Isolation and characterization of rabies monoclonal antibody charge variants.

Current postexposure prophylaxis of rabies includes vaccines, human rabies immunoglobulin (RIG), equine RIG, and recombinant monoclonal antibodies (mAb). In the manufacturing of rabies recombinant mAb, charge variants are the most common source of heterogeneity. Charge variants of rabies mAb were isolated by salt gradient cation exchange chromatography (CEX) to separate acidic and basic and main charge variants. Separated variants were further extensively characterized using orthogonal analytical techniques, which include secondary and tertiary structure determination by far and near ultraviolet circular dichroism spectroscopy. Charge and size heterogeneity were evaluated using CEX, isoelectric focusing (IEF), capillary-IEF, size exclusion chromatography, sodium dodecyl sulfate polyacrylamide gel electrophoresis, and western blotting. Antigen binding affinity was assessed by enzyme linked immuno-sorbent assay and rapid florescence foci inhibition test. Results from structural and physicochemical characterizations concluded that charge variants are formed due to posttranslational modification demonstrating that the charge heterogeneity, these charge variants did neither show any considerable physicochemical change nor affect its biological function. This study shows that charge variants are effective components of mAb and there is no need of deliberate removal, until biological functions of rabies mAb will get affected.

Plant lectins and their usage in preparing targeted nanovaccines for cancer immunotherapy.

Plant lectins, a natural source of glycans with a therapeutic potential may lead to the discovery of new targeted therapies. Glycans extracted from plant lectins are known to act as ligands for C-type lectin receptors (CLRs) that are primarily present on immune cells. Plant-derived glycosylated lectins offer diversity in their N-linked oligosaccharide structures that can serve as a unique source of homogenous and heterogenous glycans. Among the plant lectins-derived glycan motifs, Man9GlcNAc2Asn exhibits high-affinity interactions with CLRs that may resemble glycan motifs of pathogens. Thus, such glycan domains when presented along with antigens complexed with a nanocarrier of choice may bewilder the immune cells and direct antigen cross-presentation - a cytotoxic T lymphocyte immune response mediated by CD8+ T cells. Glycan structure analysis has attracted considerable interest as glycans are looked upon as better therapeutic alternatives than monoclonal antibodies due to their cost-effectiveness, reduced toxicity and side effects, and high specificity. Furthermore, this approach will be useful to understand whether the multivalent glycan presentation on the surface of nanocarriers can overcome the low-affinity lectin-ligand interaction and thereby modulation of CLR-dependent immune response. Besides this, understanding how the heterogeneity of glycan structure impacts the antigen cross-presentation is pivotal to develop alternative targeted therapies. In the present review, we discuss the findings on structural analysis of glycans from natural lectins performed using GlycanBuilder2 - a software tool based on a thorough literature review of natural lectins. Additionally, we discuss how multiple parameters like the orientation of glycan ligands, ligand density, simultaneous targeting of multiple CLRs and design of antigen delivery nanocarriers may influence the CLR targeting efficacy. Integrating this information will eventually set the ground for new generation immunotherapeutic vaccine design for the treatment of various human malignancies.

Dietary Choices Modulate Colorectal Cancer Stem Cells: A Role of FXR Nuclear Receptor.

Intra- and inter-tumor heterogeneity (TMH) among colorectal cancer patients is considered as major hurdles to develop precise, potent, and personalized cancer therapeutics. The discernible factors that contribute to the existence of TMH and associated problems are suggested as genetic, molecular, epigenetic, and environmental pressures including shifts in trend from high-fiber diet to high-fat/processed sugar diet. In essence, components of high fat/processed sugar diet potentiate metabolic re-programing of inherent cellular heterogeneity of cancer stem cells (CSCs) by genetic and epigenetic pathways intersected by the farnesoid X receptor (FXR) nuclear receptor. Therefore, choices of dietary components shape up protumor or antitumor microenvironment by the modulation of FXR regulated transcriptional and epigenetic events in CSCs. In this article, we highlight the major understanding emanated from preclinical and clinical studies that indicate the potential contribution of high fiber/saturated sugar diet toward carcinogenesis of colorectal cancer.

A Leishmania donovani dominant-negative mutant for eIF2α kinase LdeK1 elicits host-protective immune response.

Dominant-negative mutation of LdeK1 gene, an eIF2α kinase from Leishmania donovani, revealed its role in translation regulation in response to nutrient starvation earlier. However, whether the kinase influences the infectivity of the parasites which naturally encounters nutrient deprivation during its life cycle was interesting to investigate. Both in vitro and in vivo experiments resulted in decrease of the parasite burden in peritoneal macrophages and in splenic/ hepatic load, respectively. An insight into the immune response of mice infected with mutant parasite showed enhanced pro-inflammatory cytokines and nitric oxide levels but reduced TH 2 and Treg population. The significantly reduced loss of infectivity of the parasites lacking a functional LdeK1 by modulating the immune response towards host protection makes it a potential vaccine candidate against Leishmaniasis.

