BRET-based biosensors for SARS-CoV-2 oligonucleotide detection.

The need for the early detection of emerging pathogenic viruses and their newer variants has driven the urgent demand for developing point-of-care diagnostic tools. Although nucleic acid-based methods such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and loop-mediated isothermal amplification (LAMP) have been developed, a more facile and robust platform is still required. To address this need, as a proof-of-principle study, we engineered a prototype-the versatile, sensitive, rapid, and cost-effective bioluminescence resonance energy transfer (BRET)-based biosensor for oligonucleotide detection (BioOD). Specifically, we designed BioODs against the SARS-CoV-2 parental (Wuhan strain) and B.1.617.2 Delta variant through the conjugation of specific, fluorescently modified molecular beacons (sensor module) through a complementary oligonucleotide handle DNA functionalized with the NanoLuc (NLuc) luciferase protein such that the dissolution of the molecular beacon loop upon the binding of the viral oligonucleotide will result in a decrease in BRET efficiency and, thus, a change in the bioluminescence spectra. Following the assembly of the BioODs, we determined their kinetics response, affinity for variant-specific oligonucleotides, and specificity, and found them to be rapid and highly specific. Furthermore, the decrease in BRET efficiency of the BioODs in the presence of viral oligonucleotides can be detected as a change in color in cell phone camera images. We envisage that the BioODs developed here will find application in detecting viral infections with variant specificity in a point-of-care-testing format, thus aiding in large-scale viral infection surveillance.

Distribution and Characteristics of Congenital Cardiac Surgery Centers within Bangladesh.

Congenital heart disease is a leading cause of non-communicable childhood death. This is especially true in nations with limited resources where shortages of skilled workforce, healthcare facilities, and essential equipment limit the ability to provide care. This retrospective study was designed to determine the volume and distribution of surgical care being provided to patients with congenital heart disease in Bangladesh, as well as to characterize the facilities providing such care. Pre-existing survey data of hospitals performing congenital heart surgery in the year 2022 in Bangladesh was obtained. Additional information was gathered on these facilities, including hospital location and type. The distribution of care by geographic location, type of facility, and volume of cases was reported. Overall, a total of 2333 surgeries were performed in 2022 at 28 facilities. The majority of hospitals were performing <50 cases per year, while a small number (5) provided greater than 50.0% of all surgeries. In addition, while the majority of hospitals were private in nature, the majority of surgeries occurred at not-for-profit hospitals. There was a large geographic skew of surgeries and hospitals being located within the city of Dhaka (79.0% of centers and 94.0% of surgeries). The data suggests that, though there has been great progress in increasing the number of surgeries performed in Bangladesh, the vast majority of patients still do not have access to care. In addition, nearly all care is being provided in Dhaka, which presents challenges for patients who come from across the nation seeking care. Finally, there is a great need for further research to fully understand the challenges faced and find workable solutions.

A slow but steady nanoLuc: R162A mutation results in a decreased, but stable, nanoLuc activity.

