Publications

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.

Broad-spectrum antibiotics are frequently prescribed to children. Early childhood represents a dynamic period for the intestinal microbial ecosystem, which is readily shaped by environmental cues; antibiotic-induced disruption of this sensitive community may have long-lasting host consequences. Here we demonstrate that a single pulsed macrolide antibiotic treatment (PAT) course early in life is sufficient to lead to durable alterations to the murine intestinal microbiota, ileal gene expression, specific intestinal T-cell populations, and secretory IgA expression. A PAT-perturbed microbial community is necessary for host effects and sufficient to transfer delayed secretory IgA expression. Additionally, early-life antibiotic exposure has lasting and transferable effects on microbial community network topology. Our results indicate that a single early-life macrolide course can alter the microbiota and modulate host immune phenotypes that persist long after exposure has ceased.High or multiple doses of macrolide antibiotics, when given early in life, can perturb the metabolic and immunological development of lab mice. Here, Ruiz et al. show that even a single macrolide course, given early in life, leads to long-lasting changes in the gut microbiota and immune system of mice.

Background: Inflammatory bowel diseases (IBD) are believed to be driven by dysregulated interactions between the host and the gut microbiota. Our goal is to characterize and infer relationships between mucosal T cells, the host tissue environment, and microbial communities in patients with IBD who will serve as basis for mechanistic studies on human IBD.

Methods: We characterized mucosal CD4+ T cells using flow cytometry, along with matching mucosal global gene expression and microbial communities data from 35 pinch biopsy samples from patients with IBD. We analyzed these data sets using an integrated framework to identify predictors of inflammatory states and then reproduced some of the putative relationships formed among these predictors by analyzing data from the pediatric RISK cohort.

Results: We identified 26 predictors from our combined data set that were effective in distinguishing between regions of the intestine undergoing active inflammation and regions that were normal. Network analysis on these 26 predictors revealed SAA1 as the most connected node linking the abundance of the genus Bacteroides with the production of IL17 and IL22 by CD4+ T cells. These SAA1-linked microbial and transcriptome interactions were further reproduced with data from the pediatric IBD RISK cohort.

Conclusions: This study identifies expression of SAA1 as an important link between mucosal T cells, microbial communities, and their tissue environment in patients with IBD. A combination of T cell effector function data, gene expression and microbial profiling can distinguish between intestinal inflammatory states in IBD regardless of disease types.

Venom peptide toxins such as conotoxins play a critical role in the characterization of nicotinic acetylcholine receptor (nAChR) structure and function and have potential as nervous system therapeutics as well. However, the lack of solved structures of conotoxins bound to nAChRs and the large size of these peptides are barriers to their computational docking and design. We addressed these challenges in the context of the α4β2 nAChR, a widespread ligand-gated ion channel in the brain and a target for nicotine addiction therapy, and the 19-residue conotoxin α-GID that antagonizes it. We developed a docking algorithm, ToxDock, which used ensemble-docking and extensive conformational sampling to dock α-GID and its analogs to an α4β2 nAChR homology model. Experimental testing demonstrated that a virtual screen with ToxDock correctly identified three bioactive α-GID mutants (α-GID[A10V], α-GID[V13I], and α-GID[V13Y]) and one inactive variant (α-GID[A10Q]). Two mutants, α-GID[A10V] and α-GID[V13Y], had substantially reduced potency at the human α7 nAChR relative to α-GID, a desirable feature for α-GID analogs. The general usefulness of the docking algorithm was highlighted by redocking of peptide toxins to two ion channels and a binding protein in which the peptide toxins successfully reverted back to near-native crystallographic poses after being perturbed. Our results demonstrate that ToxDock can overcome two fundamental challenges of docking large toxin peptides to ion channel homology models, as exemplified by the α-GID:α4β2 nAChR complex, and is extendable to other toxin peptides and ion channels.

