Transcription Factors Mat2 and Znf2 Operate Cellular Circuits Orchestrating Opposite- and Same-Sex Mating in Cryptococcus neoformans

Cryptococcus neoformans is a human fungal pathogen that undergoes a dimorphic transition from a unicellular yeast to multicellular hyphae during opposite sex (mating) and unisexual reproduction (same-sex mating). Opposite- and same-sex mating are induced by similar environmental conditions and involve many shared components, including the conserved pheromone sensing Cpk1 MAPK signal transduction cascade that governs the dimorphic switch in C. neoformans. However, the homeodomain cell identity proteins Sxi1α/Sxi2a encoded by the mating type locus that are essential for completion of sexual reproduction following cell–cell fusion during opposite-sex mating are dispensable for same-sex mating. Therefore, identification of downstream targets of the Cpk1 MAPK pathway holds the key to understanding molecular mechanisms governing the two distinct developmental fates. Thus far, homology-based approaches failed to identify downstream transcription factors which may therefore be species-specific. Here, we applied insertional mutagenesis via Agrobacterium-mediated transformation and transcription analysis using whole genome microarrays to identify factors involved in C. neoformans differentiation. Two transcription factors, Mat2 and Znf2, were identified as key regulators of hyphal growth during same- and opposite-sex mating. Mat2 is an HMG domain factor, and Znf2 is a zinc finger protein; neither is encoded by the mating type locus. Genetic, phenotypic, and transcriptional analyses of Mat2 and Znf2 provide evidence that Mat2 is a downstream transcription factor of the Cpk1 MAPK pathway whereas Znf2 functions as a more terminal hyphal morphogenesis determinant. Although the components of the MAPK pathway including Mat2 are not required for virulence in animal models, Znf2, as a hyphal morphology determinant, is a negative regulator of virulence. Further characterization of these elements and their target circuits will reveal genes controlling biological processes central to fungal development and virulence

The integration of nitrogen and carbon catabolite repression in Aspergillus nidulans requires the GATA factor AreA and an additional positive-acting element, ADA.

The expression of the structural genes of the proline utilization cluster of Aspergillus nidulans is repressed efficiently only when both repressing carbon and nitrogen sources are present. Two hypotheses can account for this fact. One is a direct or indirect competition mechanism between the positive-acting AreA GATA factor, mediating nitrogen metabolite repression, and the negative-acting CreA protein, mediating carbon catabolite repression. The second is to propose that CreA prevents the binding or activity of another, as yet unidentified, positive-acting factor, here called ADA. We show the second possibility to be the correct one, and we localize the new positive cis-acting element within 290 bp of the prnD-prnB divergent promoter

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation. ©2019, The Author(s), under exclusive licence to Springer Nature America, Inc.NIH (grant no. K99-HG009920-01)Fellowship from the Canadian Institutes for Health ResearchMIT Presidential Fellowshi