Cell membrane proteins with high N-glycosylation, high expression and multiple interaction partners are preferred by mammalian viruses as receptors

GateView: A Multi-Omics Platform for Gene Feature Analysis of Virus Receptors within Human Normal Tissues and Tumors

Viruses are obligate intracellular parasites that rely on cell surface receptor molecules to complete the first step of invading host cells. The experimental method for virus receptor screening is time-consuming, and receptor molecules have been identified for less than half of known viruses. This study collected known human viruses and their receptor molecules. Through bioinformatics analysis, common characteristics of virus receptor molecules (including sequence, expression, mutation, etc.) were obtained to study why these membrane proteins are more likely to become virus receptors. An in-depth analysis of the cataloged virus receptors revealed several noteworthy findings. Compared to other membrane proteins, human virus receptors generally exhibited higher expression levels and lower sequence conservation. These receptors were found in multiple tissues, with certain tissues and cell types displaying significantly higher expression levels. While most receptor molecules showed noticeable age-related variations in expression across different tissues, only a limited number of them exhibited gender-related differences in specific tissues. Interestingly, in contrast to normal tissues, virus receptors showed significant dysregulation in various types of tumors, particularly those associated with dsRNA and retrovirus receptors. Finally, GateView, a multi-omics platform, was established to analyze the gene features of virus receptors in human normal tissues and tumors. Serving as a valuable resource, it enables the exploration of common patterns among virus receptors and the investigation of virus tropism across different tissues, population preferences, virus pathogenicity, and oncolytic virus mechanisms.

Cellular receptors for mammalian viruses

The interaction of viral surface components with cellular receptors and other entry factors determines key features of viral infection such as host range, tropism and virulence. Despite intensive research, our understanding of these interactions remains limited. Here, we report a systematic analysis of published work on mammalian virus receptors and attachment factors. We build a dataset twice the size of those available to date and specify the role of each factor in virus entry. We identify cellular proteins that are preferentially used as virus receptors, which tend to be plasma membrane proteins with a high propensity to interact with other proteins. Using machine learning, we assign cell surface proteins a score that predicts their ability to function as virus receptors. Our results also reveal common patterns of receptor usage among viruses and suggest that enveloped viruses tend to use a broader repertoire of alternative receptors than non-enveloped viruses, a feature that might confer them with higher interspecies transmissibility.

Single-nucleus transcriptome inventory of giant panda reveals cellular basis for fitness optimization under low metabolism

BACKGROUND

Energy homeostasis is essential for the adaptation of animals to their environment and some wild animals keep low metabolism adaptive to their low-nutrient dietary supply. Giant panda is such a typical low-metabolic mammal exhibiting species specialization of extremely low daily energy expenditure. It has low levels of basal metabolic rate, thyroid hormone, and physical activities, whereas the cellular bases of its low metabolic adaptation remain rarely explored.

RESULTS

In this study, we generate a single-nucleus transcriptome atlas of 21 organs/tissues from a female giant panda. We focused on the central metabolic organ (liver) and dissected cellular metabolic status by cross-species comparison. Adaptive expression mode (i.e., AMPK related) was prominently displayed in the hepatocyte of giant panda. In the highest energy-consuming organ, the heart, we found a possibly optimized utilization of fatty acid. Detailed cell subtype annotation of endothelial cells showed the uterine-specific deficiency of blood vascular subclasses, indicating a potential adaptation for a low reproductive energy expenditure.

CONCLUSIONS

Our findings shed light on the possible cellular basis and transcriptomic regulatory clues for the low metabolism in giant pandas and helped to understand physiological adaptation response to nutrient stress.