NanoLuc (NLuc) luciferase has found extensive application in designing a range of biological assays, including gene expression analysis, protein-protein interaction, and protein conformational changes due to its enhanced brightness and small size. However, questions related to its mechanism of interaction with the substrate, furimazine, as well as bioluminescence activity remain elusive. Here, we combined molecular dynamics (MD) simulation and mutational analysis to show that the R162A mutation results in a decreased but stable bioluminescence activity of NLuc in living cells and in vitro. Specifically, we performed multiple, all-atom, explicit solvent MD simulations of the apo and furimazine-docked (holo) NLuc structures revealing differential dynamics of the protein in the absence and presence of the ligand. Further, analysis of trajectories for hydrogen bonds (H-bonds) formed between NLuc and furimazine revealed substantial H-bond interaction between R162 and Q32 residues. Mutation of the two residues in NLuc revealed a decreased but stable activity of the R162A, but not Q32A, mutant NLuc in live cell and in vitro assays performed using lysates prepared from cells expressing the proteins and with the furimazine substrate. In addition to highlighting the role of the R162 residue in NLuc activity, we believe that the mutant NLuc will find wide application in designing in vitro assays requiring extended monitoring of NLuc bioluminescence activity. SIGNIFICANCE: Bioluminescence has been extensively utilized in developing a variety of biological and biomedical assays. In this regard, engineering of brighter bioluminescent proteins, i.e. luciferases, has played a significant role. This is acutely exemplified by the engineering of the NLuc luciferase, which is small in size and displays much enhanced bioluminescence and thermal stability compared to previously available luciferases. While enhanced bioluminescent activity is desirable in a multitude of biological and biomedical assays, it would also be useful to develop variants of the protein that display a prolonged bioluminescence activity. This is specifically relevant in designing assays that require bioluminescence for extended periods, such as in the case of biosensors designed for monitoring slow enzymatic or cellular signaling reactions, without necessitating multiple rounds of luciferase substrate addition or any specialized reagents that result in increased assay costs. In the current manuscript, we report a mutant NLuc that possesses a stable and prolonged bioluminescence activity, albeit lower than the wild-type NLuc, and envisage a wider application of the mutant NLuc in designing biosensors for monitoring slower biological and biomedical events.

Exosome nanovesicles as potential biomarkers and immune checkpoint signaling modulators in lung cancer microenvironment: recent advances and emerging concepts.

Lung cancer remains the leading cause of cancer-related deaths globally, and the survival rate remains low despite advances in diagnosis and treatment. The progression of lung cancer is a multifaceted and dynamic phenomenon that encompasses interplays among cancerous cells and their microenvironment, which incorporates immune cells. Exosomes, which are small membrane-bound vesicles, are released by numerous cell types in normal and stressful situations to allow communication between cells. Tumor-derived exosomes (TEXs) possess diverse neo-antigens and cargoes such as proteins, RNA, and DNA and have a unique molecular makeup reflecting tumor genetic complexity. TEXs contain both immunosuppressive and immunostimulatory factors and may play a role in immunomodulation by influencing innate and adaptive immune components. Moreover, they transmit signals that contribute to the progression of lung cancer by promoting metastasis, epithelial-mesenchymal transition (EMT), angiogenesis, and immunosuppression. This makes them a valuable resource for investigating the immune environment of tumors, which could pave the way for the development of non-invasive biomarkers that could aid in the prognosis, diagnosis, and immunotherapy of lung cancer. While immune checkpoint inhibitor (ICI) immunotherapy has shown promising results in treating initial-stage cancers, most patients eventually develop adaptive resistance over time. Emerging evidence demonstrates that TEXs could serve as a prognostic biomarker for immunotherapeutic response and have a significant impact on both systemic immune suppression and tumor advancement. Therefore, understanding TEXs and their role in lung cancer tumorigenesis and their response to immunotherapies is an exciting research area and needs further investigation. This review highlights the role of TEXs as key contributors to the advancement of lung cancer and their clinical significance in lung immune-oncology, including their possible use as biomarkers for monitoring disease progression and prognosis, as well as emerging shreds of evidence regarding the possibility of using exosomes as targets to improve lung cancer therapy.

Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro.

Background: SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro. Methods: We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer. Results: Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro. Conclusions: Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Identification of a potential DNA methyltransferase (DNMT) inhibitor.

DNA methyltransferases (DNMTs) play an important role in the epigenetic regulation of gene expression through the methylation of DNA. Since hypermethylation and consequent suppression of tumor suppressor genes are associated with cancer development and progression, DNA hypomethylating agents (HMAs) such as DNMT inhibitors have been proposed for cancer therapy. Two nucleoside analogues approved for the treatment of hematological cancers, decitabine and azacytidine, have poor pharmacokinetic properties, and hence there is a critical need for identifying novel HMAs. Virtual screening of a library of ∼40,000 compounds from the ZINC database, followed by molecular docking of 4,000 compounds having potential druggable properties with DNMT1, DNMT3A and DNMT3B were performed. One unique inhibitor (ZINC167686681) was identified that successfully passed through the Lipinski Rule of 5, geometry constraints as well as ADME/Tox filters and having strong binding energy for DNMTs. Further, molecular dynamics simulations of the docked complexes showed detailed structural features critical for its binding with the DNMTs and the stability of their interaction. Our study identified a compound with potential druggable properties and predicted to bind and inhibit DNMTs. Further investigations involving cellular and animal models of ZINC167686681 will help in potentially taking it into clinical trials for the treatment of cancers.Communicated by Ramaswamy H. Sarma.

