Publications

Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.

Type 1 regulatory T cells (Tr1 cells) are induced by interleukin-27 (IL-27) and have critical roles in the control of autoimmunity and resolution of inflammation. We found that the transcription factors IRF1 and BATF were induced early on after treatment with IL-27 and were required for the differentiation and function of Tr1 cells in vitro and in vivo. Epigenetic and transcriptional analyses revealed that both transcription factors influenced chromatin accessibility and expression of the genes required for Tr1 cell function. IRF1 and BATF deficiencies uniquely altered the chromatin landscape, suggesting that these factors serve a pioneering function during Tr1 cell differentiation.

We explore to which extent stars within Galactic disk open clusters resemble each other in the high-dimensional space of their photospheric element abundances, and contrast this with pairs of field stars. Our analysis is based on abundances for 20 elements, homogeneously derived from APOGEE spectra (with carefully quantified uncertainties, with a median value of ∼0.03 dex). We consider 90 red giant stars in seven open clusters and find that most stars within a cluster have abundances in most elements that are indistinguishable (in a χ2-sense) from those of the other members, as expected for stellar birth siblings. An analogous analysis among pairs of >1000 field stars shows that highly significant abundance differences in the 20-dimensional space can be established for the vast majority of these pairs, and that the APOGEE-based abundance measurements have high discriminating power. However, pairs of field stars whose abundances are indistinguishable even at 0.03~dex precision exist: ∼0.3 percent of all field star pairs, and ∼1.0 percent of field star pairs at the same (solar) metallicity [Fe/H] = 0±0.02. Most of these pairs are presumably not birth siblings from the same cluster, but rather doppelganger. Our analysis implies that 'chemical tagging' in the strict sense, identifying birth siblings for typical disk stars through their abundance similarity alone, will not work with such data. However, our approach shows that abundances have extremely valuable information for probabilistic chemo-orbital modeling and combined with velocities, we have identified new cluster members from the field.

Development of mice with hepatocyte knockout of lanosterol 14α-demethylase (HCyp51−/−) from cholesterol synthesis is characterized by the progressive onset of liver injury with ductular reaction and fibrosis. These changes begin during puberty and are generally more aggravated in the knockout females. However, a subgroup of (pre)pubertal knockout mice (runts) exhibits a pronounced male prevalent liver dysfunction characterized by downregulated amino acid metabolism and elevated Casp12. RORC transcriptional activity is diminished in livers of all runt mice, in correlation with the depletion of potential RORC ligands subsequent to CYP51 disruption. Further evidence for this comes from the global analysis that identified a crucial overlap between hepatic Cyp51−/− and Rorc−/− expression profiles. Additionally, the reduction in RORA and RORC transcriptional activity was greater in adult HCyp51−/− females than males, which correlates well with their downregulated amino and fatty acid metabolism. Overall, we identify a global and sex-dependent transcriptional de-regulation due to the block in cholesterol synthesis during development of the Cyp51 knockout mice and provide in vivo evidence that sterol intermediates downstream of lanosterol may regulate the hepatic RORC activity.

We study formation of stellar mass binary black holes (BBHs) originating from Population III (PopIII) stars, performing stellar evolution simulations for PopIII binaries with MESA. We find that a significant fraction of PopIII binaries form massive BBHs through stable mass transfer between two stars in a binary, without experiencing common envelope phases. We investigate necessary conditions required for PopIII binaries to form BBHs coalescing within the Hubble time with a semi-analytical model calibrated by the stellar evolution simulations. The formation efficiency of coalescing PopIII BBHs is estimated for two different initial conditions for PopIII binaries with large and small separations, respectively. Consequently, in both models, ∼10% of the total PopIII binaries form BBHs only through stable mass transfer and ∼10% of these BBHs merge due to gravitational wave emission within the Hubble time. Furthermore, the chirp mass of merging BBHs has a flat distribution over 15≲Mchirp/M⊙≲35. This formation pathway of PopIII BBHs is presumably robust because stable mass transfer is less uncertain than common envelope evolution, which is the main formation channel for Population II BBHs. We also test the hypothesis that the BBH mergers detected by LIGO originate from PopIII stars using our result and the total number of PopIII stars formed in the early universe as inferred from the optical depth measured by Planck. We conclude that the PopIII BBH formation scenario can explain the mass-weighted merger rate of the LIGO's O1 events with the maximal PopIII formation efficiency inferred from the Planck measurement, even without BBHs formed by unstable mass transfer or common envelope phases.

