Publications

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.

Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.

To make sense of the world our brains must analyze high-dimensional datasets streamed by our sensory organs. Because such analysis begins with dimensionality reduction, modelling early sensory processing requires biologically plausible online dimensionality reduction algorithms. Recently, we derived such an algorithm, termed similarity matching, from a Multidimensional Scaling (MDS) objective function. However, in the existing algorithm, the number of output dimensions is set a priori by the number of output neurons and cannot be changed. Because the number of informative dimensions in sensory inputs is variable there is a need for adaptive dimensionality reduction. Here, we derive biologically plausible dimensionality reduction algorithms which adapt the number of output dimensions to the eigenspectrum of the input covariance matrix. We formulate three objective functions which, in the offline setting, are optimized by the projections of the input dataset onto its principal subspace scaled by the eigenvalues of the output covariance matrix. In turn, the output eigenvalues are computed as i) soft-thresholded, ii) hard-thresholded, iii) equalized thresholded eigenvalues of the input covariance matrix. In the online setting, we derive the three corresponding adaptive algorithms and map them onto the dynamics of neuronal activity in networks with biologically plausible local learning rules. Remarkably, in the last two networks, neurons are divided into two classes which we identify with principal neurons and interneurons in biological circuits.

cAMP signaling and the control of Schwann cell fate: The ubiquitous second messenger cyclic adenosine monophosphate (cAMP) controls a variety of cellular responses in a cell type-specific and stimulus-dependent manner through an elaborate network of signaling intermediaries that connect stimulation of cell membrane receptors (typically G protein-coupled receptors, GPCRs) to transcription factor activation. Schwann cells (SCs) are highly responsive to cAMP throughout their lifespan, as extensive research has shown that SC survival, lineage specification, proliferation and differentiation into myelin-forming cells require cAMP signaling.

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.

Positive-Unlabeled (PU) learning scenarios are a class of semi-supervised learning where only a fraction of the data is labeled, and all available labels are positive. The goal is to assign correct (positive and negative) labels to as much data as possible. Several important learning problems fall into the PU-learning domain, as in many cases the cost and feasibility of obtaining negative examples is prohibitive. In addition to the positive-negative disparity the overall cost of labeling these datasets typically leads to situations where the number of unlabeled examples greatly outnumbers the labeled. Accordingly, we perform several experiments, on both synthetic and real-world datasets, examining the performance of state of the art PU-learning algorithms when there is significant bias in the labeling process. We propose novel PU algorithms and demonstrate that they outperform the current state of the art on a variety of benchmarks. Lastly, we present a methodology for removing the costly parameter-tuning step in a popular PU algorithm.