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Covid-19 Special Track

Attention Presenters - please review the Speaker Information Page available here
Schedule subject to change
All times listed are in UTC
Friday, July 30th
11:00-11:10
COVID-19 Track and Panel
Format: Live-stream

Moderator(s): Jacques van Helden

  • Jacques van Helden

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Details soon!

11:10-12:05
COVID-19 Panel
Format: Live-stream

Moderator(s): Jacques van Helden

  • Time Hubbard, Kings College London, United Kingdom
  • Sebastian Mauer-Stroh, A*STAR, Singapore
  • David Carr, Wellcome Trust, United Kingdom
  • Tulio de Oliveira, University of KwaZulu-Natal, South Africa
12:05-12:20
WikiPathways as a platform for COVID-19 pathway models
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • Nhung Pham, Maastricht University, Netherlands
  • Finterly Hu, Maastricht University, Netherlands
  • Friederike Ehrhart, Maastricht University, Netherlands
  • Egon Willighagen, Maastricht University, Netherlands
  • Alexander Pico, The Gladstone Institutes, UCSF, United States
  • Chris Evelo, Maastricht University, Netherlands
  • Martina Kutmon, Maastricht University, Netherlands

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COVID-19 is causing severe health problems all over the world. To identify effective treatments for COVID-19, detailed pathway models to analyze, understand, and predict downstream effects are essential. In the COVID-19 Disease Map project, pathway curators and repositories joined forces to build a knowledge repository of molecular mechanisms of COVID-19. The project aims to describe intensively curated molecular pathways depicting host-virus interactions useful for data analysis and modeling. WikiPathways (www.wikipathways.org), an established community-curated pathway database, has been a core contributor from the start.

Using pathway models from WikiPathways and the COVID-19 Disease Map, we developed an automated R workflow using pathway and network analysis approaches to analyzed transcriptomics datasets focussing on pathway crosstalk between virus-related and host-immune response processes. The workflow also enables the extension of pathways with drug-target information, and the identification of missing knowledge in our pathway models, which can be used to target new investigations.

With this project, we highlight WikiPathways and COVID-19 Disease Map as central resources for COVID-19 related pathway information. The development of automated and reproducible data analysis workflows is essential to further expand our understanding of this virus infection in parallel with the incoming information and availability of datasets.

12:40-12:55
Revealing SARS-CoV-2 protein architectures and function by integrating modeling and in situ MS proteomics
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • Nir Kalisman, The Hebrew University of Jerusalem, Israel
  • Dina Schneidman, The Hebrew University of Jerusalem, Israel
  • Michal Linial, The Hebrew University of Jerusalem, Israel

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The genome of SARS-CoV-2, the causal virus of the COVID-19 pandemic, encodes 29 proteins. However, only a handful of them is associated with structure and function. In this study, we utilize a novel application called in situ cross-linking mass spectrometry (in situ CLMS) that provides rich spatial information on the structures of proteins as they occur in intact cells. We demonstrate the utility of this approach by targeting three SARS-CoV-2 proteins for which full atomic structures are missing. We show that integrating cross-links with external structural data is sufficient to model the full-length protein. Cells that expressed tagged-Nsp1 were subjected to in situ CLMS approach. We identified the interactions of Nsp1 with the 40S ribosomal subunit which confirms its fundamental role in blocking translation of infected cells. Similarly, based on structure predictions of individual domains for Nsp2 by AlphaFold2, we successfully assembled Nsp2 into a single consistent model. The Nucleocapsid (N) protein plays a key role in genome packing and virion assembly. Using in situ CLMS was fundamental to assemble a model of the full dimer from available 3D structures of individual domains. These results highlight the importance of cellular context for achieving detailed atomic resolution of SARS-CoV-2 proteins.

12:55-13:10
Host stress granule hijacking by Coronavirus nucleocapsids: Potential roles of protein intrinsic disorder and liquid-liquid phase separation
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • Mahdi Moosa, University at Buffalo, United States
  • Priya Banerjee, University at Buffalo, United States

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Nucleocapsid protein (N) of coronaviruses play multifaceted roles in the viral life cycle. In its structural role, N protein binds to the RNA genome in a beads-on-a-string fashion. In its non-structural role, N protein plays crucial roles in viral replication through its association with host RNA binding proteome. To understand the molecular basis of such multi-functionality, we performed sequence analysis of the N protein and found that the N protein harbors multiple disordered domains. These disordered regions are enriched in amino acids that have high π-π contact potential, a sequence feature that drives liquid-liquid phase separation of many human RNA binding proteins. Interestingly, our analysis of the N protein’s host interactomes reveal a significant enrichment of stress granule proteins that also have multivalent π interaction sites. Based on these analyses as well as recent experimental observations, we propose that the disordered domains of coronavirus N protein engage with host stress granule ribonucleoproteins to facilitate virus replication through protein co-condensation.

