Publications

Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect ecigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.94 area under the curve in each category. These promising initial results form the foundation for further studies to realize the ideal surveillance solution.

Background
Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases.

Methods
To investigate the relationship of antibiotic exposure with microbiome perturbations in a murine model of growth promotion, C57BL/6 mice received lifelong sub-therapeutic antibiotic treatment (STAT), or not (control), and were fed HFD starting at 13 weeks. To characterize microbiota changes caused by STAT, the V4 region of the 16S rRNA gene was examined from collected fecal samples and analyzed.

Results
In this model, which included HFD, STAT mice developed increased weight and fat mass compared to controls. Although results in males and females were not identical, insulin resistance and NAFLD were more severe in the STAT mice. Fecal microbiota from STAT mice were distinct from controls. Compared with controls, STAT exposure led to early conserved diet-independent microbiota changes indicative of an immature microbial community. Key taxa were identified as STAT-specific and several were found to be predictive of disease. Inferred network models showed topological shifts concurrent with growth promotion and suggest the presence of keystone species.

Conclusions
These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation.

The 3D organization of chromosomes enables cells to balance the biophysical constraints of the crowded nucleus with the functional dynamics of gene regulation. Physical contacts between genes and their regulatory elements are essential for proper transcriptional control and maintenance of these interactions is critical for preventing aberrations in physiological processes that could manifest as disease states. The first insights into global nuclear organization came from imaging studies using FISH (fluorescent in-situ hybridization) analyses, which demonstrated that chromosomes occupy individual territories in the nucleus with minimal intermingling between them [1]. The development of chromosome conformation capture (3C) in which chromatin fragments in close physical proximity can be detected enabled the characterization of molecular interactions between different loci [2]. When 3C-based techniques incorporated massive parallel sequencing (such as in Hi-C) the description of molecular chromatin interactions at a genome-wide scale was finally possible [3]. Hi-C was the first unbiased approach aimed at capturing all interactions in the nucleus thereby providing a snapshot of nuclear organization at the global scale. The first Hi-C study revealed that each chromosomal territory is further divided into large domains of 5–10Mb that physically separate two compartments (A and B), which strongly correlate with active and inactive chromatin, respectively [3]. Furthermore, this study demonstrated that interactions between loci in the same compartment occur at a higher frequency than between loci in different compartments [3]. With the progressive decrease in sequencing costs, higher-resolution Hi-C revealed a new level of nuclear organization where compartments A and B can be further divided into “topologically associated domains” (TADs) [4–6]. In mammalian cells these domains range in size from a few 100kbs to 5Mbs in size (with an average of 1MB). Since they exhibit a high degree of conservation between cell types and species it was proposed that TADs represent the fundamental unit of physical organization of the genome [5].

Thanks to the confluence of genomic technology and computational developments, the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective focuses on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin state and transcriptional regulatory structure and dynamics. We highlight 4 research questions that require further investigation in order to make progress in network inference: (1) using overall constraints on network structure such as sparsity, (2) use of informative priors and data integration to constrain individual model parameters, (3) estimation of latent regulatory factor activity under varying cell conditions, and (4) new methods for learning and modeling regulatory factor interactions. We conclude that methods combining advances in these 4 categories of required effort with new genomic technologies will result in biophysically motivated dynamic genome-wide regulatory network models for several of the best-studied organisms and cell types.

The principal B. subtilis laboratory strain, strain 168, is derived from a parent strain isolated in Marburg, Germany, following a mutagenesis procedure (1). The popularity of this strain arose after it was shown to be competent for genetic transformation (2, 3), which paved the way for myriad molecular genetics analyses that led to a detailed understanding of the biology of B. subtilis and related Gram-positive bacteria.

Many cellular processes are driven by cytoskeletal assemblies. It remains unclear how cytoskeletal filaments and motor proteins organize into cellular scale structures and how molecular properties of cytoskeletal components affect the large-scale behaviors of these systems. Here, we investigate the self-organization of stabilized microtubules in Xenopus oocyte extracts and find that they can form macroscopic networks that spontaneously contract. We propose that these contractions are driven by the clustering of microtubule minus ends by dynein. Based on this idea, we construct an active fluid theory of network contractions, which predicts a dependence of the timescale of contraction on initial network geometry, a development of density inhomogeneities during contraction, a constant final network density, and a strong influence of dynein inhibition on the rate of contraction, all in quantitative agreement with experiments. These results demonstrate that the motor-driven clustering of filament ends is a generic mechanism leading to contraction.

Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

Cutinases are powerful hydrolases that can cleave ester bonds of polyesters such as poly(ethylene terephthalate) (PET), opening up new options for enzymatic routes for polymer recycling and surface modification reactions. Cutinase from Aspergillus oryzae (AoC) is promising owing to the presence of an extended groove near the catalytic triad which is important for the orientation of polymeric chains. However, the catalytic efficiency of AoC on rigid polymers like PET is limited by its low thermostability; as it is essential to work at or over the glass transition temperature (Tg) of PET, that is, 70°C. Consequently, in this study we worked toward the thermostabilization of AoC. Use of Rosetta computational protein design software in conjunction with rational design led to a 6°C improvement in the thermal unfolding temperature (Tm) and a 10-fold increase in the half-life of the enzyme activity at 60°C. Surprisingly, thermostabilization did not improve the rate or temperature optimum of enzyme activity. Three notable findings are presented as steps toward designing more thermophilic cutinase: (a) surface salt bridge optimization produced enthalpic stabilization, (b) mutations to proline reduced the entropy loss upon folding, and (c) the lack of a correlative increase in the temperature optimum of catalytic activity with thermodynamic stability suggests that the active site is locally denatured at a temperature below the Tm of the global structure. Proteins 2016; 84:60–72. © 2015 Wiley Periodicals, Inc.

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.