Reversal of the unique Q493R mutation increases the affinity of Omicron S1-RBD for ACE2.

The SARS-CoV-2 Omicron variant containing 15 mutations, including the unique Q493R, in the spike protein receptor binding domain (S1-RBD) is highly infectious. While comparison with previously reported mutations provide some insights, the mechanism underlying the increased infections and the impact of the reversal of the unique Q493R mutation seen in BA.4, BA.5, BA.2.75, BQ.1 and XBB lineages is not yet completely understood. Here, using structural modelling and molecular dynamics (MD) simulations, we show that the Omicron mutations increases the affinity of S1-RBD for ACE2, and a reversal of the unique Q493R mutation further increases the ACE2-S1-RBD affinity. Specifically, we performed all atom, explicit solvent MD simulations using a modelled structure of the Omicron S1-RBD-ACE2 and compared the trajectories with the WT complex revealing a substantial reduction in the Cα-atom fluctuation in the Omicron S1-RBD and increased hydrogen bond and other interactions. Residue level analysis revealed an alteration in the interaction between several residues including a switch in the interaction of ACE2 D38 from S1-RBD Y449 in the WT complex to the mutated R residue (Q493R) in Omicron complex. Importantly, simulations with Revertant (Omicron without the Q493R mutation) complex revealed further enhancement of the interaction between S1-RBD and ACE2. Thus, results presented here not only provide insights into the increased infectious potential of the Omicron variant but also a mechanistic basis for the reversal of the Q493R mutation seen in some Omicron lineages and will aid in understanding the impact of mutations in SARS-CoV-2 evolution.

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Swarm Intelligence in Internet of Medical Things: A Review.

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

SARS-CoV-2 accessory proteins ORF7a and ORF3a use distinct mechanisms to down-regulate MHC-I surface expression.

Major histocompatibility complex class I (MHC-I) molecules, which are dimers of a glycosylated polymorphic transmembrane heavy chain and the small-protein ß2-microglobulin (ß2m), bind peptides in the endoplasmic reticulum that are generated by the cytosolic turnover of cellular proteins. In virus-infected cells, these peptides may include those derived from viral proteins. Peptide-MHC-I complexes then traffic through the secretory pathway and are displayed at the cell surface where those containing viral peptides can be detected by CD8+ T lymphocytes that kill infected cells. Many viruses enhance their in vivo survival by encoding genes that down-regulate MHC-I expression to avoid CD8+ T cell recognition. Here, we report that two accessory proteins encoded by SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, down-regulate MHC-I expression using distinct mechanisms. First, ORF3a, a viroporin, reduces the global trafficking of proteins, including MHC-I, through the secretory pathway. The second, ORF7a, interacts specifically with the MHC-I heavy chain, acting as a molecular mimic of ß2m to inhibit its association. This slows the exit of properly assembled MHC-I molecules from the endoplasmic reticulum. We demonstrate that ORF7a reduces antigen presentation by the human MHC-I allele HLA-A*02:01. Thus, both ORF3a and ORF7a act post-translationally in the secretory pathway to lower surface MHC-I expression, with ORF7a exhibiting a specific mechanism that allows immune evasion by SARS-CoV-2.

Structure-Based Virtual Screening and Functional Validation of Potential Hit Molecules Targeting the SARS-CoV-2 Main Protease.