13:10-13:25
The Phosphorylation Model of SARS-CoV-2 Nucleocapsid Protein
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • Tomer M Yaron, Weill Cornell Medicine, United States
  • Brook E Heaton, Duke University School of Medicine, United States
  • Tyler M Levy, Cell Signaling Technology, Inc., United States
  • Jared L Johnson, Weill Cornell Medicine, United States
  • Tristan X Jordan, Icahn School of Medicine at Mount Sinai, United States
  • Benjamin R Tenoever, Icahn School of Medicine at Mount Sinai, United States
  • John Blenis, Weill Cornell Medicine, United States
  • Elena Piskounova, Weill Cornell Medicine, United States
  • Nicholas S Heaton, Duke University School of Medicine, United States
  • Lewis C Cantley, Weill Cornell Medicine, United States

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While vaccines are vital for preventing COVID-19 infections, it is critical to develop new therapies to treat patients who become infected. Pharmacological targeting of a host factor required for viral replication can suppress viral spread with a low probability of viral mutation leading to resistance. In particular, host kinases are highly druggable targets and a number of conserved coronavirus proteins, notably the nucleoprotein (N), require phosphorylation for full functionality.

In order to understand how targeting kinases could be used to compromise viral replication, we used a combination of phosphoproteomics and bioinformatics as well as genetic and pharmacological kinase inhibition to define the enzymes important for SARS-CoV-2 N protein phosphorylation and viral replication. From these data, we propose a model whereby SRPK1/2 initiates phosphorylation of the N protein, which primes for further phosphorylation by GSK-3A/B and CK1 to achieve extensive phosphorylation of the N protein SR-rich domain. Importantly, we were able to leverage our data to identify an FDA-approved kinase inhibitor, Alectinib, that suppresses N phosphorylation by SRPK1/2 and limits SARS-CoV-2 replication.

Together, these data suggest that repurposing or developing novel host-kinase directed therapies may be an efficacious strategy to prevent or treat COVID-19 and other coronavirus-mediated diseases.

13:25-13:40
Efficient Algorithms for Optimized mRNA Sequence Design
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • He Zhang, Baidu Research USA, United States
  • Liang Zhang, Baidu Research USA; Oregon State University, United States
  • Ang Lin, Stemirna Therapeutics Inc., China
  • Ziyu Li, Baidu Research USA, United States
  • Congcong Xu, Stemirna Therapeutics Inc., China
  • Kaibo Liu, Baidu Research USA, United States
  • Boxiang Liu, Baidu Research USA, United States
  • Xiaopin Ma, Stemirna Therapeutics Inc., China
  • Fanfan Zhao, Stemirna Therapeutics Inc., China
  • Hangwen Li, Stemirna Therapeutics Inc., China
  • David Mathews, University of Rochester, United States
  • Yujian Zhang, Stemirna Therapeutics Inc., China
  • Liang Huang, Baidu Research USA; Oregon State University, United States

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A messenger RNA (mRNA) vaccine can benefit from an mRNA sequence that is stable and highly productive in protein expression, which have been shown to be correlated to greater mRNA secondary structure folding stability and optimal codon usage. However, sequence design remains a hard problem due to the exponentially many synonymous mRNA sequences that encode the same protein. We propose and implement an efficient algorithm that can solve this problem in O(n^3)-time theoretically, where n is the mRNA sequence length. We observe that this algorithm achieves quadratic-runtime in practice when n<8,000, and can design SARS-CoV-2 spike genome in 7.9 mins. We further develop a linear-time approximate version, LinearDesign, based on beam pruning heuristics, which can finish spike genome design in 4 mins with only 5.5% MFE loss. We also extend this algorithm for incorporating the codon optimality, which can jointly optimize folding free energy and codon usage. Our novel algorithm enlarges the design space greatly, reaching a large region that has never been explored before. We design seven mRNA sequences of the SARS-CoV-2 spike protein, which perform better on chemical stability, protein expression and immunogenicity than the codon-optimized benchmark in wet-lab assays.

