Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19.

SARS-CoV-2, the causative agent of COVID-19, remains among the main causes of global mortality. Although antigen/antibody-based immunoassays and neutralizing antibodies targeting SARS-CoV-2 have been successfully developed over the past 2 years, they are often inefficient and unreliable for emerging SARS-CoV-2 variants. Novel approaches against SARS-CoV-2 and its variants are therefore urgently needed. Aptamers have been developed for the detection and inhibition of several different viruses such as HIV, influenza viruses, Middle East respiratory syndrome coronavirus (MERS-CoV), and SARS-CoV. Aptamers targeting SARS-CoV-2 represent a promising tool in the fight against COVID-19, which is of paramount importance for the current and any future pandemics. This review presents recent advances and future trends in the development of aptamer-based approaches for SARS-CoV-2 diagnosis and treatment.

Fluorescence detection of the human angiotensinogen protein by the G-quadruplex aptamer.

Noncanonical G-quadruplex nucleic acid structures can be used as probes in biosensors for the detection of metal ions, proteins and nucleic acids. Angiotensinogen (AGT) is a glycosylated globulin found in serum, which can regulate blood pressure and body fluid homeostasis. AGT is an important part of the renin-angiotensin system (RAS) and can be potentially used as a biomarker of diseases with RAS alterations. G-quadruplex based biosensors can detect targets with high accuracy and speed and low cost. Employing the magnetic bead enrichment method we constructed a reliable and efficient fluorescent biosensor platform for G-quadruplex based detection of the human AGT protein. The primary antibody in our biosensor is recognized by fluorescently labeled secondary antibodies, which leads to the detection of AGT captured by the G-quadruplex aptamer coupled magnetic beads. This G-quadruplex based fluorescent biosensor designed with a detection limit of 5 × 10-5 mg mL-1 was used for the successful detection of AGT at the cellular level. Our G-quadruplex based fluorescent biosensor will contribute to the more reliable and efficient detection of AGT.

Correction of out-of-focus microscopic images by deep learning.

Motivation: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. Results: To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets. Availability and Implementation: Code and dataset are publicly available at: https://github.com/jiangdat/COMI.

Deep learning for microscopic examination of protozoan parasites.

The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2.

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Multiplex Biosensing for Simultaneous Detection of Mutations in SARS-CoV-2.

COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has become the world's largest public health emergency of the past few decades. Thousands of mutations were identified in the SARS-CoV-2 genome. Some mutants are more infectious and may replace the original strains. Recently, B.1.1.7(Alpha), B1.351(Beta), and B.1.617.2(Delta) strains, which appear to have increased transmissibility, were detected. These strains accounting for the high proportion of newly diagnosed cases spread rapidly over the world. Particularly, the Delta variant has been reported to account for a vast majority of the infections in several countries over the last few weeks. The application of biosensors in the detection of SARS-CoV-2 is important for the control of the COVID-19 pandemic. Due to high demand for SARS-CoV-2 genotyping, it is urgent to develop reliable and efficient systems based on integrated multiple biosensor technology for rapid detection of multiple SARS-CoV-2 mutations simultaneously. This is important not only for the detection and analysis of the current but also for future mutations. Novel biosensors combined with other technologies can be used for the reliable and effective detection of SARS-CoV-2 mutants.

KPC-Mediated Resistance to Ceftazidime-Avibactam and Collateral Effects in Klebsiella pneumoniae.

Carbapenem-resistant Enterobacterales, such as Klebsiella pneumoniae carbapenemase (KPC)-producing K. pneumoniae, represent a major threat to public health due to their rapid spread. Novel drug combinations such as ceftazidime-avibactam (CZA), combining a broad-spectrum cephalosporin along with a broad-spectrum ß-lactamase inhibitor, have recently been introduced and have been shown to exhibit excellent activity toward multidrug-resistant KPC-producing Enterobacterales strains. However, CZA-resistant K. pneumoniae isolates are now being increasingly reported, mostly corresponding to producers of KPC variants. In this study, we evaluated in vitro the nature of the mutations in the KPC-2 and KPC-3 ß-lactamase sequences (the most frequent KPC-type enzymes) that lead to CZA resistance and the subsequent effects of these mutations on susceptibility to other ß-lactam antibiotics. Single-step in vitro selection assays were conducted, resulting in the identification of a series of mutations in the KPC sequence which conferred the ability of those mutated enzymes to confer resistance to CZA. Hence, 16 KPC-2 variants and 10 KPC-3 variants were obtained. Production of the KPC variants in an Escherichia coli recombinant strain resulted in a concomitant increased susceptibility to broad-spectrum cephalosporins and carbapenems, with the exceptions of ceftazidime and piperacillin-tazobactam, compared to wild-type KPC enzymes. Enzymatic assays showed that all of the KPC variants identified exhibited an increased affinity toward ceftazidime and a slightly decreased sensitivity to avibactam, sustaining their impact on CZA resistance. However, their respective carbapenemase activities were concurrently negatively impacted.