The recent global health emergency caused by the coronavirus disease 2019 (COVID-19) pandemic has taken a heavy toll, both in terms of lives and economies. Vaccines against the disease have been developed, but the efficiency of vaccination campaigns worldwide has been variable due to challenges regarding production, logistics, distribution and vaccine hesitancy. Furthermore, vaccines are less effective against new variants of the SARS-CoV-2 virus and vaccination-induced immunity fades over time. These challenges and the vaccines' ineffectiveness for the infected population necessitate improved treatment options, including the inhibition of the SARS-CoV-2 main protease (Mpro). Drug repurposing to achieve inhibition could provide an immediate solution for disease management. Here, we used structure-based virtual screening (SBVS) to identify natural products (from NP-lib) and FDA-approved drugs (from e-Drug3D-lib and Drugs-lib) which bind to the Mpro active site with high-affinity and therefore could be designated as potential inhibitors. We prioritized nine candidate inhibitors (e-Drug3D-lib: Ciclesonide, Losartan and Telmisartan; Drugs-lib: Flezelastine, Hesperidin and Niceverine; NP-lib: three natural products) and predicted their half maximum inhibitory concentration using DeepPurpose, a deep learning tool for drug-target interactions. Finally, we experimentally validated Losartan and two of the natural products as in vitro Mpro inhibitors, using a bioluminescence resonance energy transfer (BRET)-based Mpro sensor. Our study suggests that existing drugs and natural products could be explored for the treatment of COVID-19.

Corrigendum: Decreased interfacial dynamics caused by the N501Y mutation in the SARS-CoV-2 S1 spike:ACE2 complex.

[This corrects the article DOI: 10.3389/fmolb.2022.846996.].

The evolving role of extracellular vesicles (exosomes) as biomarkers in traumatic brain injury: Clinical perspectives and therapeutic implications.

Developing effective disease-modifying therapies for neurodegenerative diseases (NDs) requires reliable diagnostic, disease activity, and progression indicators. While desirable, identifying biomarkers for NDs can be difficult because of the complex cytoarchitecture of the brain and the distinct cell subsets seen in different parts of the central nervous system (CNS). Extracellular vesicles (EVs) are heterogeneous, cell-derived, membrane-bound vesicles involved in the intercellular communication and transport of cell-specific cargos, such as proteins, Ribonucleic acid (RNA), and lipids. The types of EVs include exosomes, microvesicles, and apoptotic bodies based on their size and origin of biogenesis. A growing body of evidence suggests that intercellular communication mediated through EVs is responsible for disseminating important proteins implicated in the progression of traumatic brain injury (TBI) and other NDs. Some studies showed that TBI is a risk factor for different NDs. In terms of therapeutic potential, EVs outperform the alternative synthetic drug delivery methods because they can transverse the blood-brain barrier (BBB) without inducing immunogenicity, impacting neuroinflammation, immunological responses, and prolonged bio-distribution. Furthermore, EV production varies across different cell types and represents intracellular processes. Moreover, proteomic markers, which can represent a variety of pathological processes, such as cellular damage or neuroinflammation, have been frequently studied in neurotrauma research. However, proteomic blood-based biomarkers have short half-lives as they are easily susceptible to degradation. EV-based biomarkers for TBI may represent the complex genetic and neurometabolic abnormalities that occur post-TBI. These biomarkers are not caught by proteomics, less susceptible to degradation and hence more reflective of these modifications (cellular damage and neuroinflammation). In the current narrative and comprehensive review, we sought to discuss the contemporary knowledge and better understanding the EV-based research in TBI, and thus its applications in modern medicine. These applications include the utilization of circulating EVs as biomarkers for diagnosis, developments of EV-based therapies, and managing their associated challenges and opportunities.

A genetically encoded BRET-based SARS-CoV-2 Mpro protease activity sensor.