Aspergillus fumigatus conidial metalloprotease Mep1p cleaves host complement proteins.

Innate immunity in animals including humans encompasses the complement system, which is considered an important host defense mechanism against Aspergillus fumigatus, one of the most ubiquitous opportunistic human fungal pathogens. Previously, it has been shown that the alkaline protease Alp1p secreted from A. fumigatus mycelia degrades the complement components C3, C4, and C5. However, it remains unclear how the fungal spores (i.e. conidia) defend themselves against the activities of the complement system immediately after inhalation into the lung. Here, we show that A. fumigatus conidia contain a metalloprotease Mep1p, which is released upon conidial contact with collagen and inactivates all three complement pathways. In particular, Mep1p efficiently inactivated the major complement components C3, C4, and C5 and their activation products (C3a, C4a, and C5a) as well as the pattern-recognition molecules MBL and ficolin-1, either by directly cleaving them or by cleaving them to a form that is further broken down by other proteases of the complement system. Moreover, incubation of Mep1p with human serum significantly inhibited the complement hemolytic activity and conidial opsonization by C3b and their subsequent phagocytosis by macrophages. Together, these results indicate that Mep1p associated with and released from A. fumigatus conidia likely facilitates early immune evasion by disarming the complement defense in the human host.

Breast cancer stem cells as last soldiers eluding therapeutic burn: A hard nut to crack.

Cancer stem cells (CSCs) are found in many cancer types, including breast carcinoma. Breast cancer stem cells (BCSCs) are considered as seed of cancer formation and they are associated with metastasis and genotoxic drug resistance. Several studies highlighted the presence of BCSCs in tumor microenvironment and they are accentuated with several carcinoma events including metastasis and resistance to genotoxic drugs and they also rebound after genotoxic burn. Stemness properties of a small population of cells in carcinoma have provided clues regarding the role of tumor microenvironment in tumor pathophysiology. Hence, insights in cancer stem cell biology with respect to molecular signaling, genetics and epigenetic behavior of CSCs have been used to modulate tumor drug resistance due to genotoxic drugs and signaling protein inhibitors. This review summarizes major scientific breakthroughs in understanding the contribution of BCSCs towards tumor's capability to endure destruction inflicted by molecular as well as genotoxic drugs.

Coordinated regulation of p53 apoptotic targets BAX and PUMA by SMAR1 through an identical MAR element.

How tumour suppressor p53 bifurcates cell cycle arrest and apoptosis and executes these distinct pathways is not clearly understood. We show that BAX and PUMA promoters harbour an identical MAR element and are transcriptional targets of SMAR1. On mild DNA damage, SMAR1 selectively represses BAX and PUMA through binding to the MAR independently of inducing p53 deacetylation through HDAC1. This generates an anti-apoptotic response leading to cell cycle arrest. Importantly, knockdown of SMAR1 induces apoptosis, which is abrogated in the absence of p53. Conversely, apoptotic DNA damage results in increased size and number of promyelocytic leukaemia (PML) nuclear bodies with consequent sequestration of SMAR1. This facilitates p53 acetylation and restricts SMAR1 binding to BAX and PUMA MAR leading to apoptosis. Thus, our study establishes MAR as a damage responsive cis element and SMAR1-PML crosstalk as a switch that modulates the decision between cell cycle arrest and apoptosis in response to DNA damage.

Use of 2D FFT and DTW in Protein Sequence Comparison.

Protein sequence comparison remains a challenging work for the researchers owing to the computational complexity due to the presence of 20 amino acids compared with only four nucleotides in Genome sequences. Further, protein sequences of different species are of different lengths; it throws additional changes to the researchers to develop methods, specially alignment-free methods, to compare protein sequences. In this work, an efficient technique to compare protein sequences is developed by a graphical representation. First, the classified grouping of 20 amino acids with a cardinality of 4 based on polar class is considered to narrow down the representational range from 20 to 4. Then a unit vector technique based on a two-quadrant Cartesian system is proposed to provide a new two-dimensional graphical representation of the protein sequence. Now, two approaches are proposed to cope with the varying lengths of protein sequences from various species: one uses Dynamic Time Warping (DTW), while the other one uses a two-dimensional Fast Fourier Transform (2D FFT). Next, the effectiveness of these two techniques is analyzed using two evaluation criteria-quantitative measures based on symmetric distance (SD) and computational speed. An analysis is performed on five data sets of 9 ND4, 9 ND5, 9 ND6, 12 Baculovirus, and 24 TF proteins under the two methods. It is found that the FFT-based method produces the same results as DTW but in less computational time. It is found that the result of the proposed method agrees with the known biological reference. Further, the present method produces better clustering than the existing ones.

MMV method: a new approach to compare protein sequences under binary representation.