Prediction of mammalian virus cross-species transmission based on host proteins

Most emerging viruses are spilled over from mammals. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of newly emerging viruses. This study systematically investigated the host proteins associated with the cross-species transmission of mammalian viruses based on 1,271 pairs of virus-mammal interactions including 382 viruses from 33 viral families and 73 mammal species from 11 orders. Numerous host proteins were found to contribute to the cross-species transmission of mammalian viruses. Host proteins potentially contributing to virus cross-species transmission are specific to viral families, and few overlaps of such host proteins are observed in different viral families. Based on these host proteins, the random-forest (RF) models were built to predict the cross-species transmission potential of mammalian viruses. Moderate performance was obtained when using all viruses together. However, when modeling by viral family, the performance of the RF models varied much among viral families. In 13 viral families such as Flaviviridae, Retroviridae, and Poxviridae, the AUC of the RF model was greater than 0.8. Finally, the contribution of virus receptors to cross-species transmission was evaluated, and the virus receptor was found to have a minor effect in predicting the cross-species transmission of mammalian viruses. The study deepens our understanding of the mechanism of virus cross-species transmission and provides a framework for predicting the cross-species transmission of mammalian viruses. IMPORTANCE Emerging viruses pose serious threats to humans. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of emerging viruses. This study systematically identified host factors associated with cross-species transmission of mammalian viruses and further built machine-learning models for predicting cross-species transmission of the viruses based on host factors including virus receptors. The study not only deepens our understanding of the mechanism of virus cross-species transmission but also provides a framework for predicting the cross-species transmission of mammalian viruses based on host factors.

The Transcriptome Landscape of the In Vitro Human Airway Epithelium Response to SARS-CoV-2

Airway-liquid interface cultures of primary epithelial cells and of induced pluripotent stem-cell-derived airway epithelial cells (ALI and iALI, respectively) are physiologically relevant models for respiratory virus infection studies because they can mimic the in vivo human bronchial epithelium. Here, we investigated gene expression profiles in human airway cultures (ALI and iALI models), infected or not with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), using our own and publicly available bulk and single-cell transcriptome datasets. SARS-CoV-2 infection significantly increased the expression of interferon-stimulated genes (IFI44, IFIT1, IFIT3, IFI35, IRF9, MX1, OAS1, OAS3 and ISG15) and inflammatory genes (NFKBIA, CSF1, FOSL1, IL32 and CXCL10) by day 4 post-infection, indicating activation of the interferon and immune responses to the virus. Extracellular matrix genes (ITGB6, ITGB1 and GJA1) were also altered in infected cells. Single-cell RNA sequencing data revealed that SARS-CoV-2 infection damaged the respiratory epithelium, particularly mature ciliated cells. The expression of genes encoding intercellular communication and adhesion proteins was also deregulated, suggesting a mechanism to promote shedding of infected epithelial cells. These data demonstrate that ALI/iALI models help to explain the airway epithelium response to SARS-CoV-2 infection and are a key tool for developing COVID-19 treatments.

Serum ACE2 Level is Associated With Severe SARS-CoV-2 Infection: A Cross-Sectional Observational Study

Objectives:

Angiotensin-converting enzyme 2 (ACE2) represents the primary receptor for SARS-CoV-2 to enter endothelial cells, causing coronavirus disease of 2019 (COVID-19). In this study, we investigate the association between circulating ACE2 levels with the severity of COVID-19.

Methods:

Serum ACE2 levels were measured in 144 COVID-19-positive subjects at hospital admission, and 123 COVID-19-negative control subjects. The association between ACE2 and clinical outcomes was analyzed.

Results:

About 144 COVID-19 patients and 123 healthy controls data were analyzed, the mean age of patients was 62 years and 50% of them were males. The mean age of the control group was 55 years and 63% were males. ACE-II level was measured and compared between COVID-19 patients and controls and revealed no significant differences (P > .05). ACE-II level was measured in COVID-19 patients and compared between different patient’s subgroups, ACE II level was not dependent on gender, smoking, ACE intake, or comorbidities (P > .05), and was significantly correlated with cardiovascular diseases (CVS) (P-value = .046) ICU admission (P-value = .0007) and Death (P-value = .0082).

Conclusion:

There was no significant difference between the COVID-19 and Control group, however, ACE2 serum level was significantly higher in patients with COVID-19 who were critically ill or non-survivors, its increased level is also associated with length of stay. Elevated ACE2 level is associated with the severity of COVID-19 disease, and it has the potential to be a predictor of the severity of the disease.

Screening of cell-virus, cell-cell, gene-gene crosstalk among animal kingdom at single cell resolution

BACKGROUND

The exact animal origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains obscure and understanding its host range is vital for preventing interspecies transmission.

METHODS

Herein, we applied single-cell sequencing to multiple tissues of 20 species (30 data sets) and integrated them with public resources (45 data sets covering 26 species) to expand the virus receptor distribution investigation. While the binding affinity between virus and receptor is essential for viral infectivity, understanding the receptor distribution could predict the permissive organs and tissues when infection occurs.

