Low complexity domains of the nucleocapsid protein of SARS-CoV-2 form amyloid fibrils.

The self-assembly of the Nucleocapsid protein (NCAP) of SARS-CoV-2 is crucial for its function. Computational analysis of the amino acid sequence of NCAP reveals low-complexity domains (LCDs) akin to LCDs in other proteins known to self-assemble as phase separation droplets and amyloid fibrils. Previous reports have described NCAP's propensity to phase-separate. Here we show that the central LCD of NCAP is capable of both, phase separation and amyloid formation. Within this central LCD we identified three adhesive segments and determined the atomic structure of the fibrils formed by each. Those structures guided the design of G12, a peptide that interferes with the self-assembly of NCAP and demonstrates antiviral activity in SARS-CoV-2 infected cells. Our work, therefore, demonstrates the amyloid form of the central LCD of NCAP and suggests that amyloidogenic segments of NCAP could be targeted for drug development.

Advantages and prospects of traditional Chinese medicine in treating COVID-19

Towards the end of 2019, a novel coronavirus pneumonia (coronavirus disease 19, COVID-19) caused by SARS-CoV-2 infection emerged in Wuhan. The SARS-CoV-2 virus quickly spread across the globe, seriously affecting public health and economic development of countries worldwide. Currently, antiviral drugs developed for COVID-19 lack strong clinical trial support and the high mutation rate of the virus causes difficulties in vaccine development, thus a complex and delayed large scale role out of an efficacious vaccine. Traditional Chinese medicine (TCM) has been used for treating various conditions for thousands of years and has a unique systems theory. It can be individualized into specific therapeutic regimens according to the patients' physical condition, clinical symptoms, and other distinguishing factors. In addition, TCM often has different effects at different disease stages, thus contributing to disease prevention, treatment, and rehabilitation. Existing evidence shows that TCM has efficacy in the treatment of COVID-19. The active ingredients of TCM have various pharmacological properties, including antiviral, anti-inflammatory, and immunomodulatory activity, with clinical trials showing that these prescriptions reduce symptoms of COVID-19, promote viral clearance, and ultimately improve survival in infected patients. This article discusses the advantages and mechanisms of TCM in the treatment of COVID-19, hoping to provide a reference platform in the fight against the disease. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Longitudinal proteomic investigation of COVID-19 vaccination.

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac®) at a 28-day interval. Using TMT-based proteomics, we identified 1,715 serum and 7,342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) at baseline using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers before vaccination for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

Proteomics Investigation of Diverse Serological Patterns in COVID-19.

Serum antibodies IgM and IgG are elevated during Coronavirus Disease 2019 (COVID-19) to defend against viral attacks. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative, whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their 2-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense and that high titers of IgM might not be favorable to COVID-19 recovery.

Smart technology vs. embarrassed human: The inhibiting effect of anticipated technology embarrassment

The travel and tourism segment has recently seen some of the most considerable growth in interactive kiosks because of the COVID-19 pandemic. Consequently, it is important for companies to understand how customers feel when they are using these kiosks. This research answers the call for research of automation in tourism as a social phenomenon (Tussyadiah, 2020) by investigating the role of a social emotion – anticipated technology embarrassment. This research identifies anticipated technology embarrassment as a negative emotion that may hinder interactive kiosks' usage. Moreover, this study suggests that specific queue design and queue distractor can effectively reduce anticipated technology embarrassment. Two observational field studies and three lab experiments confirm our hypotheses.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Restriction reduction: The effects of mobility restriction on consumers’ preferences for advertisements of tourism products

Individuals frequently experience restrictions in their mobility owing to circumstances outside of their control. This paper examines the effect of mobility restrictions on individuals’ perceptions of personal freedom, and subsequent preferences for tourism advertisements. In a secondary data analysis and three experiments, we show that physical confinement triggered by restricted mobility causes individuals to psychologically feel that their personal freedoms are threatened. In turn, these experiences result in a compensatory response, where people more strongly prefer advertisements that signal scarcity-reduction over advertisements that signal control-restoration. This effect is mitigated when people are prevention-oriented and is reversed when the restrictions are enacted absolutely (without ambiguity and possible mutability). We discuss the implications of our findings for advertising practice and strategies for tourism product placement.

Humanoid versus non-humanoid robots: how mortality salience shapes preference for robot services under the COVID-19 pandemic? (Special Issue: Curated Collection: artificial intelligence and robotics in tourism.)

