Role of TAM Receptors in Antimalarial Humoral Immune Response.

Immune response against malaria and the clearance of Plasmodium parasite relies on germinal-center-derived B cell responses that are temporally and histologically layered. Despite a well-orchestrated germinal center response, anti-Plasmodium immune response seldom offers sterilizing immunity. Recent studies report that certain pathophysiological features of malaria such as extensive hemolysis, hypoxia as well as the extrafollicular accumulation of short-lived plasmablasts may contribute to this suboptimal immune response. In this review, we summarize some of those studies and attempt to connect certain host intrinsic features in response to the malarial disease and the resultant gaps in the immune response.

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal.

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation: The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

Analyzing the cation-aromatic interactions in proteins: Cation-aromatic database V2.0.

The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation-π or cation-σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation-aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS-HIS, PHE-ARG, and TRP-ARG pairs exhibited a preferential geometry. The motif pair HIS-HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR-LYS at 10.17%. Conversely, the motif pair TRP-HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation-aromatic interactions in biological molecules. The updated version of CAD (Cation-Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Identification of CCZ1 as an essential lysosomal trafficking regulator in Marburg and Ebola virus infections.

Marburg and Ebola filoviruses are two of the deadliest infectious agents and several outbreaks have occurred in the last decades. Although several receptors and co-receptors have been reported for Ebola virus, key host factors remain to be elucidated. In this study, using a haploid cell screening platform, we identify the guanine nucleotide exchange factor CCZ1 as a key host factor in the early stage of filovirus replication. The critical role of CCZ1 for filovirus infections is validated in 3D primary human hepatocyte cultures and human blood-vessel organoids, both critical target sites for Ebola and Marburg virus tropism. Mechanistically, CCZ1 controls early to late endosomal trafficking of these viruses. In addition, we report that CCZ1 has a role in the endosomal trafficking of endocytosis-dependent SARS-CoV-2 infections, but not in infections by Lassa virus, which enters endo-lysosomal trafficking at the late endosome stage. Thus, we have identified an essential host pathway for filovirus infections in cell lines and engineered human target tissues. Inhibition of CCZ1 nearly completely abolishes Marburg and Ebola infections. Thus, targeting CCZ1 could potentially serve as a promising drug target for controlling infections caused by various viruses, such as SARS-CoV-2, Marburg, and Ebola.

Detection of lytic phage infecting flavour-producing strain of Lacticaseibacillus paracasei in the dairy effluents of Kerala.

The performance of the starter culture is a critical factor that decides the quality of fermented milk. Dahi is a fermented milk product popular in India made using a mixed starter culture of lactic acid bacteria comprising acid and flavour producers. The prevalence of bacteriophages in the dairy environment can critically affect the activity of these starter cultures resulting in starter failure. As there is little information available on the occurrence of bacteriophages in the dairy environment of Kerala, this research communication examines the presence of lytic bacteriophages acting against three potential flavour-producing strains of Lacticaseibacillus paracasei (Lc. paracasei). Dairy effluent samples were screened for the presence of phages against the strains of Lc. paracasei by the multiple host enrichment method. Plates showing clearance zone in spot assay were confirmed for the presence of phages by double-layer agar assay. The plaques obtained in the double-layer agar assay were purified for further identification by next-generation sequencing. A bacteriophage infecting one of the three strains of Lc. paracasei was detected by the plaque assay and the blast annotation of the bacteriophage sequence found 86.05% similarity of the phage to Siphoviridae family. The study endorses the need for monitoring phages in the dairy environment to control phage-related starter failure in the state of Kerala.

Phylogenetic analysis of Babesia gibsoni isolates of south India using apical membrane antigen, 50 kDa surface antigen, and 70 kDa heat shock protein genes.