RESULTS

Based on the transcriptomic data, the expression profiles of receptor or associated entry factors for viruses capable of causing respiratory, blood, and brain diseases were described in detail. Conserved cellular connectomes and regulomes were also identified, revealing fundamental cell-cell and gene-gene cross-talks from reptiles to humans.

CONCLUSIONS

Overall, our study provides a resource of the single-cell atlas of the animal kingdom which could help to identify the potential host range and tissue tropism of viruses and reveal the host-virus co-evolution.

An atlas of human viruses provides new insights into diversity and tissue tropism of human viruses

MOTIVATION

Viruses continue to threaten human health. Yet, the complete viral species carried by humans and their infection characteristics have not been fully revealed.

RESULTS

This study curated an atlas of human viruses from public databases and literature, and built the Human Virus Database (HVD). The HVD contains 1,131 virus species of 54 viral families which were more than twice the number of the human-infecting virus species reported in previous studies. These viruses were identified in human samples including 68 human tissues, the excreta and body fluid. The viral diversity in humans was age-dependent with a peak in the infant and a valley in the teenager. The tissue tropism of viruses was found to be associated with several factors including the viral group (DNA, RNA or reverse-transcribing viruses), enveloped or not, viral genome length and GC content, viral receptors and the virus-interacting proteins. Finally, the tissue tropism of DNA viruses was predicted using a random-forest algorithm with a middle performance. Overall, the study not only provides a valuable resource for further studies of human viruses, but also deepens our understanding towards the diversity and tissue tropism of human viruses.

AVAILABILITY

The HVD is available at http://computationalbiology.cn/humanVirusBase/#/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolutionary history of type II transmembrane serine proteases involved in viral priming

Type II transmembrane serine proteases (TTSPs) are a family of trypsin-like membrane-anchored serine proteases that play key roles in the regulation of some crucial processes in physiological conditions, including cardiac function, digestion, cellular iron homeostasis, epidermal differentiation, and immune responses. However, some of them, in particular TTSPs expressed in the human airways, were identified as host factors that promote the proteolytic activation and spread of respiratory viruses such as influenza virus, human metapneumovirus, and coronaviruses, including SARS-CoV-2. Given their involvement in viral priming, we hypothesized that members of the TTSP family may represent targets of positive selection, possibly as the result of virus-driven pressure. Thus, we investigated the evolutionary history of sixteen TTSP genes in mammals. Evolutionary analyses indicate that most of the TTSP genes that have a verified role in viral proteolytic activation present signals of pervasive positive selection, suggesting that viral infections represent a selective pressure driving the evolution of these proteases. We also evaluated genetic diversity in human populations and we identified targets of balancing selection in TMPRSS2 and TMPRSS4. This scenario may be the result of an ancestral and still ongoing host–pathogen arms race. Overall, our results provide evolutionary information about candidate functional sites and polymorphic positions in TTSP genes.

VThunter: a database for single-cell screening of virus target cells in the animal kingdom

Viral infectious diseases are a devastating and continuing threat to human and animal health. Receptor binding is the key step for viral entry into host cells. Therefore, recognizing viral receptors is fundamental for understanding the potential tissue tropism or host range of these pathogens. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology has paved the way for studying the expression of viral receptors in different tissues of animal species at single-cell resolution, resulting in huge scRNA-seq datasets. However, effectively integrating or sharing these datasets among the research community is challenging, especially for laboratory scientists. In this study, we manually curated up-to-date datasets generated in animal scRNA-seq studies, analyzed them using a unified processing pipeline, and comprehensively annotated 107 viral receptors in 142 viruses and obtained accurate expression signatures in 2 100 962 cells from 47 animal species. Thus, the VThunter database provides a user-friendly interface for the research community to explore the expression signatures of viral receptors. VThunter offers an informative and convenient resource for scientists to better understand the interactions between viral receptors and animal viruses and to assess viral pathogenesis and transmission in species. Database URL: https://db.cngb.org/VThunter/.

