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Hahn, S.

Publications and source records attributed to Hahn, S..

25 records · Page 2Linked to original sources

The ABCD Stop Signal Data: Response to Bissett et al.

This paper responds to a recent critique by Bissett and colleagues (Bissett et al., eLife, In Press) of the fMRI Stop task being used in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study(R)). The critique focuses primarily on a design feature of the task that the authors contend lead to a violation of race model assumptions (i.e., that the Go and Stop processes are fully independent) which are relevant to the calculation of the Stop Signal Reaction Time, a measure of the inhibition process. Bissett and colleagues also raise a number of secondary concerns. In this response we note that satisfying race model assumptions is a pernicious challenge for Stop task designs but also that the race model is quite robust against violations of its assumptions. Most importantly, while Bissett et al. raise conceptual concerns with the task we focus here on analyses of both the performance and the neuroimaging data and we conclude that the concerns appear to have minimal impact on the neuroimaging data (the validity of which do not rely on race model assumptions) and have far less of an impact on the performance data than the critique suggests. We note that Bissett et al. did not apply any performance-based exclusions to the data they analyzed, that a number of the trial coding errors that they flagged were already identified and corrected in the ABCD annual data releases, that a number of the secondary concerns reflect sensible design decisions and, indeed, that their own computational modeling of the ABCD Stop task suggests the problems they identify have just a modest impact on the rank ordering of individual differences in subject performance. In this paper, we list some adjustments that have been made to the task and some new flags that are now added to the annual, curated data releases. We stress that the ABCD data are fully available to the scientific community who are empowered to apply whatever inclusion and exclusion criteria they deem appropriate for their analyses and we conclude that the ABCD Stop task yields valuable data that researchers can use to track adolescent neurodevelopment.

neuroscience↗

A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning

Transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that permit their dynamic behavior, fuzzy interactions and target specificity. We screened a large set of random 30-mer peptides for AD function and trained a deep neural network (ADpred) on the AD-positive and negative sequences. ADpred correctly identifies known ADs within protein sequences and accurately predicts the consequences of mutations. We show that functional ADs are (1) located within intrinsically disordered regions with biased amino acid composition, (2) contain clusters of hydrophobic residues near acidic side chains, (3) are enriched or depleted for particular dipeptide sequences, and (4) have higher helical propensity than surrounding regions. Taken together, our findings fit the model of "fuzzy" binding through hydrophobic protein-protein interfaces, where activator-coactivator binding takes place in a dynamic hydrophobic environment rather than through combinations of sequence-specific interactions.

molecular biology↗

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

Using deep learning to augment structured illumination microscopy (SIM), we obtained a fivefold reduction in the number of raw images required for super-resolution SIM, and generated images under extreme low light conditions (100X fewer photons). We validated the performance of deep neural networks on different cellular structures and achieved multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

biophysics↗

Mediator subunit Med15 dictates the conserved 'fuzzy' binding mechanism of yeast transcription activators Gal4 and Gcn4

The acidic activation domain (AD) of yeast transcription factor Gal4 plays a dual role in both transcription repression and activation through sequence-dependent binding to Gal80 repressor and sequence-independent binding to Mediator subunit Med15. The activation function of Gal4 arises from two hydrophobic regions within the 40-residue AD. We show by NMR that each AD region binds the Mediator subunit Med15 using a "fuzzy" protein interface. Remarkably, comparison of chemical shift perturbations shows that Gal4 and Gcn4, two ADs of different sequence, interact nearly identically with Med15. The findings that two ADs of different sequence use an identical fuzzy binding mechanism shows a common sequence-independent mechanism for AD-Mediator binding, similar to interactions within a hydrophobic cloud. In contrast, the same region of Gal4 AD interacts with Gal80 via a tight structured complex, implying that the structured binding partner of an intrinsically disordered protein dictates the type of protein interaction.

molecular biology↗

Two separate roles for the transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA

Deletions within genes coding for subunits of the transcription coactivator SAGA caused strong genome-wide defects in transcription and SAGA-mediated chromatin modifications. In contrast, rapid SAGA depletion produced only modest transcription defects at 13% of protein-coding genes - genes that are generally more sensitive to rapid TFIID depletion. However, transcription of these "coactivator-redundant" genes is strongly affected by rapid depletion of both factors, showing the overlapping functions of TFIID and SAGA at this gene set. We suggest that this overlapping function is linked to TBP-DNA recruitment. The remaining 87% of expressed genes that we term "TFIID-dependent" are highly sensitive to rapid TFIID depletion and insensitive to rapid SAGA depletion. Genome-wide mapping of SAGA and TFIID found binding of both factors at many genes independent of gene class. DNA analysis suggests that the distinction between the gene classes is due to multiple components rather than any single regulatory factor or promoter sequence motif.

molecular biology↗

Transcription activation domains of the yeast factors Met4 and Ino2: tandem activation domains with properties similar to the yeast Gcn4 activator

Eukaryotic transcription activation domains (ADs) are intrinsically disordered polypeptides that typically interact with coactivator complexes, leading to stimulation of transcription initiation, elongation and chromatin modifications. Here we examine the properties of two strong and conserved yeast ADs: Met4 and Ino2. Both factors have tandem ADs that were identified by conserved sequence and functional studies. While AD function from both factors depends on hydrophobic residues, Ino2 further requires key conserved acidic and polar residues for optimal function. Binding studies show that the ADs bind multiple Med15 activator binding domains (ABDs) with a similar order of micromolar affinity, and similar but distinct thermodynamic properties. Protein crosslinking shows that no unique complex is formed upon Met4-Med15 binding. Rather, we observed heterogeneous AD-ABD contacts with nearly every possible AD-ABD combination. Many of these properties are similar to those observed with the yeast activator Gcn4, which forms a large heterogeneous, dynamic, and fuzzy complex with Med15. We suggest that this molecular behavior is common among eukaryotic activators.

molecular biology↗

Transcription activator-coactivator specificity is mediated by a large and dynamic fuzzy protein-protein complex

Transcription activation domains (ADs) are inherently disordered proteins that often target multiple coactivator complexes, but the specificity of these interactions is not understood. Efficient activation by yeast Gcn4 requires tandem Gcn4 ADs and four activator-binding domains (ABDs) on its target, the Mediator subunit Med15. Multiple ABDs are a common feature of coactivator complexes. We find that the large Gcn4-Med15 complex is heterogeneous, containing nearly all possible AD-ABD interactions. This complex forms using a dynamic fuzzy protein-protein interface where ADs use hydrophobic residues to bind hydrophobic surfaces of the ABDs in multiple orientations. This combinatorial mechanism allows individual interactions of low affinity and specificity to generate a biologically functional, specific, and higher affinity complex despite lacking a defined protein-protein interface. This binding strategy is likely representative of many activators that target multiple coactivators and allows great flexibility in combinations of activators that synergize to regulate genes with variable coactivator requirements.

molecular biology↗