The prevalence of canine babesiosis due to Babesia gibsoni has increased throughout the world including in southern India. The polymerase chain reaction (PCR) based molecular characterization of B. gibsoni in dogs of Kerala, south India, targeting three specific genes viz., apical membrane antigen (AMA1), 50 kDa surface antigen (P50), and heat shock protein (HSP70) was undertaken in this study. Out of 297 blood samples collected from clinically suspected animals, microscopy detected piroplasms of B. gibsoni in 60 (20.20 per cent), while the PCR targeting the BgP50 gene detected 85 (28.61 per cent). Polymerase chain reaction targeting the BgAMA1 and BgHSP70 detected a lesser number of samples (60 and 65 respectively) as positive. The phylogenetic analysis of BgHSP70 gene sequences did not reveal genetic heterogeneity among the B. gibsoni isolates of South India and from other countries, while the BgP50 gene differentiated the Indian isolates from Japanese isolates. When BgAMA1 was used for phylogenetic analysis, genetic variation was not observed among Indian and Taiwanese isolates, however, differentiated them from the Japanese isolates.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085 .

Green Synthesized Silver Nanoparticles Using Lactobacillus Acidophilus as an Antioxidant, Antimicrobial, and Antibiofilm Agent Against Multi-drug Resistant Enteroaggregative Escherichia Coli.

The present study was envisaged to employ the green synthesis and characterization of silver nanoparticles (AgNPs) using the potential probiotic strain Lactobacillus acidophilus, to assess its antibacterial as well as antibiofilm activity against multi-drug-resistant enteroaggregative Escherichia coli (MDR-EAEC) strains and to investigate their antioxidant activity. In this study, AgNPs were successfully synthesized through an eco-friendly protocol, which was then confirmed by its X-ray diffraction (XRD) pattern. A weight loss of 15% up to 182 °C with a narrow exothermic peak between 170 °C and 205 °C was observed in thermogravimetric analysis-differential thermal analysis (TGA-DTA), while aggregated nanoclusters were observed in scanning electron microscopy (SEM). Moreover, the transmission electron microscopy (TEM) imaging of AgNPs revealed a spherical morphology and crystalline nature with an optimum size ranging from 10 to 20 nm. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values of green synthesized AgNPs against the MDR-EAEC strains were found to be 7.80 mg/L and 15.60 mg/L, respectively. In vitro time-kill kinetic assay revealed a complete elimination of the MDR-EAEC strains after 180 min on co-incubation with the AgNPs. Moreover, the green synthesized AgNPs were found safe by in vitro haemolytic assay. Besides, the green synthesized AgNPs exhibited significant biofilm inhibition (P < 0.001) formed by MDR-EAEC strains. Additionally, a concentration-dependent antioxidant activity was observed in 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. Hence, this study demonstrated potential antibacterial as well as antibiofilm activity of green synthesized AgNPs against MDR-EAEC strains with antioxidant properties and warrants further in-depth studies to explore it as an effective antimicrobial agent against MDR infections.

Redirecting Imipramine against Bluetongue Virus Infection: Insights from a Genome-wide Haploid Screening Study.

Bluetongue virus (BTV), an arbovirus of ruminants, is a causative agent of numerous epidemics around the world. Due to the emergence of novel reassortant BTV strains and new outbreaks, there is an unmet need for efficacious antivirals. In this study, we used an improved haploid screening platform to identify the relevant host factors for BTV infection. Our screening tool identified and validated the host factor Niemann-Pick C1 (NPC1), a lysosomal membrane protein that is involved in lysosomal cholesterol transport, as a critical factor in BTV infection. This finding prompted us to investigate the possibility of testing imipramine, an antidepressant drug known to inhibit NPC1 function by interfering with intracellular cholesterol trafficking. In this study, we evaluated the sensitivity of BTV to imipramine using in vitro assays. Our results demonstrate that imipramine pretreatment inhibited in vitro replication and progeny release of BTV-4, BTV-8, and BTV-16. Collectively, our findings highlight the importance of NPC1 for BTV infection and recommend the reprofiling of imipramine as a potential antiviral drug against BTV.

Sequence and phylogenetic analysis of the thrombospondin-related adhesive protein gene of Babesia gibsoni isolates in dogs in South India.

