The Signaling Pathways Project (SPP) is an integrated knowledgebase that aims to make mammalian cellular signaling 'omics datasets fair and accessible for researchers. It includes biocurated data on transcriptional responses to various cellular signaling nodes, particularly in relation to COVID-19 and its impact on human cellular signaling pathways. The project emphasizes the development of consensomes to identify significant transcriptional footprints related to coronavirus infections, potentially aiding in hypothesis generation for further research.

1. The Signaling Pathways Project:
The Signaling Pathways Project, An Integrated ‘Omics Knowledgebase
For Mammalian Cellular Signaling Pathways
Scott Ochsner, PhD & Neil McKenna, PhD
Department of Molecular and Cellular Biology
Baylor College of Medicine, Houston TX

2. The Signaling Pathways Project: making cell signaling ‘omics
datasets FAIR for bench researchers
FAIR: Findable, Accessible, Interoperable, Re-usable

3. Signaling pathways involve physical and functional interactions
between distinct classes of signaling pathway nodes

4. Transcriptomic
Expression array & RNA-Seq
Cistromic
ChIP-Seq
Receptors
Enzymes
Transcription
Factors
Co-nodes
Signaling Pathways Project (SPP): two universes of biocurated
transcriptional data points mapped to cell signaling pathway nodes
SPP node types
Hs
Mm
Rn
Hs
Mm Mm

5. Consensomes: lists of genes ranked by their transcriptional
responsiveness to cellular signaling pathway nodes
Transcriptomic Cistromic (ChIP-Seq)
Human insulin receptor family

6. Consensomes: lists of genes ranked by their transcriptional
responsiveness to cellular signaling pathway nodes

7. Consensomes: lists of genes ranked by their transcriptional
responsiveness to cellular signaling pathway nodes

8. Signaling Pathways Project (SPP) and the coronavirus crisis

9. Signaling Pathways Project (SPP) and the coronavirus crisis
• How can we leverage SPP to help researchers formulate hypotheses
around the impact of CoV infection on human cellular signaling pathways?
• Biocuration of existing archived CoV infection transcriptomic datasets
• 8 Severe Acute Respiratory Syndrome coronavirus (SARS-CoV-1)
• 13 Middle East Respiratory Syndrome CoV (MERS-CoV)
• 1 SARS-CoV-2 (initially, now 5 and counting…)
• 3 human coronavirus HCoV 229E
• 6 Influenza A Virus
• Using these datasets we generated five transcriptomic consensomes (Pan-
CoV, SARS-CoV-2, SARS-CoV-2, MERS and IAV)

10. Canonical interferon-stimulated viral response genes
have elevated rankings in the Pan-CoV consensome

11. SPP consensomes identify high confidence transcriptional
footprints for cellular signaling pathway nodes & CoV infection

12. Pan CoV infection Consensome
SPP consensomes identify high confidence transcriptional
footprints for cellular signaling pathway nodes & CoV infection

13. Node consensome CoV infection consensome
Significant overlap between node and a CoV consensome is
evidence of a role for that node in the cellular response to infection

14. Node consensome CoV infection consensome
Overlap not significantly different from random (OR = 1, p > 0.05)
Significant overlap between node and a CoV consensome is
evidence of a role for that node in the cellular response to infection

15. Node consensome CoV infection consensome
Overlap significantly different from random (OR = > 1, p < 0.05)
Significant overlap between node and a CoV consensome is
evidence of a role for that node in the cellular response to infection

16. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

17. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

18. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

19. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

20. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

21. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

22. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

23. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

24. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

25. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

26. Pathway nodes with known roles in CoV and/or viral infection
have expanded transcriptional footprints in the Pan-CoV consensome

27. Transcriptional footprint expansion analysis illuminates novel aspects of
the interface between CoV infection and human cell signaling

28. Progesterone receptor signaling downregulates SARS2 infection &
interferon-stimulated inflammatory response genes
PGR full antagonist RU486

29. Sutton et al. (2020) NEJM 10.1056/NEJMc2009316
Is there a link between PR antagonism of interferon signaling and
asymptomatic presentation in SARS-CoV-2 in pregnancy?

30. Pathway nodes mediating epithelial to mesenchymal transition
have SARS-CoV-2-specific transcriptional footprint expansions

31. Pathway nodes mediating epithelial to mesenchymal transition
have SARS-CoV-2-specific transcriptional footprint expansions

32. Pathway nodes mediating epithelial to mesenchymal transition
have SARS-CoV-2-specific transcriptional footprint expansions

33. Pathway nodes mediating epithelial to mesenchymal transition
have SARS-CoV-2-specific transcriptional footprint expansions

34. Epithelial to mesenchymal transition contributes to pathological
conditions in SARS-CoV-2 infection target organ systems

35. Canonical EMT genes have elevated rankings in
the SARS-CoV-2 consensome

36. Hypothesis: epithelial to mesenchymal transition contributes
to SARS-CoV-2 infection

37. Live Demo

38. The Hypothesis Center: a dkNET-hosted, SPP-driven hypothesis
generation environment for dkNET users

39. BioRxiv pre-print on coronavirus work
& datasets on SPP site

40. Thank you
National Institute of Diabetes, Digestive & Kidney Diseases (NIDDK)
NIDDK Information Network (dkNET)
American Thyroid Association
www.signalingpathways.org
@sigpathproject