This paper examines the influence of mortality salience on preference for humanoid robot service. Six studies confirm that consumers/tourists are reluctant to adopt humanoid (vs. non-humanoid) service robots and robotic services when mortality is salient. The effect is driven by the perceived threat to human identity. However, temporal distance can alleviate the mortality salience effect. Eliciting a distant-future temporal perspective can reduce consumers'/tourists' existential anxiety, and then attenuate negative reactions to humanoid service robots. This research provides an innovative standpoint on consumers' reactions to service robots under conditions of mortality salience (e.g., during the COVID-19 pandemic). It also offers insight into service robot implementation and design in the hospitality and tourism industry.

Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic?

This paper examines the influence of mortality salience on preference for humanoid robot service. Six studies confirm that consumers/tourists are reluctant to adopt humanoid (vs. non-humanoid) service robots and robotic services when mortality is salient. The effect is driven by the perceived threat to human identity. However, temporal distance can alleviate the mortality salience effect. Eliciting a distant-future temporal perspective can reduce consumers'/tourists' existential anxiety, and then attenuate negative reactions to humanoid service robots. This research provides an innovative standpoint on consumers' reactions to service robots under conditions of mortality salience (e.g., during the COVID-19 pandemic). It also offers insight into service robot implementation and design in the hospitality and tourism industry.

Gut microbiota, inflammation, and molecular signatures of host response to infection.

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.

Persistence and clearance of viral RNA in 2019 novel coronavirus disease rehabilitation patients.

BACKGROUND: A patient's infectivity is determined by the presence of the virus in different body fluids, secretions, and excreta. The persistence and clearance of viral RNA from different specimens of patients with 2019 novel coronavirus disease (COVID-19) remain unclear. This study analyzed the clearance time and factors influencing 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19, providing further evidence to improve the management of patients during convalescence. METHODS: The clinical data and laboratory test results of convalescent patients with COVID-19 who were admitted to from January 20, 2020 to February 10, 2020 were collected retrospectively. The reverse transcription polymerase chain reaction (RT-PCR) results for patients' oropharyngeal swab, stool, urine, and serum samples were collected and analyzed. Convalescent patients refer to recovered non-febrile patients without respiratory symptoms who had two successive (minimum 24 h sampling interval) negative RT-PCR results for viral RNA from oropharyngeal swabs. The effects of cluster of differentiation 4 (CD4)+ T lymphocytes, inflammatory indicators, and glucocorticoid treatment on viral nucleic acid clearance were analyzed. RESULTS: In the 292 confirmed cases, 66 patients recovered after treatment and were included in our study. In total, 28 (42.4%) women and 38 men (57.6%) with a median age of 44.0 (34.0-62.0) years were analyzed. After in-hospital treatment, patients' inflammatory indicators decreased with improved clinical condition. The median time from the onset of symptoms to first negative RT-PCR results for oropharyngeal swabs in convalescent patients was 9.5 (6.0-11.0) days. By February 10, 2020, 11 convalescent patients (16.7%) still tested positive for viral RNA from stool specimens and the other 55 patients' stool specimens were negative for 2019-nCoV following a median duration of 11.0 (9.0-16.0) days after symptom onset. Among these 55 patients, 43 had a longer duration until stool specimens were negative for viral RNA than for throat swabs, with a median delay of 2.0 (1.0-4.0) days. Results for only four (6.9%) urine samples were positive for viral nucleic acid out of 58 cases; viral RNA was still present in three patients' urine specimens after throat swabs were negative. Using a multiple linear regression model (F = 2.669, P = 0.044, and adjusted R = 0.122), the analysis showed that the CD4+ T lymphocyte count may help predict the duration of viral RNA detection in patients' stools (t = -2.699, P = 0.010). The duration of viral RNA detection from oropharyngeal swabs and fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (15 days vs. 8.0 days, respectively; t = 2.550, P = 0.013) and the duration of viral RNA detection in fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (20 days vs. 11 days, respectively; t = 4.631, P < 0.001). There was no statistically significant difference in inflammatory indicators between patients with positive fecal viral RNA test results and those with negative results (P > 0.05). CONCLUSIONS: In brief, as the clearance of viral RNA in patients' stools was delayed compared to that in oropharyngeal swabs, it is important to identify viral RNA in feces during convalescence. Because of the delayed clearance of viral RNA in the glucocorticoid treatment group, glucocorticoids are not recommended in the treatment of COVID-19, especially for mild disease. The duration of RNA detection may relate to host cell immunity.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19.