13:40-13:55
A Machine Learning approach for pre-miRNA discovery in SARS-CoV-2
Format: Pre-recorded with live Q&A

Moderator(s): Shoshana Wodak

  • Gabriela Merino, SINC-CONICET-FICH-UNL/IIB-UNER, Argentina
  • Leandro Bugnon, SINC-CONICET-FICH-UNL, Argentina
  • Jonathan Raad, SINC-CONICET-FICH-UNL, Argentina
  • Federico Ariel, IAL-CONICET-UNL, Argentina
  • Diego Milone, SINC-CONICET-FICH-UNL, Argentina
  • Georgina Stegmayer, SINC-CONICET-FICH-UNL, Argentina

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We have developed a novel approach based on machine learning (ML) for identifying precursors of microRNAs (pre-miRNAs) in the genome of the novel coronavirus SARS-CoV-2. The discovery of miRNAs in the novel virus is of high importance in the context of the current sanitary crisis for the improvement of diagnostic and treatment strategies. For the discovery of pre-miRNAs 3 ML methods were used in combination: a novel deep convolutional neural network (mirDNN), a deep self-organizing map (deeSOM), and a one-class support vector machine (OC-SVM). Each method provided a list of candidates to potential pre-miRNAs in the viral genome, supported by a score. In this study, pre-miRNAs were identified as those having scores in the top 10th percentile in all methods. With this approach, 12 candidate structures were discovered in the viral genome and validated with small RNA-seq data. The expression of 8 mature miRNAs-like sequences was confirmed from SARS-CoV-2 infected human cells. The predicted miRNAs were found as targeting a subset of human genes of which 109 are transcriptionally deregulated upon infection, and 28 of those genes are down-regulated in infected human cells and related to respiratory diseases and viral infection, previously associated with SARS-CoV-1 and SARS-CoV-2.

14:20-14:35
Published Anti-SARS-CoV-2 In Vitro Hits Share Common Mechanisms of Action that Synergize with Antivirals
Format: Pre-recorded with live Q&A

Moderator(s): Thomas Lengauer

  • Jing Xing, Michigan State University, United States
  • Shreya Paithankar, MSU, United States
  • Ke Liu, Michigan State University, United States
  • Katie Uhl, MSU, United States
  • Xiaopeng Li, MSU, United States
  • Meehyun Ko, Institut Pasteur Korea, South Korea
  • Seungtaek Kim, Institut Pasteur Korea, South Korea
  • Jeremy Haskins, MSU, United States
  • Bin Chen, MSU, United States

Presentation Overview: Show

As of February 15th, 2021, SARS-CoV-2 has infected 108 million people and claimed 2 million lives. Vaccines are promising for a cure, yet the emerging mutations of SARS-COV-2 impose challenges to target the virus proteins; thus targeting host cells remains a viable therapeutic approach. The global efforts in the trailing months have led to the discovery of at least 184 drug repurposing candidates in vitro. Gaining additional insights into their mechanisms will facilitate an enhanced understanding of infection and the development of better therapeutics for COVID-19.

Leveraging large-scale drug-induced gene expression profiles from the Library of Integrated Network-Based Cellular Signatures (LINCS) project, we identified 63 genes specifically dysregulated by anti-SARS-CoV-2 active compounds, which was computationally validated with five independent studies of CRISPR screening for pro-viral host factors. The expression change of a few genes upon drug treatment was further confirmed in small lung airway cells. Interestingly, nearly 40% of the active compounds change the expression of 11 key genes from the cholesterol homeostasis or microtubule cytoskeleton organization pathway. The gene expression pattern of these pathways (e.g. up-regulation of NPC1 and HMGCS1, down-regulation of CCNB1 and TUBB6) is associated with COVID-19 patient severity, i.e. these genes were reversely dysregulated in patient transcriptome change. Screening the FDA-approved drugs with this pattern discovered monensin, a polyether antibiotic, as a potential hit and following experiments confirmed its efficacy in Vero-E6 cells infected by SARS-CoV-2 (IC50 of 11 µM and CC50 > 50 µM). Furthermore, compounds with a similar pattern of gene expression changing are more likely to present a synergistic effect with known antivirals such as remdesivir and arbidol. For example, the combination of coronavirus RdRp inhibitor remdesivir and anti-inflammatory drug cepharanthine inhibited > 95% cytopathic effect at 2.5 µM, while only 10-30% for each single agent.

In summary, our survey of the positive in vitro hits reveals their common mechanistic effects through the regulation of cholesterol homeostasis and microtubule cytoskeleton organization, which also associates with patient severity, and correlates with antiviral drug synergism.