N-Linked Glycosylation on Anthrax Toxin Receptor 1 Is Essential for Seneca Valley Virus Infection

Seneca Valley virus (SVV) is a picornavirus with potency in selectively infecting and lysing cancerous cells. The cellular receptor for SVV mediating the selective tropism for tumors is anthrax toxin receptor 1 (ANTXR1), a type I transmembrane protein expressed in tumors. Similar to other mammalian receptors, ANTXR1 has been shown to harbor N-linked glycosylation sites in its extracellular vWA domain. However, the exact role of ANTXR1 glycosylation on SVV attachment and cellular entry was unknown. Here we show that N-linked glycosylation in the ANTXR1 vWA domain is necessary for SVV attachment and entry. In our study, tandem mass spectrometry analysis of recombinant ANTXR1-Fc revealed the presence of complex glycans at N166, N184 in the vWA domain, and N81 in the Fc domain. Symmetry-expanded cryo-EM reconstruction of SVV-ANTXR1-Fc further validated the presence of N166 and N184 in the vWA domain. Cell blocking, co-immunoprecipitation, and plaque formation assays confirmed that deglycosylation of ANTXR1 prevents SVV attachment and subsequent entry. Overall, our results identified N-glycosylation in ANTXR1 as a necessary post-translational modification for establishing stable interactions with SVV. We anticipate our findings will aid in selecting patients for future cancer therapeutics, where screening for both ANTXR1 and its glycosylation could lead to an improved outcome from SVV therapy.

Prediction of Virus-Receptor Interactions Based on Improving Similarities

Viral infectious diseases have been seriously threatening human health. The receptor binding is the first step of viral infection. Predicting virus-receptor interactions will be helpful for the interaction mechanism of viruses and receptors, and further find some effective ways of preventing and treating viral infectious diseases so as to reduce the morbidity and mortality caused by viruses. Some computation algorithms have been proposed for identifying potential virus-receptor interactions. However, a common problem in those methods is the presence of noise in the similarity network. A new computational model (Network Enhancement and the Regularized Least Squares [NERLS]) is proposed to predict virus-receptor interactions based on improving similarities by Network Enhancement (NE). NERLS integrates the virus sequence similarity, the receptor sequence similarity and known virus-receptor interactions. We compute the virus sequence similarity and known virus-receptor interactions to construct the virus similarity network. The receptor similarity network is constructed by the Gaussian interaction profile kernel similarity and the receptor sequence similarity. To obtain the final virus similarity network and the final receptor similarity network, NE is, respectively, applied for reducing the noise of the virus similarity network and the receptor similarity network. Finally, NERLS employs the regularized least squares to predict interactions of viruses and receptors. The experiment results show that NERLS achieves the area under curve value of 0.893 and 0.921 in 10-fold cross-validation and leave-one-out cross-validation, respectively, which is consistently superior to four related methods [which include Initial interaction scores method via the neighbors and the Laplacian regularized Least Square (IILLS), Bi-random walk on a heterogeneous network (BRWH), Laplacian regularized least squares classifier (LapRLS), and Collaborative matrix factorization (CMF)]. Furthermore, a case study also demonstrates that NERLS effectively predicts potential virus-receptor interactions.

Phage protein receptors have multiple interaction partners and high expressions

MOTIVATION

Receptors on host cells play a critical role in viral infection. How phages select receptors is still unknown.

RESULTS

Here, we manually curated a high-quality database named phageReceptor, including 427 pairs of phage-host receptor interactions, 341 unique viral species or sub-species and 69 bacterial species. Sugars and proteins were most widely used by phages as receptors. The receptor usage of phages in Gram-positive bacteria was different from that in Gram-negative bacteria. Most protein receptors were located on the outer membrane. The phage protein receptors (PPRs) were highly diverse in their structures, and had little sequence identity and no common protein domain with mammalian virus receptors. Further functional characterization of PPRs in Escherichia coli showed that they had larger node degrees and betweennesses in the protein-protein interaction network, and higher expression levels, than other outer membrane proteins, plasma membrane proteins or other intracellular proteins. These findings were consistent with what observed for mammalian virus receptors reported in previous studies, suggesting that viral protein receptors tend to have multiple interaction partners and high expressions. The study deepens our understanding of virus-host interactions.