We explore the significance of phenylalanine outside of the phosphotriesterase (PTE) dimer interface through mutagenesis studies and computational modeling. Previous studies have demonstrated that the residue-specific incorporation of para-fluorophenylalanine (pFF) into PTE improves stability, suggesting the importance of phenylalanines in stabilization of the dimer. However, this comes at a cost of decreased solubility due to pFF incorporation into other parts of the protein. Motivated by this, eight single solvent-exposed phenylalanine mutants are evaluated via ROSETTA and good correspondence between experiments and these predictions is observed. Three residues, F304, F327, and F335, appear to be important for PTE activity and stability, even though they do not reside in the dimer interface region or active site. While the remaining mutants do not significantly affect structure or activity, one variant, F306L, reveals improved activity at ambient and elevated temperatures. These studies provide further insight into role of these residues on PTE function and stability.

The Brownian motion of two particles in three dimensions serves as a model for predicting the diffusion-limited reaction rate, as first discussed by von Smoluchowski. Deutch and Felderhof extended the calculation to account for hydrodynamic interactions between the particles and the target, which results in a reduction of the rate coefficient by about half. Many chemical reactions take place in quasi-two-dimensional systems, such as on the membrane or surface of a cell. We perform a Smoluchowski-like calculation in a quasi-two-dimensional geometry, i.e., a membrane surrounded by fluid, and account for hydrodynamic interactions between the particles. We show that rate coefficients are reduced relative to the case of no interactions. The reduction is more pronounced than the three-dimensional case due to the long-range nature of two-dimensional flows.

Use of low resolution single cell DNA FISH and population based high resolution chromosome conformation capture techniques have highlighted the importance of pairwise chromatin interactions in gene regulation. However, it is unlikely that associations involving regulatory elements act in isolation of other interacting partners that also influence their impact. Indeed, the influence of multi-loci interactions remains something of an enigma as beyond low-resolution DNA FISH we do not have the appropriate tools to analyze these. Here we present a method that uses standard 4C-seq data to identify multi-loci interactions from the same cell. We demonstrate the feasibility of our method using 4C-seq data sets that identify known pairwise and novel tri-loci interactions involving the Tcrb and Igk antigen receptor enhancers. We further show that the three Igk enhancers, MiEκ, 3′Eκ and Edκ, interact simultaneously in this super-enhancer cluster, which add to our previous findings showing that loss of one element decreases interactions between all three elements as well as reducing their transcriptional output. These findings underscore the functional importance of simultaneous interactions and provide new insight into the relationship between enhancer elements. Our method opens the door for studying multi-loci interactions and their impact on gene regulation in other biological settings.

In the microenvironment of a malignancy, tumor cells do not exist in isolation, but rather in a diverse ecosystem consisting not only of heterogeneous tumor-cell clones, but also normal cell types such as fibroblasts, vasculature, and an extensive pool of immune cells at numerous possible stages of activation and differentiation. This results in a complex interplay of diverse cellular signaling systems, where the immune cell component is now established to influence cancer progression and therapeutic response. It is experimentally difficult and laborious to comprehensively and systematically profile these distinct cell types from heterogeneous tumor samples in order to capitalize on potential therapeutic and biomarker discoveries. One emerging solution to address this challenge is to computationally extract cell-type specific information directly from bulk tumors. Such in silico approaches are advantageous because they can capture both the cell-type specific profiles and the tissue systems level of cell-cell interactions. Accurately and comprehensively predicting these patterns in tumors is an important challenge to overcome, not least given the success of immunotherapeutic drug treatment of several human cancers. This is especially challenging for subsets of closely related immune cell phenotypes with relatively small gene expression differences, which have critical functional distinctions. Here, we outline the existing and emerging novel bioinformatics strategies that can be used to profile the tumor immune landscape.

An engineered supercharged coiled-coil protein (CSP) and the cationic transfection reagent Lipofectamine 2000 are combined to form a lipoproteoplex for the purpose of dual delivery of siRNA and doxorubicin. CSP, bearing an external positive charge and axial hydrophobic pore, demonstrates the ability to condense siRNA and encapsulate the small-molecule chemotherapeutic, doxorubicin. The lipoproteoplex demonstrates improved doxorubicin loading relative to Lipofectamine 2000. Furthermore, it induces effective transfection of GAPDH (60% knockdown) in MCF-7 breast cancer cells with efficiencies comparing favorably to Lipofectamine 2000. When the lipoproteoplex is loaded with doxorubicin, the improved doxorubicin loading (∼40 μg Dox/mg CSP) results in a substantial decrease in MCF-7 cell viability.

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.