peptoid monomer. Significantly, the lowest energy geometry, when dispersion forces are accounted for, corresponds to a “cis-Pro touch-turn” conformation, an unusual turn motif that has been observed at protein catalytic centers and binding sites. The peptomer touch-turn thus represents a useful design element for the construction of folded oligomers capable of molecular recognition and as modules in the assembly of structurally complex peptoid–protein hybrid macromolecules.

Are legislators responsive to their constituents in their public communication? To what extent are they able to shape the agenda that the mass public cares about, as expressed by the issues they discuss? We address this twofold question with an analysis of all tweets sent by Members of the U.S. Congress and a random sample of their followers from January  to March . Using a Latent Dirichlet Allocation model, we extract topics that represent the diversity of issues that legislators and ordinary citizens discuss on this social networking site. en, we exploit variation in the distribution of topics over time to test whether Members of Congress lead or follow their constituents in their selection of issues to discuss, employing a Granger-causality framework. We find that legislators are responsive in their public statements to their constituents, but also that they have limited influence on their followers’ public agenda. To further understand the mechanisms that explain political responsiveness, we also examine whether Members of Congress are more responsive to specific constituents groups, showing that they are more influenced by co-partisans, politically interested citizens, and social media users located within their constituency.

With the notable exception of B-cell malignancies, the efficacy of chimeric antigen receptor (CAR) T cells has been limited, and CAR T cells have not been shown to expand and persist in patients with nonlymphoid tumors. Here we demonstrate that redirection of primary human T cells with a CAR containing the inducible costimulator (ICOS) intracellular domain generates tumor-specific IL-17-producing effector cells that show enhanced persistence. Compared with CARs containing the CD3ζ chain alone, or in tandem with the CD28 or the 4-1BB intracellular domains, ICOS signaling increased IL-17A, IL-17F, and IL-22 following antigen recognition. In addition, T cells redirected with an ICOS-based CAR maintained a core molecular signature characteristic of TH17 cells and expressed higher levels of RORC, CD161, IL1R-1, and NCS1. Of note, ICOS signaling also induced the expression of IFN-γ and T-bet, consistent with a TH17/TH1 bipolarization. When transferred into mice with established tumors, TH17 cells that were redirected with ICOS-based CARs mediated efficient antitumor responses and showed enhanced persistence compared with CD28- or 4-1BB-based CAR T cells. Thus, redirection of TH17 cells with a CAR encoding the ICOS intracellular domain is a promising approach to augment the function and persistence of CAR T cells in hematologic malignancies.