We are pleased to announce the second Cold Spring Harbor winter 2020 conference on JAK-STAT Pathways in Health & Disease, which will begin at 7:30 pm on Monday, April 6 and run through lunch on Thursday, April 9. 

Topics:

  • Jak-STAT Mutations and Genomic Functions
  • JAK-STAT Signaling in the Hematopoietic System
  • STAT3, Metabolism and Cancer
  • JAK-STAT Signaling and Regulation
  • Transcriptional and Epigenetic Regulation by STATs
  • JAK-STAT Inhibition

https://meetings.cshl.edu/meetings.aspx?meet=stats&year=20

cold spring harbor labratory

Therapeutic targeting of STAT3 by monobodies

An international consortium headed by the Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences ETH Lausanne collaborated with Veronika Sexl group members and developed the first monobodies targeted against STAT3. Monobodies are synthetic binding proteins engineered to selectively bind intracellular proteins – also those lacking an enzymatic domain. High affinity STAT3 monobodies were identified in a combinatorial phage and yeast display library sorting screen. The authors show selective interference of the monobodies with cellular STAT3 activity. This is an attractive therapeutic option due to STAT3’s homology with other STAT proteins that complicates the development of selective inhibitors.

Published in Nature Communications

Grégory La Sala, Camille Michiels, Tim Kükenshöner, Tania Brandstoetter, Barbara Maurer, Akiko Koide, Kelvin Lau, Florence Pojer, Shohei Koide, Veronika Sexl, Laure Dumoutier & Oliver Hantschel

Selective inhibition of STAT3 signaling using monobodies targeting the coiled-coil and N-terminal domains

Doi: https://doi.org/10.1038/s41467-020-17920-z

 

Nikolaus Fortelny and Christoph Bock of CeMM showed the usefulness of knowledge-primed neural networks (KPNNs) for the interpretation of single-cell RNA-seq data. They expect that the use of deep learning on biological networks will also be relevant in other areas of biomedicine analysing big data sets, including metabolomics, proteomics and cellular or cognitive networks.

Published in Genome Biology

Nikolaus Fortelny and Christoph Bock

Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data

Doi: https://doi.org/10.1186/s13059-020-02100-5