Babesia gibsoni, the causative agent of canine piroplasmosis, is a tick-borne intraerythrocytic protozoan parasite predominantly reported in Asian countries. The present study aimed at genotypic characterization of B. gibsoni isolates prevalent in dogs in Kerala, a southern state of India. Blood samples were collected from 272 dogs in Kerala and B. gibsoni infection was detected by microscopy and polymerase chain reaction (PCR). Molecular confirmation of B. gibsoni parasites was carried out by 18S rRNA nested-PCR, followed by sequencing. Nested-PCR detected a higher percentage of dogs (40.44%) positive for B. gibsoni infection than microscopy where 15.81% dogs were detected positive for infection. Genetic characterization of B. gibsoni isolates (n = 11) prevalent in dogs in the state of Kerala was carried out by PCR amplification and sequencing of the 855 bp thrombospondin-related adhesive protein (TRAP) gene fragment. Phylogenetic analysis of the B. gibsoni TRAP (BgTRAP) gene revealed that B. gibsoni isolates from Kerala formed a distinct cluster with the isolates from north India and Bangladesh, away from other East Asian isolates. Nucleotide analysis of the tandem repeats of BgTRAP gene showed considerable genetic variation among Indian isolates that was shared by B. gibsoni isolates of Bangladesh but not by the isolates of East Asian countries. The results of the present study further confirmed that B. gibsoni parasites in a distinct genetic clade are endemic in dogs in India and Bangladesh. However, elaborate studies are required for better understanding of the genetic diversity of B. gibsoni.

Prevalence of Anti-Toxocara canis Antibodies in Dogs Detected with Recombinant Cathepsin L-1 and TES-26 Antigens in Three States of India.

PURPOSE: Toxocara canis is a common intestinal nematode parasite of dogs with recognized zoonotic potential in tropical countries. The purpose of this study was to determine the seroprevalence of anti-T. canis antibodies in two target dog populations: household and community-owned, distributed over three distinct geographical regions of India. METHODS: Two recombinant proteins of T. canis, cathepsin L-1 (CL-1) and Toxocara excretory-secretory-26 (TES-26), expressed in Escherichia coli, were used for studying the prevalence of anti-T. canis antibodies in dog populations in three distinct geographical regions of the country using an IgG-enzyme-linked immunosorbent assay. A total of 615 sera, 507 from household and 108 from community owned dogs were screened for IgG antibodies. RESULTS: ELISA with recombinant (r) CL-1 showed 37.7% and 53.7% seroreactivity in household and community owned dogs, respectively. However, the rTES-26 antigen showed higher seroreactivity of 39.6% and 87.9% in the corresponding groups of household and community owned dogs, respectively. Chi-squared analysis of the data indicated that there was not any association in the prevalence of anti-T. canis antibodies between the samples analyzed from the three regions and the two cohorts of dog groups. However, the seroprevalence was higher in community owned dogs compared to household owned dogs. CONCLUSION: The results of the serological evaluation suggest that both the groups of dogs show high seroreactivity rates and are likely to harbor T. canis infections of tissue dwelling dormant larvae.

Nucleoside-Modified mRNA Vaccines Protect IFNAR-/- Mice against Crimean-Congo Hemorrhagic Fever Virus Infection.

