Howard Hughes Medical Institute

About: Howard Hughes Medical Institute is a nonprofit organization based out in Chevy Chase, Maryland, United States. It is known for research contribution in the topics: Gene & RNA. The organization has 20371 authors who have published 34677 publications receiving 5247143 citations. The organization is also known as: HHMI & hhmi.org.

Efficient proximity labeling in living cells and organisms with TurboID.

Abstract: Protein interaction networks and protein compartmentalization underlie all signaling and regulatory processes in cells. Enzyme-catalyzed proximity labeling (PL) has emerged as a new approach to study the spatial and interaction characteristics of proteins in living cells. However, current PL methods require over 18 h of labeling time or utilize chemicals with limited cell permeability or high toxicity. We used yeast display-based directed evolution to engineer two promiscuous mutants of biotin ligase, TurboID and miniTurbo, which catalyze PL with much greater efficiency than BioID or BioID2, and enable 10-min PL in cells with non-toxic and easily deliverable biotin. Furthermore, TurboID extends biotin-based PL to flies and worms.

Broad diversity of neutralizing antibodies isolated from memory B cells in HIV-infected individuals

Abstract: Serologic memory is an important factor in long-term vaccine efficacy, but there is little understanding of the antibodies produced by memory B cells in individuals infected with important human pathogens such as HIV To examine the memory antibody response to HIV, Scheid et al cloned more than 500 antibodies from HIV-specific memory B cells from six HIV-infected patients with high serum titres of broadly neutralizing antibodies The B-cell memory response to HIV in these patients was composed of up to 50 independent expanded B clones expressing a heterogeneous collection of antibodies to different viral epitopes, several of which may be important for broad HIV neutralization and effective vaccination This study clones and characterizes antibodies present in six HIV-infected subjects with low-to-intermediate viral loads Antibodies to conserved epitopes on the human immunodeficiency virus (HIV) surface protein gp140 can protect against infection in non-human primates, and some infected individuals show high titres of broadly neutralizing immunoglobulin (Ig)G antibodies in their serum However, little is known about the specificity and activity of these antibodies1,2,3 To characterize the memory antibody responses to HIV, we cloned 502 antibodies from HIV envelope-binding memory B cells from six HIV-infected patients with broadly neutralizing antibodies and low to intermediate viral loads We show that in these patients, the B-cell memory response to gp140 is composed of up to 50 independent clones expressing high affinity neutralizing antibodies to the gp120 variable loops, the CD4-binding site, the co-receptor-binding site, and to a new neutralizing epitope that is in the same region of gp120 as the CD4-binding site Thus, the IgG memory B-cell compartment in the selected group of patients with broad serum neutralizing activity to HIV is comprised of multiple clonal responses with neutralizing activity directed against several epitopes on gp120

Protein disaggregation mediated by heat-shock protein Hsp104.

Abstract: The heat-inducible members of the Hsp100 (or Clp) family of proteins share a common function in helping organisms to survive extreme stress, but the basic mechanism through which these proteins function is not understood. Hsp104 protects cells against a variety of stresses, under many physiological conditions, and its function has been evolutionarily conserved, at least from Saccharomyces cerevisiae to Arabidopsis thaliana. Homology with the Escherichia coli ClpA protein suggests that Hsp104 may provide stress tolerance by helping to rid the cell of heat-denatured proteins through proteolysis. But genetic analysis indicates that Hsp104 may function like Hsp70 as a molecular chaperone. Here we investigate the role of Hsp104 in vivo using a temperature-sensitive Vibrio harveyi luciferase-fusion protein as a test substrate. We find that Hsp104 does not protect luciferase from thermal denaturation, nor does it promote proteolysis of luciferase. Rather, Hsp104 functions in a manner not previously described for other heat-shock proteins: it mediates the resolubilization of heat-inactivated luciferase from insoluble aggregates.

Signal Transduction Cascades Regulating Fungal Development and Virulence

Abstract: Cellular differentiation, mating, and filamentous growth are regulated in many fungi by environmental and nutritional signals. For example, in response to nitrogen limitation, diploid cells of the yeast Saccharomyces cerevisiae undergo a dimorphic transition to filamentous growth referred to as pseudohyphal differentiation. Yeast filamentous growth is regulated, in part, by two conserved signal transduction cascades: a mitogen-activated protein kinase cascade and a G-protein regulated cyclic AMP signaling pathway. Related signaling cascades play an analogous role in regulating mating and virulence in the plant fungal pathogen Ustilago maydis and the human fungal pathogens Cryptococcus neoformans and Candida albicans. We review here studies on the signaling cascades that regulate development of these and other fungi. This analysis illustrates both how the model yeast S. cerevisiae can serve as a paradigm for signaling in other organisms and also how studies in other fungi provide insights into conserved signaling pathways that operate in many divergent organisms.

Dictionary learning algorithms for sparse representation

Abstract: Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations.Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).