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

The SARS-CoV-2 nucleocapsid protein preferentially binds long and structured RNAs (preprint)

The SARS-CoV-2 nucleocapsid protein (NCAP) functions in viral RNA genome packaging, virion assembly, RNA synthesis and translation, and regulation of host immune response. RNA-binding is central to these processes. Little is known how NCAP selects its binding partners in the myriad of host and viral RNAs. To address this fundamental question, we employed electrophoresis mobility shift and competition assays to compare NCAP binding to RNAs that are of SARS-CoV-2 vs. non-SARS-CoV-2, long vs. short, and structured vs. unstructured. We found that although NCAP can bind all RNAs tested, it primarily binds structured RNAs, and their association suppresses strong interaction with single-stranded RNAs. NCAP prefers long RNAs, especially those containing multiple structures separated by single-stranded linkers that presumably offer conformational flexibility. Additionally, all three major regions of NCAP bind RNA, including the low complexity domain and dimerization domain that promote formation of NCAP oligomers, amyloid fibrils and liquid-liquid phase separation. Combining these observations, we propose that NCAP-NCAP interactions that mediate higher-order structures during packaging also drive recognition of the genomic RNA and call this mechanism recognition-by-packaging. This study provides a biochemical basis for understanding the complex NCAP-RNA interactions in the viral life cycle and a broad range of similar biological processes.

Maternal and infant outcomes during the COVID-19 pandemic: a retrospective study in Guangzhou, China.

In late December 2019, the COVID-19 pandemic caused a great threat to people's lives worldwide. As a special category of the population, pregnant women are vulnerable during emergencies. This study was designed to explore whether or not the COVID-19 pandemic has influenced maternal and infant outcomes. We collected maternal characteristics, laboratory results, condition in the third trimester, maternal outcome, fetal or neonatal outcomes, and characteristics of amniotic fluid, umbilical cord and placenta from pregnant women and fetals or newborns in the first affiliated hospital of Jinan university from 24 January to 31 March 2020 (peak period), chose the same types of data at the hospital during the same period in 2019 and 1 January-23 January 2020 (prior to the outbreak of COVID-19 in 2020) as a control. Our study focused on uncomplicated singleton pregnancies among women not infected by COVID-19. The results demonstrated that there was not an increase in adverse outcomes of pregnant women and newborns during the COVID-19 pandemic; This might be associated with the updated design of major epidemic prevention and control systems in Guangzhou, and the extension of pregnant women's rest time during the third trimester of pregnancy. Nevertheless, the survey showed an increased incidence rate of 25-hydroxyvitamin D and zinc deficiency in newborns during the epidemic, implying that pregnant women should participate in appropriate physical exercise, increase their exposure to outdoor sunlight and improve nutrition intake to ensure healthy newborns during the quarantine period. Our study has provided some guidance for maternal management during the COVID-19 pandemic.

Maternal and Infant Outcomes during the COVID-19 Pandemic: A Retrospective Study in Guangzhou, China (preprint)

In late December 2019, the COVID-19 pandemic caused a great threat to people’s lives worldwide. As a special category of the population, pregnant women are vulnerable during emergencies. This study was designed to explore whether or not the COVID-19 pandemic has influenced maternal and infant outcomes. We collected maternal characteristics, laboratory results, condition in the third trimester, maternal outcome, fetal or neonatal outcomes, and characteristics of amniotic fluid, umbilical cord and placenta from pregnant women and fetals or newborns in the first affiliated hospital of Jinan university from 24 January to 31 March 2020 (peak period), chose the same types of data at the hospital during the same period in 2019 and 1 January − 23 January 2020 (prior to the outbreak of COVID-19 in 2020) as a control. Our study focused on uncomplicated singleton pregnancies among women not infected by COVID-19. The results demonstrated that there was not an increase in adverse outcomes of pregnant women and newborns during the COVID-19 pandemic; This might be associated with the updated design of major epidemic prevention and control systems in Guangzhou, and the extension of pregnant women’s rest time during the third trimester of pregnancy. Nevertheless, the survey showed an increased incidence rate of 25-hydroxyvitamin D and zinc deficiency in newborns during the epidemic, implying that pregnant women should participate in appropriate physical exercise, increase their exposure to outdoor sunlight and improve nutrition intake to ensure healthy newborns during the quarantine period. Our study has provided some guidance for maternal management during the COVID-19 pandemic.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