NirA Is an Alternative Nitrite Reductase from Pseudomonas aeruginosa with Potential as an Antivirulence Target.

The opportunistic pathogen Pseudomonas aeruginosa produces an arsenal of virulence factors causing a wide range of diseases in multiple hosts and is difficult to eradicate due to its intrinsic resistance to antibiotics. With the antibacterial pipeline drying up, antivirulence therapy has become an attractive alternative strategy to the traditional use of antibiotics to treat P. aeruginosa infections. To identify P. aeruginosa genes required for virulence in multiple hosts, a random library of Tn5 mutants in strain PAO1-L was previously screened in vitro for those showing pleiotropic effects in the production of virulence phenotypes. Using this strategy, we identified a Tn5 mutant with an insertion in PA4130 showing reduced levels of a number of virulence traits in vitro Construction of an isogenic mutant in this gene presented results similar to those for the Tn5 mutant. Furthermore, the PA4130 isogenic mutant showed substantial attenuation in disease models of Drosophila melanogaster and Caenorhabditis elegans as well as reduced toxicity in human cell lines. Mice infected with this mutant demonstrated an 80% increased survival rate in acute and agar bead lung infection models. PA4130 codes for a protein with homology to nitrite and sulfite reductases. Overexpression of PA4130 in the presence of the siroheme synthase CysG enabled its purification as a soluble protein. Methyl viologen oxidation assays with purified PA4130 showed that this enzyme is a nitrite reductase operating in a ferredoxin-dependent manner. The preference for nitrite and production of ammonium revealed that PA4130 is an ammonia:ferredoxin nitrite reductase and hence was named NirA.IMPORTANCE The emergence of widespread antimicrobial resistance has led to the need for development of novel therapeutic interventions. Antivirulence strategies are an attractive alternative to classic antimicrobial therapy; however, they require identification of new specific targets which can be exploited in drug discovery programs. The host-specific nature of P. aeruginosa virulence adds complexity to the discovery of these types of targets. Using a sequence of in vitro assays and phylogenetically diverse in vivo disease models, we have identified a PA4130 mutant with reduced production in a number of virulence traits and severe attenuation across all infection models tested. Characterization of PA4130 revealed that it is a ferredoxin-nitrite reductase and hence was named NirA. These results, together with attenuation of nirA mutants in different clinical isolates, high level conservation of its gene product in P. aeruginosa genomes, and the lack of orthologues in human genomes, make NirA an attractive antivirulence target.

Deep Learning for Imaging and Detection of Microorganisms.

Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms.

Biosensing Detection of the SARS-CoV-2 D614G Mutation.

The emergence of a mutant strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with an amino acid change from aspartate to a glycine residue at position 614 (D614G) has been reported and this mutant appears to be now dominant in the pandemic. Efficient detection of the SARS-CoV-2 D614G mutant by biosensing technologies is therefore crucial for the control of the pandemic.

Discovery of G-quadruplex-forming sequences in SARS-CoV-2.

The outbreak caused by the novel coronavirus SARS-CoV-2 has been declared a global health emergency. G-quadruplex structures in genomes have long been considered essential for regulating a number of biological processes in a plethora of organisms. We have analyzed and identified 25 four contiguous GG runs (G2NxG2NyG2NzG2) in the SARS-CoV-2 RNA genome, suggesting putative G-quadruplex-forming sequences (PQSs). Detailed analysis of SARS-CoV-2 PQSs revealed their locations in the open reading frames of ORF1 ab, spike (S), ORF3a, membrane (M) and nucleocapsid (N) genes. Identical PQSs were also found in the other members of the Coronaviridae family. The top-ranked PQSs at positions 13385 and 24268 were confirmed to form RNA G-quadruplex structures in vitro by multiple spectroscopic assays. Furthermore, their direct interactions with viral helicase (nsp13) were determined by microscale thermophoresis. Molecular docking model suggests that nsp13 distorts the G-quadruplex structure by allowing the guanine bases to be flipped away from the guanine quartet planes. Targeting viral helicase and G-quadruplex structure represents an attractive approach for potentially inhibiting the SARS-CoV-2 virus.

Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs).

Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.

Genetic Features Leading to Reduced Susceptibility to Aztreonam-Avibactam among Metallo-ß-Lactamase-Producing Escherichia coli Isolates.

Metallo-ß-lactamase (MBL)-producing Escherichia coli isolates resistant to the newly developed ß-lactam/ß-lactamase inhibitor drug combination aztreonam-avibactam (ATM-AVI) have been reported. Here, we analyzed a series of 118 clinical MBL-producing E. coli isolates of various geographical origins for susceptibility to ATM-AVI. The nature of the PBP3 protein sequence and the occurrence of blaCMY genes for susceptibility to ATM-AVI were investigated. We showed here that elevated MICs of ATM-AVI among MBL-producing E. coli isolates resulted from a combination of different features, including modification of PBP3 protein sequence through specific amino acid insertions and production of CMY-type enzymes, particularly, CMY-42. We showed here that those insertions identified in the PBP3 sequence are not considered the unique basis of resistance to ATM-AVI, but they significantly contribute to it.