Crimean-Congo hemorrhagic fever (CCHF), caused by Crimean-Congo hemorrhagic fever virus (CCHFV), is on the World Health Organizations' list of prioritized diseases and pathogens. With global distribution, high fatality rate, and no approved vaccine or effective treatment, CCHF constitutes a threat against global health. In the current study, we demonstrate that vaccination with nucleoside-modified mRNA-lipid nanoparticles (mRNA-LNP), encoding for the CCHFV nucleoprotein (N) or glycoproteins (GcGn) protect IFNAR-/- mice against lethal CCHFV infection. In addition, we found that both mRNA-LNP induced strong humoral and cellular immune responses in IFNAR-/- and immunocompetent mice and that neutralizing antibodies are not necessary for protection. When evaluating immune responses induced by immunization including CCHFV Gc and Gn antigens, we found the Gc protein to be more immunogenic compared with the Gn protein. Hepatic injury is prevalent in CCHF and contributes to the severity and mortality of the disease in humans. Thus, to understand the immune response in the liver after infection and the potential effect of the vaccine, we performed a proteomic analysis on liver samples from vaccinated and control mice after CCHFV infection. Similar to observations in humans, vaccination affected the metabolic pathways. In conclusion, this study shows that a CCHFV mRNA-LNP vaccine, based on viral nucleo- or glycoproteins, mediate protection against CCHFV induced disease. Consequently, genetic immunization is an attractive approach to prevent disease caused by CCHFV and we believe we have necessary evidence to bring this vaccine platform to the next step in the development of a vaccine against CCHFV infection. IMPORTANCE Crimean-Congo hemorrhagic fever virus (CCHFV) is a zoonotic pathogen causing Crimean-Congo hemorrhagic fever (CCHF), a severe fever disease. CCHFV has a wide distribution and is endemic in several areas around the world. Cases of CCHF are also being reported in new areas, indicating an expansion of the disease, which is of high concern. Dispersion of the disease, high fatality rate, and no approved vaccine makes CCHF a threat to global health. The development of a vaccine is thus of great importance. Here we show 100% protection against lethal CCHFV infection in mice immunized with mRNA-LNP encoding for different CCHFV proteins. The vaccination showed both robust humoral and cellular immunity. mRNA-LNP vaccines combine the ability to induce an effective immune response, the safety of a transient carrier, and the flexibility of genetic vaccines. This and our results from the current study support the development of a mRNA-LNP based vaccine against CCHFV.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing.

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

Human AdV-20-42-42, a Promising Novel Adenoviral Vector for Gene Therapy and Vaccine Product Development.

Preexisting immune responses toward adenoviral vectors limit the use of a vector based on particular serotypes and its clinical applicability for gene therapy and/or vaccination. Therefore, there is a significant interest in vectorizing novel adenoviral types that have low seroprevalence in the human population. Here, we describe the discovery and vectorization of a chimeric human adenovirus, which we call HAdV-20-42-42. Full-genome sequencing revealed that this virus is closely related to human serotype 42, except for the penton base, which is derived from serotype 20. The HAdV-20-42-42 vector could be propagated stably to high titers on existing E1-complementing packaging cell lines. Receptor-binding studies revealed that the vector utilized both CAR and CD46 as receptors for cell entry. Furthermore, the HAdV-20-42-42 vector was potent in transducing human and murine cardiovascular cells and tissues, irrespective of the presence of blood coagulation factor X. In vivo characterizations demonstrate that when delivered intravenously (i.v.) in mice, HAdV-20-42-42 mainly targeted the lungs, liver, and spleen and triggered robust inflammatory immune responses. Finally, we demonstrate that potent T-cell responses against vector-delivered antigens could be induced upon intramuscular vaccination in mice. In summary, from the data obtained we conclude that HAdV-20-42-42 provides a valuable addition to the portfolio of adenoviral vectors available to develop efficacious products in the fields of gene therapy and vaccination. IMPORTANCE Adenoviral vectors are under investigation for a broad range of therapeutic indications in diverse fields, such as oncology and gene therapy, as well as for vaccination both for human and veterinary use. A wealth of data shows that preexisting immune responses may limit the use of a vector. Particularly in the current climate of global pandemic, there is a need to expand the toolbox with novel adenoviral vectors for vaccine development. Our data demonstrate that we have successfully vectorized a novel adenovirus type candidate with low seroprevalence. The cell transduction data and antigen-specific immune responses induced in vivo demonstrate that this vector is highly promising for the development of gene therapy and vaccine products.

A super-potent tetramerized ACE2 protein displays enhanced neutralization of SARS-CoV-2 virus infection.

