Publications

Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild.

During class switch recombination (CSR), B cells replace the Igh Cμ or δ exons with another downstream constant region exon (CH), altering the antibody isotype. CSR occurs through the introduction of AID-mediated double-strand breaks (DSBs) in switch regions and subsequent ligation of broken ends. Here, we developed an assay to investigate the dynamics of DSB formation in individual cells. We demonstrate that the upstream switch region Sμ is first targeted during recombination and that the mechanism underlying this control relies on 53BP1. Surprisingly, regulation of break order occurs through residual binding of 53BP1 to chromatin before the introduction of damage and independent of its established role in DNA repair. Using chromosome conformation capture, we show that 53BP1 mediates changes in chromatin architecture that affect break order. Finally, our results explain how changes in Igh architecture in the absence of 53BP1 could promote inversional rearrangements that compromise CSR.

Th17 lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear receptor. We identified the DEAD-box RNA helicase DDX5 as a RORγt partner that coordinates transcription of selective Th17 genes and is required for Th17-mediated inflammatory pathologies. Surprisingly, the ability of DDX5 to interact with RORγt and co-activate its targets depends on its intrinsic RNA helicase activity and binding of a conserved nuclear long noncoding RNA (lncRNA), Rmrp, which is mutated in Cartilage-Hair Hypoplasia (CHH) patients. A targeted Rmrp mutation in mice, corresponding to one in CHH patients, abrogated the lncRNA’s chromatin recruitment, ability to potentiate DDX5-RORγt interaction and RORγt target gene transcription. Elucidation of the link between Rmrp and the DDX5-RORγt complex reveals a role for RNA helicases and lncRNAs in tissue-specific transcriptional regulation and promises new opportunities for therapeutic intervention in Th17-dependent diseases.

We present a fast direct solver for two dimensional scattering problems, where an incident wave impinges on a penetrable medium with compact support. We represent the scattered field using a volume potential whose kernel is the outgoing Green's function for the exterior domain. Inserting this representation into the governing partial differential equation, we obtain an integral equation of the Lippmann-Schwinger type. The principal contribution here is the development of an automatically adaptive, high-order accurate discretization based on a quad tree data structure which provides rapid access to arbitrary elements of the discretized system matrix. This permits the straightforward application of state-of-the-art algorithms for constructing compressed versions of the solution operator. These solvers typically require $O(N^{3/2})$ work, where $N$ denotes the number of degrees of freedom. We demonstrate the performance of the method for a variety of problems in both the low and high frequency regimes.

V(D)J recombination relies on the presence of proximal enhancers that activate the antigen receptor (AgR) loci in a lineage- and stage-specific manner. Unexpectedly, we find that both active and inactive AgR enhancers cooperate to disseminate their effects in a localized and long-range manner. Here, we demonstrate the importance of short-range contacts between active enhancers that constitute an Igk super-enhancer in B cells. Deletion of one element reduces the interaction frequency between other enhancers in the hub, which compromises the transcriptional output of each component. Furthermore, we establish that, in T cells, long-range contact and cooperation between the inactive Igk enhancer MiEκ and the active Tcrb enhancer Eβ alters enrichment of CBFβ binding in a manner that impacts Tcrb recombination. These findings underline the complexities of enhancer regulation and point to a role for localized and long-range enhancer-sharing between active and inactive elements in lineage- and stage-specific control.

Selecting regularization parameters in penalized high-dimensional graphical models in a principled, data-driven, and computationally efficient manner continues to be one of the key challenges in high-dimensional statistics. We present substantial computational gains and conceptual generalizations of the Stability Approach to Regularization Selection (StARS), a state-of-the-art graphical model selection scheme. Using properties of the Poisson-Binomial distribution and convex non-asymptotic distributional modeling we propose lower and upper bounds on the StARS graph regularization path which results in greatly reduced computational cost without compromising regularization selection. We also generalize the StARS criterion from single edge to induced subgraph (graphlet) stability. We show that simultaneously requiring edge and graphlet stability leads to superior graph recovery performance independent of graph topology. These novel insights render Gaussian graphical model selection a routine task on standard multi-core computers.

The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise time points during song production. However, the neural representation of this behavior at a population level remains elusive. We used two-photon microscopy to monitor ensemble activity during singing, integrating across multiple trials by adopting a Bayesian inference approach to more precisely estimate burst timing. Additionally, we examined spiking and motor-related synaptic inputs using intracellular recordings during singing. With both experimental approaches, we find that premotor events do not occur preferentially at the onsets or offsets of song syllables or at specific subsyllabic motor landmarks. These results strongly support the notion that HVC projection neurons collectively exhibit a temporal sequence during singing that is uncoupled from ongoing movements.

Background

The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms.

New method

We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise.

Results

We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation.
Comparison with existing methods

Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria.
Conclusions

Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms.

Increasing incidence of inflammatory bowel diseases, such as Crohn’s disease, in developed nations is associated with changes to the microbial environment, such as decreased prevalence of helminth colonization and alterations to the gut microbiota. We find that helminth infection protects mice deficient in the Crohn’s disease susceptibility gene Nod2 from intestinal abnormalities by inhibiting colonization by an inflammatory Bacteroides species. Resistance to Bacteroides colonization was dependent on type 2 immunity, which promoted the establishment of a protective microbiota enriched in Clostridiales. Additionally, we show that individuals from helminth-endemic regions harbor a similar protective microbiota and that deworming treatment reduced levels of Clostridiales and increased Bacteroidales. These results support a model of the hygiene hypothesis in which certain individuals are genetically susceptible to the consequences of a changing microbial environment.

Connectivity patterns of neocortex exhibit several odd properties: for example, most neighboring excitatory neurons do not connect, which seems curiously wasteful. Brunel’s elegant theoretical treatment reveals how optimal information storage can naturally impose these peculiar properties.