AVAILABILITY AND IMPLEMENTATION

phageReceptor is publicly available from: http://www.computationalbiology.cn/phageReceptor/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Additionally, general treatments, coronavirus-specific treatments, and antiviral treatments useful in fighting COVID-19 are addressed. This review sets out to shed light on the SARS-CoV-2 and host receptor recognition, a crucial factor for successful virus infection and taking immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein of SARS-CoV-2. A variety of improved or new approaches also have been developed. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection. Moreover, the genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three-dimensional structure of the Main protease (Mpro) is available. The reported structure of the target Mpro was described in this review to identify potential drugs for COVID-19 using virtual high throughput screening.

COVID-19 and the Kidney: From Epidemiology to Clinical Practice

The new respiratory infectious disease coronavirus disease 2019 (COVID-19) that originated in Wuhan, China, in December 2019 and caused by a new strain of zoonotic coronavirus, named severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), to date has killed over 630,000 people and infected over 15,000,000 worldwide. Most of the deceased patients had pre-existing comorbidities; over 20% had chronic kidney disease (CKD). Furthermore, although SARS-CoV-2 infection is characterized mainly by diffuse alveolar damage and acute respiratory failure, acute kidney injury (AKI) has developed in a high percentage of cases. As AKI has been shown to be associated with worse prognosis, we believe that the impact of SARS-CoV-2 on the kidney should be investigated. This review sets out to describe the main renal aspects of SARS-CoV-2 infection and the role of the virus in the development and progression of kidney damage. In this article, attention is focused on the epidemiology, etiology and pathophysiological mechanisms of kidney damage, histopathology, clinical features in nephropathic patients (CKD, hemodialysis, peritoneal dialysis, AKI, transplantation) and prevention and containment strategies. Although there remains much more to be learned with regards to this disease, nonetheless it is our hope that this review will aid in the understanding and management of SARS-CoV-2 infection.

Host-Virus Arms Races Drive Elevated Adaptive Evolution in Viral Receptors

Viral receptors are the cell surface proteins that are hijacked by viruses to initialize their infections. Viral receptors are subject to two conflicting directional forces, namely, negative selection due to functional constraints and positive selection due to host-virus arms races. It remains largely obscure whether negative pleiotropy limits the rate of adaptation in viral receptors. Here, we perform evolutionary analyses of 96 viral receptor genes in primates and find that 41 out of 96 viral receptors experienced adaptive evolution. Many positively selected residues in viral receptors are located at the virus-receptor interfaces. Compared with control proteins, viral receptors exhibit significantly elevated rate of adaptation. Further analyses of genetic polymorphisms in human populations reveal signals of positive selection and balancing selection for 53 and 5 viral receptors, respectively. Moreover, we find that 49 viral receptors experienced different selection pressures in different human populations, indicating that viruses represent an important driver of local adaptation in humans. Our findings suggest that diverse viruses, many of which have not been known to infect nonhuman primates, have maintained antagonistic associations with primates for millions of years, and the host-virus conflicts drive accelerated adaptive evolution in viral receptors.IMPORTANCE Viruses hijack cellular proteins, termed viral receptors, to assist their entry into host cells. While viral receptors experience negative selection to maintain their normal functions, they also undergo positive selection due to an everlasting evolutionary arms race between viruses and hosts. A complete picture on how viral receptors evolve under two conflicting forces is still lacking. In this study, we systematically analyzed the evolution of 96 viral receptors in primates and human populations. We found around half of viral receptors underwent adaptive evolution and exhibit significantly elevated rates of adaptation compared to control genes in primates. We also found signals of past natural selection for 58 viral receptors in human populations. Interestingly, 49 viral receptors experienced different selection pressures in different human populations, indicating that viruses represent an important driver of local adaptation in humans. Our results suggest that host-virus arms races drive accelerated adaptive evolution in viral receptors.