Approaches are needed for therapy of the severe acute respiratory syndrome from SARS-CoV-2 coronavirus (COVID-19). Interfering with the interaction of viral antigens with the angiotensin converting enzyme 2 (ACE-2) receptor is a promising strategy by blocking the infection of the coronaviruses into human cells. We have implemented a novel protein engineering technology to produce a super-potent tetravalent form of ACE2, coupled to the human immunoglobulin γ1 Fc region, using a self-assembling, tetramerization domain from p53 protein. This high molecular weight Quad protein (ACE2-Fc-TD) retains binding to the SARS-CoV-2 receptor binding spike protein and can form a complex with the spike protein plus anti-viral antibodies. The ACE2-Fc-TD acts as a powerful decoy protein that out-performs soluble monomeric and dimeric ACE2 proteins and blocks both SARS-CoV-2 pseudovirus and SARS-CoV-2 virus infection with greatly enhanced efficacy. The ACE2 tetrameric protein complex promise to be important for development as decoy therapeutic proteins against COVID-19. In contrast to monoclonal antibodies, ACE2 decoy is unlikely to be affected by mutations in SARS-CoV-2 that are beginning to appear in variant forms. In addition, ACE2 multimeric proteins will be available as therapeutic proteins should new coronaviruses appear in the future because these are likely to interact with ACE2 receptor.

Human species D adenovirus hexon capsid protein mediates cell entry through a direct interaction with CD46.

Human adenovirus species D (HAdV-D) types are currently being explored as vaccine vectors for coronavirus disease 2019 (COVID-19) and other severe infectious diseases. The efficacy of such vector-based vaccines depends on functional interactions with receptors on host cells. Adenoviruses of different species are assumed to enter host cells mainly by interactions between the knob domain of the protruding fiber capsid protein and cellular receptors. Using a cell-based receptor-screening assay, we identified CD46 as a receptor for HAdV-D56. The function of CD46 was validated in infection experiments using cells lacking and overexpressing CD46, and by competition infection experiments using soluble CD46. Remarkably, unlike HAdV-B types that engage CD46 through interactions with the knob domain of the fiber protein, HAdV-D types infect host cells through a direct interaction between CD46 and the hexon protein. Soluble hexon proteins (but not fiber knob) inhibited HAdV-D56 infection, and surface plasmon analyses demonstrated that CD46 binds to HAdV-D hexon (but not fiber knob) proteins. Cryoelectron microscopy analysis of the HAdV-D56 virion-CD46 complex confirmed the interaction and showed that CD46 binds to the central cavity of hexon trimers. Finally, soluble CD46 inhibited infection by 16 out of 17 investigated HAdV-D types, suggesting that CD46 is an important receptor for a large group of adenoviruses. In conclusion, this study identifies a noncanonical entry mechanism used by human adenoviruses, which adds to the knowledge of adenovirus biology and can also be useful for development of adenovirus-based vaccine vectors.

Molecular Characterization of Hemoparasites and Hemoplasmas Infecting Domestic Cats of Southern India.

In the present study, 111 blood samples were collected from apparently healthy cats belonging to four districts of Kerala, southern India, and they were investigated for the presence of hemoparasites and hemoplasmas by light microscopic examination and polymerase chain reaction (PCR). The microscopic examination of the Giemsa-stained blood smears did not reveal any parasites/organisms. However, PCR followed by nucleotide sequencing could detect 10 (9.01%) out of 111 samples infected with Hepatozoon felis, 3 (2.70%) with Cytauxzoon spp., and 10 (9.01%) with Mycoplasma spp. None of the samples revealed amplicons specific for the Babesia spp. and Trypanosoma evansi. The phylogenetic analysis of 18S ribosomal RNA (rRNA) gene sequences of H. felis revealed the existence of two different populations of H. felis circulating in the blood of infected cats. The phylogenetic tree was constructed based on 18S rRNA gene sequences of Cytauxzoon spp. and revealed that these isolates formed a distinct clade and do not cluster with any of the isolates from other countries. Among the 10 samples positive for Mycoplasma spp. infections, 7 were detected positive for Candidatus Mycoplasma haemominutum, two for Mycoplasma haemofelis, and one for Candidatus Mycoplasma turicensis. Phylogenetic analysis of 16S rRNA gene sequences of Mycoplasma spp. showed no distinct geographical grouping of the sequences. The sequences of M. haemofelis, Candidatus M. haemominutum, and Candidatus M. turicensis identified in the study clustered along with their respective isolates from around the world. To the best of our knowledge, this study forms the first report of molecular detection of Cytauxzoon spp. and Candidatus M. turicensis in cats from India.