We present a novel platform for the large-scale simulation of three-dimensional fibrous structures immersed in a Stokesian fluid and evolving under confinement or in free-space in three dimensions. One of the main motivations for this work is to study the dynamics of fiber assemblies within biological cells. For this, we also incorporate the key biophysical elements that determine the dynamics of these assemblies, which include the polymerization and depolymerization kinetics of fibers, their interactions with molecular motors and other objects, their flexibility, and hydrodynamic coupling. This work, to our knowledge, is the first technique to include many-body hydrodynamic interactions (HIs), and the resulting fluid flows, in cellular assemblies of flexible fibers. We use non-local slender body theory to compute the fluid–structure interactions of the fibers and a second-kind boundary integral formulation for other rigid bodies and the confining boundary. A kernel-independent implementation of the fast multipole method is utilized for efficient evaluation of HIs. The deformation of the fibers is described by nonlinear Euler–Bernoulli beam theory and their polymerization is modeled by the reparametrization of the dynamic equations in the appropriate non-Lagrangian frame. We use a pseudo-spectral representation of fiber positions and implicit time-stepping to resolve large fiber deformations, and to allow time-steps not excessively constrained by temporal stiffness or fiber–fiber interactions. The entire computational scheme is parallelized, which enables simulating assemblies of thousands of fibers. We use our method to investigate two important questions in the mechanics of cell division: (i) the effect of confinement on the hydrodynamic mobility of microtubule asters; and (ii) the dynamics of the positioning of mitotic spindle in complex cell geometries. Finally to demonstrate the general applicability of the method, we simulate the sedimentation of a cloud of semi-flexible fibers.

Both microbial and host genetic factors contribute to the pathogenesis of autoimmune disease1-4. Accumulating evidence suggests that microbial species that potentiate chronic inflammation, as in inflammatory bowel disease (IBD), often also colonize healthy individuals. These microbes, including the Helicobacter species, have the propensity to induce autoreactive T cells and are collectively referred to as pathobionts4-8. However, an understanding of how such T cells are constrained in healthy individuals is lacking. Here we report that host tolerance to a potentially pathogenic bacterium, Helicobacter hepaticus (H. hepaticus), is mediated by induction of RORγt+Foxp3+ regulatory T cells (iTreg) that selectively restrain pro-inflammatory TH17 cells and whose function is dependent on the transcription factor c-Maf. Whereas H. hepaticus colonization of wild-type mice promoted differentiation of RORγt-expressing microbe-specific iTreg in the large intestine, in disease-susceptible IL-10-deficient animals there was instead expansion of colitogenic TH17 cells. Inactivation of c-Maf in the Treg compartment likewise impaired differentiation of bacteria-specific iTreg, resulting in accumulation of H. hepaticus-specific inflammatory TH17 cells and spontaneous colitis. In contrast, RORγt inactivation in Treg only had a minor effect on bacterial-specific Treg-TH17 balance, and did not result in inflammation. Our results suggest that pathobiont-dependent IBD is a consequence of microbiota-reactive T cells that have escaped this c-Maf-dependent mechanism of iTreg-TH17 homeostasis.

A wide variety of protein and peptidomimetic design tasks require matching functional three-dimensional motifs to potential oligomeric scaffolds. Enzyme design, for example, aims to graft active-site patterns typically consisting of 3 to 15 residues onto new protein surfaces. Identifying suitable proteins capable of scaffolding such active-site engraftment requires costly searches to identify protein folds that can provide the correct positioning of side chains to host the desired active site. Other examples of biodesign tasks that require simpler fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications the speed and scaling of geometric search limits downstream design to small patterns. Here we present an adaptive algorithm to searching for side chain take-off angles compatible with an arbitrarily specified functional pattern that enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided but our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive- geometric-search-for-protein-design).

Inference of eukaryotic transcription regulatory networks remains challenging due to the large number of regu- lators, combinatorial interactions, and redundant pathways. Even in the model system Saccharomyces cerevisiae, inference has performed poorly. Most existing inference algorithms ignore crucial regulatory components, like RNA stability and post-transcriptional modulation of regulators. Here we demonstrate that explicitly modeling tran- scription factor activity and RNA half-lives during inference of a genome-wide transcription regulatory network in yeast not only advances prediction performance, but also produces new insights into gene- and condition-specific variation of RNA stability. We curated a high quality gold standard reference network that we use for priors on network structure and model validation. We incorporate variation of RNA half-lives into the Inferelator inference framework, and show improved performance over previously described algorithms and over implementations of the algorithm that do not model RNA degradation. We recapitulate known condition- and gene-specific trends in RNA half-lives, and make new predictions about RNA half-lives that are confirmed by experimental data.

We consider the problems of compressed sensing and optimal denoising for signals $\mathbf{x_0}\in\mathbb{R}^N$ that are monotone, i.e., $\mathbf{x_0}(i+1) \geq \mathbf{x_0}(i)$, and sparsely varying, i.e., $\mathbf{x_0}(i+1) > \mathbf{x_0}(i)$ only for a small number $k$ of indices $i$. We approach the compressed sensing problem by minimizing the total variation norm restricted to the class of monotone signals subject to equality constraints obtained from a number of measurements $A\mathbf{x_0}$. For random Gaussian sensing matrices $A\in\mathbb{R}^{m\times N}$ we derive a closed form expression for the number of measurements $m$ required for successful reconstruction with high probability. We show that the probability undergoes a phase transition as $m$ varies, and depends not only on the number of change points, but also on their location. For denoising we regularize with the same norm and derive a formula for the optimal regularizer weight that depends only mildly on $\mathbf{x_0}$. We obtain our results using the statistical dimension tool.