Prediction of the Receptorome for the Human-Infecting Virome

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein–protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the enterovirus, norovirus and West Nile virus. Finally, the candidate alternative receptors of the SARS-CoV-2 were also predicted in this study. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses

The new coronavirus (SARS-CoV-2) outbreak from December 2019 in Wuhan, Hubei, China, has been declared a global public health emergency. Angiotensin I converting enzyme 2 (ACE2), is the host receptor by SARS-CoV-2 to infect human cells. Although ACE2 is reported to be expressed in lung, liver, stomach, ileum, kidney and colon, its expressing levels are rather low, especially in the lung. SARS-CoV-2 may use co-receptors/auxiliary proteins as ACE2 partner to facilitate the virus entry. To identify the potential candidates, we explored the single cell gene expression atlas including 119 cell types of 13 human tissues and analyzed the single cell co-expression spectrum of 51 reported RNA virus receptors and 400 other membrane proteins. Consistent with other recent reports, we confirmed that ACE2 was mainly expressed in lung AT2, liver cholangiocyte, colon colonocytes, esophagus keratinocytes, ileum ECs, rectum ECs, stomach epithelial cells, and kidney proximal tubules. Intriguingly, we found that the candidate co-receptors, manifesting the most similar expression patterns with ACE2 across 13 human tissues, are all peptidases, including ANPEP, DPP4 and ENPEP. Among them, ANPEP and DPP4 are the known receptors for human CoVs, suggesting ENPEP as another potential receptor for human CoVs. We also conducted "CellPhoneDB" analysis to understand the cell crosstalk between CoV-targets and their surrounding cells across different tissues. We found that macrophages frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, highlighting the importance of tissue macrophages in immune defense and immune pathogenesis.

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses

The new coronavirus (SARS-CoV-2) outbreak from December 2019 in Wuhan, Hubei, China, has been declared a global public health emergency. Angiotensin I converting enzyme 2 (ACE2), is the host receptor by SARS-CoV-2 to infect human cells. Although ACE2 is reported to be expressed in lung, liver, stomach, ileum, kidney and colon, its expressing levels are rather low, especially in the lung. SARS-CoV-2 may use co-receptors/auxiliary proteins as ACE2 partner to facilitate the virus entry. To identify the potential candidates, we explored the single cell gene expression atlas including 119 cell types of 13 human tissues and analyzed the single cell co-expression spectrum of 51 reported RNA virus receptors and 400 other membrane proteins. Consistent with other recent reports, we confirmed that ACE2 was mainly expressed in lung AT2, liver cholangiocyte, colon colonocytes, esophagus keratinocytes, ileum ECs, rectum ECs, stomach epithelial cells, and kidney proximal tubules. Intriguingly, we found that the candidate co-receptors, manifesting the most similar expression patterns with ACE2 across 13 human tissues, are all peptidases, including ANPEP, DPP4 and ENPEP. Among them, ANPEP and DPP4 are the known receptors for human CoVs, suggesting ENPEP as another potential receptor for human CoVs. We also conducted "CellPhoneDB" analysis to understand the cell crosstalk between CoV-targets and their surrounding cells across different tissues. We found that macrophages frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, highlighting the importance of tissue macrophages in immune defense and immune pathogenesis.

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses

The new coronavirus (SARS-CoV-2) outbreak from December 2019 in Wuhan, Hubei, China, has been declared a global public health emergency. Angiotensin I converting enzyme 2 (ACE2), is the host receptor by SARS-CoV-2 to infect human cells. Although ACE2 is reported to be expressed in lung, liver, stomach, ileum, kidney and colon, its expressing levels are rather low, especially in the lung. SARS-CoV-2 may use co-receptors/auxiliary proteins as ACE2 partner to facilitate the virus entry. To identify the potential candidates, we explored the single cell gene expression atlas including 119 cell types of 13 human tissues and analyzed the single cell co-expression spectrum of 51 reported RNA virus receptors and 400 other membrane proteins. Consistent with other recent reports, we confirmed that ACE2 was mainly expressed in lung AT2, liver cholangiocyte, colon colonocytes, esophagus keratinocytes, ileum ECs, rectum ECs, stomach epithelial cells, and kidney proximal tubules. Intriguingly, we found that the candidate co-receptors, manifesting the most similar expression patterns with ACE2 across 13 human tissues, are all peptidases, including ANPEP, DPP4 and ENPEP. Among them, ANPEP and DPP4 are the known receptors for human CoVs, suggesting ENPEP as another potential receptor for human CoVs. We also conducted "CellPhoneDB" analysis to understand the cell crosstalk between CoV-targets and their surrounding cells across different tissues. We found that macrophages frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, highlighting the importance of tissue macrophages in immune defense and immune pathogenesis.

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Background

Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited.

Result

In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF).

Conlusion

The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.