The main protease, Mpro, is critical for SARS-CoV-2 replication and an appealing target for designing anti-SARS-CoV-2 agents. Therefore, there is a demand for the development of improved sensors to monitor its activity. Here, we report a pair of genetically encoded, bioluminescence resonance energy transfer (BRET)-based sensors for detecting Mpro proteolytic activity in live cells as well as in vitro. The sensors were generated by sandwiching peptides containing the Mpro N-terminal autocleavage sites, either AVLQSGFR (short) or KTSAVLQSGFRKME (long), in between the mNeonGreen and NanoLuc proteins. Co-expression of the sensors with Mpro in live cells resulted in their cleavage while mutation of the critical C145 residue (C145A) in Mpro completely abrogated their cleavage. Additionally, the sensors recapitulated the inhibition of Mpro by the well-characterized pharmacological agent GC376. Further, in vitro assays with the BRET-based Mpro sensors revealed a molecular crowding-mediated increase in the rate of Mpro activity and a decrease in the inhibitory potential of GC376. The sensors developed here will find direct utility in studies related to drug discovery targeting the SARS-CoV-2 Mpro and functional genomics application to determine the effect of sequence variation in Mpro.

Decreased Interfacial Dynamics Caused by the N501Y Mutation in the SARS-CoV-2 S1 Spike:ACE2 Complex.

Coronavirus Disease of 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a massive health crisis across the globe, with some genetic variants gaining enhanced infectivity and competitive fitness, and thus significantly aggravating the global health concern. In this regard, the recent SARS-CoV-2 alpha, beta, and gamma variants (B.1.1.7, B.1.351, and P.1 lineages, respectively) are of great significance in that they contain several mutations that increase their transmission rates as evident from clinical reports. By the end of March 2021, these variants were accounting for about two-thirds of SARS-CoV-2 variants circulating worldwide. Specifically, the N501Y mutation in the S1 spike receptor binding domain (S1-RBD) of these variants have been reported to increase its affinity for ACE2, although the basis for this is not entirely clear yet. Here, we dissect the mechanism underlying the increased binding affinity of the N501Y mutant for ACE2 using molecular dynamics (MD) simulations of the available ACE2-S1-RBD complex structure (6M0J) and show a prolonged and stable interfacial interaction of the N501Y mutant S1-RBD with ACE2 compared to the wild type S1-RBD. Additionally, we find that the N501Y mutant S1-RBD displays altered dynamics that likely aids in its enhanced interaction with ACE2. By elucidating a mechanistic basis for the increased affinity of the N501Y mutant S1-RBD for ACE2, we believe that the results presented here will aid in developing therapeutic strategies against SARS-CoV-2 including designing of therapeutic agents targeting the ACE2-S1-RBD interaction.

Patterned Substrate of Mobile and Immobile Ligands to Probe EphA2 Receptor Clustering.

A multitude of membrane-localized receptors are utilized by cells to integrate both biochemical and physical signals from their microenvironment. The clustering of membrane receptors is widely presumed to have functional consequences for subsequent signal transduction. However, it is experimentally challenging to selectively manipulate receptor clustering without altering other biochemical aspects of the cellular system. Here, we describe a method to fabricate multicomponent, ligand-functionalized microarrays, for spatially segregated and simultaneous monitoring of receptor activation and signaling in individual living cells. While existing micropatterning techniques allow for the display of fixed ligands, this protocol uniquely allows for functionalization of both mobile membrane corrals and immobile polymers with selective ligands, as well as microscopic monitoring of cognate receptor activation at the cell membrane interface. This protocol has been developed to study the effects of clustering on EphA2 signaling transduction. It is potentially applicable to multiple cell signaling systems, or microbe/host interactions. Graphical abstract: A side-by-side comparison of clustered or non-clustered EphA2 receptor signaling in a single cell.

Understanding the Mechanism of Dysglycemia in a Fanconi-Bickel Syndrome Patient.