G-quadruplex based biosensor: A potential tool for SARS-CoV-2 detection.

G-quadruplex is a non-canonical nucleic acid structure formed by the folding of guanine rich DNA or RNA. The conformation and function of G-quadruplex are determined by a number of factors, including the number and polarity of nucleotide strands, the type of cations and the binding targets. Recent studies led to the discovery of additional advantageous attributes of G-quadruplex with the potential to be used in novel biosensors, such as improved ligand binding and unique folding properties. G-quadruplex based biosensor can detect various substances, such as metal ions, organic macromolecules, proteins and nucleic acids with improved affinity and specificity compared to standard biosensors. The recently developed G-quadruplex based biosensors include electrochemical and optical biosensors. A novel G-quadruplex based biosensors also show better performance and broader applications in the detection of a wide spectrum of pathogens, including SARS-CoV-2, the causative agent of COVID-19 disease. This review highlights the latest developments in the field of G-quadruplex based biosensors, with particular focus on the G-quadruplex sequences and recent applications and the potential of G-quadruplex based biosensors in SARS-CoV-2 detection.

KPC-50 Confers Resistance to Ceftazidime-Avibactam Associated with Reduced Carbapenemase Activity.

KPC-50 is a KPC-3 variant identified from a Klebsiella pneumoniae clinical isolate recovered in Switzerland in 2019. Compared to KPC-3, KPC-50 shows (i) a three-amino-acid insertion (Glu-Ala-Val) between amino acids 276 and 277, (ii) an increased affinity to ceftazidime, (iii) a decreased sensitivity to avibactam, explaining the ceftazidime-avibactam resistance, and (iv) an association with a sharp reduction of its carbapenemase activity.

Transfer Learning for Toxoplasma gondii Recognition.

Toxoplasma gondii, one of the world's most common parasites, can infect all types of warm-blooded animals, including one-third of the world's human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T. gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well-trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient, and reliable methods of T. gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning-based microscopic image recognition method for T. gondii identification. This approach employs the fuzzy cycle generative adversarial network (FCGAN) with transfer learning utilizing knowledge gained by parasitologists that Toxoplasma is banana or crescent shaped. Our approach aims to build connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for ×400 and ×1,000 Toxoplasma microscopic images, respectively. We showed the high accuracy and effectiveness of our approach in newly collected unlabeled Toxoplasma microscopic images, compared to other currently available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning-based diagnostic solutions, potentially enabling broader clinical access in developing countries.IMPORTANCE Toxoplasma gondii, one of the world's most common parasites, can infect all types of warm-blooded animals, including one-third of the world's human population. Artificial intelligence (AI) could provide accurate and rapid diagnosis in fighting Toxoplasma So far, none of the previously reported deep learning methods have attempted to explore the advantages of transfer learning for Toxoplasma detection. The knowledge from parasitologists is that the Toxoplasma parasite is generally banana or crescent shaped. Based on this, we built connections between microscopic and macroscopic associated objects by embedding the fuzzy C-means cluster algorithm into the cycle generative adversarial network (Cycle GAN). Our approach achieves high accuracy and effectiveness in ×400 and ×1,000 Toxoplasma microscopic images.

Deactivation and mislocalization of Toxoplasma gondii rhoptry protein 18 induced by a single amino acid mutation on the proton transport catalytic aspartic acid.

Rhoptry protein 18 (ROP18) is a major determinant of strain-specific virulence in Toxoplasma gondii. The kinase activity of ROP18 is required for acute virulence, while the aspartate in the catalytic loop of ROP18 is considered essential for phosphoryl transfer. We showed that a single amino acid mutation at the catalytic aspartate residue (D409A mutation) significantly altered ROP18 kinase activity in vitro, and abolished ROP18-mediated ATF6ß degradation. Furthermore, the investigated single amino acid mutation in ROP18 led to alternation of subcellular localization of ROP18 protein. Our findings demonstrate that a single amino acid mutation on the proton transport catalytic aspartic acid induced alternations associated with ROP18 protein.

Toxoplasma gondii secretory proteins and their role in invasion and pathogenesis.

T. gondii is a major opportunistic pathogen chronically infecting nearly one third of the world's population. Due to the high infection and mortality rates in immunocompromised patients and newborns, the extent or magnitude of T. gondii pathogenesis is determined mainly by host-pathogen interactions. T. gondii utilizes specialized secretory proteins to modify host cellular factors and facilitate invasion and replication. This review provides update on the recent progress in this field of research with particular emphasis on the T. gondii secretory proteins and their role in invasion and pathogenesis.