14:35-14:50
Network controllability analysis for drug repurposing in COVID-19
Format: Pre-recorded with live Q&A

Moderator(s): Thomas Lengauer

  • Nicoleta Siminea, National Institute of Research and Development for Biological Sciences, and University of Bucharest, Romania
  • Victor Popescu, Department of Information Technologies, Abo Akademi University, Turku, Finland
  • Jose Angel Sanchez Martin, Department of Computer Science, Technical University of Madrid, Spain
  • Daniela Florea, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Georgiana Gavril, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Ana-Maria Gheorghe, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Corina Itcus, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Krishna Kanhaiya, Department of Information Technologies, Abo Akademi University, Turku, Finland
  • Octavian Pacioglu, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Laura Ioana Popa, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Romica Trandafir, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Maria Iris Tusa, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Manuela Sidoroff, National Institute of Research and Development for Biological Sciences, Bucharest, Romania
  • Mihaela Paun, National Institute of Research and Development for Biological Sciences, and University of Bucharest, Romania
  • Eugen Czeizler, Abo Akademi University, Turku, and National Institute of Research and Development for Biological Sciences, Bucharest, Finland
  • Andrei Paun, National Institute of Research and Development for Biological Sciences, and University of Bucharest, Romania
  • Ion Petre, University of Turku, and National Institute of Research and Development for Biological Sciences, Bucharest, Romania

Presentation Overview: Show

We investigated a network-based approach to drug repurposing in COVID-19. The focus of our analysis is on two recently published sets of genes whose loss of function led to enrichments in two experiments (low/high multiplicity of infection) of cell survivability on SARS-CoV-2 infected cells. We constructed a directed protein-protein interaction network for each of these sets of host factors, including proteins upstream of the host factors at a distance at most 2, and proteins downstream of drug targets at a distance at most 2. We used interaction data from KEGG, OmniPath and SIGNOR. Using targeted network controllability and the NetControl4BioMed platform we identified control paths of length at most three starting in drug targets and controlling the set of host factors. We focused on the drugs predicted to be most efficient in terms of the highest number of host factors they can control. We obtained 130 drugs (antineoplastic and immunomodulating agents, antithrombotic agents, sex hormones and other compounds) that we validated against existing experimental data (including viral entry, viral replication, in vitro infectivity, life virus infectivity and human cell toxicity) and clinical trials results. We conclude that network modeling methods can be efficient in drug repurposing studies for COVID-19.

14:50-15:05
Ongoing Global and Regional Adaptive Evolution of SARS-CoV-2
Format: Pre-recorded with live Q&A

Moderator(s): Thomas Lengauer

  • Nash Rochman, The National Institutes of Health, United States
  • Yuri Wolf, The National Institutes of Health, United States
  • Guilhem Faure, The Broad Institute of MIT and Harvard, United States
  • Pascal Mutz, The National Institutes of Health, United States
  • Feng Zhang, The Broad Institute of MIT and Harvard, United States
  • Eugene Koonin, The National Institutes of Health, United States

Presentation Overview: Show

We analyzed more than 300,000 genomes of SARS-CoV-2 variants available as of January 2021. We demonstrate the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, with a set of sites evolving under positive selection. The receptor-binding domain of the spike protein and the nuclear localization signal (NLS) associated region of the nucleocapsid protein are enriched with positively selected mutations. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Analysis of the phylogenetic distances between pairs of regions reveals four distinct periods of the pandemic linked to the emergence of key mutations. First, rapid diversification into region-specific phylogenies ending February 2020. A major extinction event and global homogenization concomitant with the spread of D614G in the spike protein followed, ending March 2020. NLS associated variants across multiple partitions rose to global prominence March-July, during a period of stasis in terms of inter-regional diversity. Finally, beginning July 2020, multiple mutations, some of which enable antibody evasion, began to emerge associated with ongoing regional diversification. Understanding these trends, which might be indicative of speciation, are paramount to both ongoing and future public health responses.

15:05-15:20
A Machine Learning Model for Predicting Deterioration of COVID-19 Inpatients
Format: Pre-recorded with live Q&A

Moderator(s): Thomas Lengauer

  • Omer Noy, Tel-Aviv University, Israel
  • Dan Coster, Tel-Aviv University, Israel
  • Maya Metzger, Tel-Aviv University, Israel
  • Itai Attar, Tel-Aviv University, Israel
  • Shani Shenhar-Tsafraty, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
  • Shlomo Berliner, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
  • Galia Rahav, Sheba Medical Center, Tel-Aviv University, Israel
  • Ori Rogowski, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
  • Ron Shamir, Tel-Aviv University, Israel

Presentation Overview: Show

The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by analyzing electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In prediction of deterioration within the next 7-30 hours, the model achieved an area under the ROC curve of 0.84 and area under the precision-recall curve of 0.74. It achieved values of 0.76 and 0.7 respectively in external validation on data from a different hospital.



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