Fanconi-Bickel Syndrome (FBS) is a rare disorder of carbohydrate metabolism that is characterized mainly by the accumulation of glycogen in the liver and kidney. It is inherited as an autosomal recessive disorder caused by mutations in the SLC2A2 gene, which encodes for GLUT2. Patients with FBS have dysglycemia but the molecular mechanisms of dysglycemia are still not clearly understood. Therefore, we aimed to understand the underlying molecular mechanisms of dysglycemia in a patient with FBS. Genomic DNA was isolated from a peripheral blood sample and analyzed by whole genome and Sanger sequencing. CRISPR-Cas9 was used to introduce a mutation that mimics the patient's mutation in a human kidney cell line expressing GLUT2 (HEK293T). Mutant cells were used for molecular analysis to investigate the effects of the mutation on the expression and function of GLUT2, as well as the expression of other genes implicated in dysglycemia. The patient was found to have a homozygous nonsense mutation (c.901C>T, R301X) in the SLC2A2 gene. CRISPR-Cas9 successfully mimicked the patient's mutation in HEK293T cells. The mutant cells showed overexpression of a dysfunctional GLUT2 protein, resulting in reduced glucose release activity and enhanced intracellular glucose accumulation. In addition, other glucose transporters (SGLT1 and SGLT2 in the kidney) were found to be induced in the mutant cells. These findings suggest the last loops (loops 9-12) of GLUT2 are essential for glucose transport activity and indicate that GLUT2 dysfunction is associated with dysglycemia in FBS.

SARS-CoV-2 accessory proteins ORF7a and ORF3a use distinct mechanisms to downregulate MHC-I surface expression.

Major histocompatibility complex class I (MHC-I) molecules, which are dimers of a glycosylated polymorphic transmembrane heavy chain and the small protein ß 2 -microglobulin (ß 2 m), bind peptides in the endoplasmic reticulum that are generated by the cytosolic turnover of cellular proteins. In virus-infected cells these peptides may include those derived from viral proteins. Peptide-MHC-I complexes then traffic through the secretory pathway and are displayed at the cell surface where those containing viral peptides can be detected by CD8 + T lymphocytes that kill infected cells. Many viruses enhance their in vivo survival by encoding genes that downregulate MHC-I expression to avoid CD8 + T cell recognition. Here we report that two accessory proteins encoded by SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, downregulate MHC-I expression using distinct mechanisms. One, ORF3a, a viroporin, reduces global trafficking of proteins, including MHC-I, through the secretory pathway. The second, ORF7a, interacts specifically with the MHC-I heavy chain, acting as a molecular mimic of ß 2 m to inhibit its association. This slows the exit of properly assembled MHC-I molecules from the endoplasmic reticulum. We demonstrate that ORF7a reduces antigen presentation by the human MHC-I allele HLA-A*02:01. Thus, both ORF3a and ORF7a act post-translationally in the secretory pathway to lower surface MHC-I expression, with ORF7a exhibiting a novel and specific mechanism that allows immune evasion by SARS-CoV-2. Significance Statement: Viruses may down-regulate MHC class I expression on infected cells to avoid elimination by cytotoxic T cells. We report that the accessory proteins ORF7a and ORF3a of SARS-CoV-2 mediate this function and delineate the two distinct mechanisms involved. While ORF3a inhibits global protein trafficking to the cell surface, ORF7a acts specifically on MHC-I by competing with ß 2 m for binding to the MHC-I heavy chain. This is the first account of molecular mimicry of ß 2 m as a viral mechanism of MHC-I down-regulation to facilitate immune evasion.

Competition for shared downstream signaling molecules establishes indirect negative feedback between EGFR and EphA2.

Cells sense a variety of extracellular growth factors and signaling molecules through numerous distinct receptor tyrosine kinases (RTKs) on the cell surface. In many cases, the same intracellular signaling molecules interact with more than one type of RTK. How signals from different RTKs retain the identity of the triggering receptor and how (or if) different receptors may synergize or compete remain largely unknown. Here we utilize an experimental strategy, combining microscale patterning and single-molecule imaging, to measure the competition between ephrin-A1:EphA2 and epidermal growth factor (EGF):EGF receptor (EGFR) ligand-receptor complexes for the shared downstream signaling molecules, Grb2 and SOS. The results reveal a distinct hierarchy, in which newly formed EGF:EGFR complexes outcompete ephrin-A1:EphA2 for Grb2 and SOS, revealing a type of negative crosstalk interaction fundamentally controlled by chemical mass action and protein copy number limitations.

Aleatory-aware deep uncertainty quantification for transfer learning.

The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link: https://github.com/dipuk0506/Aleatory-aware-UQ.