3rd International Conference on Applied Machine Learning and Data Science

We were glad to introduce you that the 2021 3rd International Conference on Applied Machine Learning and Data Science (ICAMLDS 2021), held on 14th-16th, May, 2021, Chengdu, China. This conference was sponsored by Chengdu University of Technology and Xiamen University. Affected by the COVID-19 pandemic and the implementation of the government's isolation policy, the author was unable to arrive at Chengdu conference safely and on time. Considering the safety and health of the participants, ICAMLDS 2021 was held via online collaboration tool, Tencent meeting, which is different from the traditional way. The aim of ICAMLDS is to present the latest research and results of scientists (professors, students, PhD Students, engineers, and post-doc scientists) related to applied machine learning and data science topics. This conference provides opportunities for the delegates in different fields to exchange new ideas and experiences face to face, to establish business or research relations and to find global partners for future collaboration. We were greatly honored to have invited Prof. Xiaonan Xiao from Xiamen University to serve as our Conference Chairman. He is a professor, Ph.D. advisor, and the chair of Department of Information and Computational Science at Xiamen University Tan Kah Kee College. Professor Xiao is the executive director of Biological Mathematical Society & Mathematical Society of Fujian Province. His expertise mainly focuses on the studies of complex systems modeling and optimal control. During the conference, the conference model was divided into three sessions, including keynote speeches, oral presentations, and online Q&A discussion. In the first part, keynote speakers were each allocated 30-45 minutes to hold their speeches including 5min for Q&A. Then in the second part, some scholars were given about 5-10 minutes to perform their oral presentations one by one and their submissions were selected as the excellent papers. We were honored to invite three sophisticated keynote speakers to deliver keynote speeches during the conference. Our beloved Conference Chair, Prof. Xiaonan Xiao was alsoinvited to present his talk. He delivered a wonderful speech on “New breakthroughs of the 21st Century AI Computing Life Science and Mathematical Thinking in College Students' Innovation Project”. In addition, we had Asso. Prof. Yi Gu from Chengdu University of Technology, China to present his latest research, the title of his speech is “Advances in atmospheric radon monitoring technology”;and the last keynote speaker, Prof. Yanling Zhou, from Hamburg University of Applied Sciences, Germany and her speech title is: Big data mining and analysis based on network messages. Each keynote speech lasted for 45min including 5min for Q&A, after keynote presentations, there are 12 orals presentations, oral speakers share their latest research results with the audience. Each orals presentations last for 15min including 5min for Q&A. The overall number of participants is 51. This format provided an opportunity to discuss the presented scientific results and was supplemented by Q&A sessions in common live chat and direct communication in local chats. There are some disadvantages of online conference, for example, 1) After statistics, we found that participants prefer face-to-face communication, and online meetings lack the awareness of participating in the exchange;2) Due to age differences, online meetings are rejected by older participants. Because they are not familiar with online conferences, they need the help of others to complete their participation in the conference;3) Speakers often interrupt their speeches because multiple authors ask questions online at the same time, making the speeches not reach the best results. In the last part of the conference, all participants were invited to discuss and explore the academic issues after the presentations. The discussion was lasted for about 30-60 minutes. The conference provided a forum for discussing applied machine learning and data science and in particular for prom ting the interchange of novel ideas and the presentations of the latest developments in this field. ICAMLDS-21 received more than 179 manuscripts, and 103 submissions have been accepted by our reviewers. By submitting a paper to ICAMLDS-21, the authors understood and agreed that papers would undergo a rigorous peer-review process. Manuscripts were reviewed by at least two independent, qualified experts in the field selected by the conference committee, who took detailed comments and the authors would submit a revised version in which these feedback taken into consideration. All papers were reviewed using a double-blind review process: authors declared their names and affiliations in the manuscript for the reviewers to see, but reviewers did not know each other's identities, nor did the authors receive information about who had reviewed their manuscript. On behalf of the organizing committee, I would like to especially thank all the editors from IOP Conference Series for their great support to ICAMLDS. Finally we wish all the authors and attendees of ICAMLDS a unique, rewarding and enjoyable memory at ICAMLDS in Chengdu, China. We are looking forward to your participation in the 4th ICAMLDS in 2022. With our warmest regards, ICAMLDS 2021 Organizing Committees, General Chairs, Program Chairs, International Technical Program Committees, Sponsors, Partners and this titles are available in this pdf.

Inhibition of amyloid formation of the Nucleoprotein of SARS-CoV-2 (preprint)

The SARS-CoV-2 Nucleoprotein (NCAP) functions in RNA packaging during viral replication and assembly. Computational analysis of its amino acid sequence reveals a central low-complexity domain (LCD) having sequence features akin to LCDs in other proteins known to function in liquid-liquid phase separation. Here we show that in the presence of viral RNA, NCAP, and also its LCD segment alone, form amyloid-like fibrils when undergoing liquid-liquid phase separation. Within the LCD we identified three 6-residue segments that drive amyloid fibril formation. We determined atomic structures for fibrils formed by each of the three identified segments. These structures informed our design of peptide inhibitors of NCAP fibril formation and liquid-liquid phase separation, suggesting a therapeutic route for Covid-19.

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.