In the present work, a new form of descriptor using minimal moment vector (MMV) is introduced to compare protein sequences in the frequency domain under their component wise binary representations. From every sequence, 20 different binary component sequences are formed, each corresponding to 20 amino acids. Each such vector is now shifted from the time domain to the frequency domain by applying the Fast Fourier Transform (FFT). Next, the power spectrum calculated from the FFT values for each component sequence is so normalized that the sum of the components equals 1. The descriptor is defined as a 20-component vector composed of the 20 second-order minimal moments calculated from the normalized spectrum of the 20 component sequences. Once the descriptor is known, the distance matrix is created by applying the Euclidean Distance measure. The phylogenetic tree is generated by applying the unweighted pair group method with the arithmetic mean (UPGMA) algorithm using Molecular Evolutionary Genetics Analysis11 (MEGA11) software. In this work, the datasets used for similarity studies are 9 NADH dehydrogenase 5 (ND5), 12 Baculoviruses, 24 Transferrins (TF) proteins, and 50 Spike Protein of coronavirus. A qualitative measure using rationalized perception is used to compare the effectiveness of the proposed method. Quantitative measure based on symmetric distance (SD) is used to compare the phylogenetic trees of the present method with those obtained by other methods. It is observed that the phylogenetic trees generated by the proposed technique are at par with their known biological references, and they produce results better than those of the earlier methods.Communicated by Ramaswamy H. Sarma.

Choice of Metric Divergence in Genome Sequence Comparison.

The paper introduces a novel probability descriptor for genome sequence comparison, employing a generalized form of Jensen-Shannon divergence. This divergence metric stems from a one-parameter family, comprising fractions up to a maximum value of half. Utilizing this metric as a distance measure, a distance matrix is computed for the new probability descriptor, shaping Phylogenetic trees via the neighbor-joining method. Initial exploration involves setting the parameter at half for various species. Assessing the impact of parameter variation, trees drawn at different parameter values (half, one-fourth, one-eighth). However, measurement scales decrease with parameter value increments, with higher similarity accuracy corresponding to lower scale values. Ultimately, the highest accuracy aligns with the maximum parameter value of half. Comparative analyses against previous methods, evaluating via Symmetric Distance (SD) values and rationalized perception, consistently favor the present approach's results. Notably, outcomes at the maximum parameter value exhibit the most accuracy, validating the method's efficacy against earlier approaches.

Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems.

This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems. The optimization process of GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off mating season. Being highly nocturnal, they are intelligible enough to leave trails as they forage through reeds and grass. Such trails would subsequently lead to food and water sources and shelter. The exploration phase is achieved when they leave the different shelters scattered around their territory to forage and leave trails. It is presumed that the alpha male maintains knowledge about these routes, and as a result, other rats modify their location according to this information. Also, the males are aware of the breeding season and separate themselves from the group. The assumption is that once the group is separated during this season, the foraging activities are concentrated within areas of abundant food sources, which aids the exploitation. Hence, the smart foraging paths and behaviors during the mating season are mathematically represented to realize the design of the GCR algorithm and carry out the optimization tasks. The performance of GCRA is tested using twenty-two classical benchmark functions, ten CEC 2020 complex functions, and the CEC 2011 real-world continuous benchmark problems. To further test the performance of the proposed algorithm, six classic problems in the engineering domain were used. Furthermore, a thorough analysis of computational and convergence results is presented to shed light on the efficacy and stability levels of GCRA. The statistical significance of the results is compared with ten state-of-the-art algorithms using Friedman's and Wilcoxon's signed rank tests. These findings show that GCRA produced optimal or nearly optimal solutions and evaded the trap of local minima, distinguishing it from the rival optimization algorithms employed to tackle similar problems. The GCRA optimizer source code is publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/165241-greater-cane-rat-algorithm-gcra.

Intracellular Ellagic Acid Derived from Goat Urine DMSO Fraction (GUDF) Predicted as an Inhibitor of c-Raf Kinase.

BACKGROUND: Dietary chemicals and their gut-metabolized products are explored for their anti-proliferative and pro-cell death effects. Dietary and metabolized chemicals are different from ruminants such as goats over humans. METHODS: Loss of cell viability and induction of death due to goat urine DMSO fraction (GUDF) derived chemicals were assessed by routine in vitro assays upon MCF-7 breast cancer cells. Intracellular metabolite profiling of MCF-7 cells treated with goat urine DMSO fraction (GUDF) was performed using an in-house designed vertical tube gel electrophoresis (VTGE) assisted methodology, followed by LC-HRMS. Next, identified intracellular dietary chemicals such as ellagic acid were evaluated for their inhibitory effects against transducers of the c-Raf signaling pathway employing molecular docking and molecular dynamics (MD) simulation. RESULTS: GUDF treatment upon MCF-7 cells displayed significant loss of cell viability and induction of cell death. A set of dietary and metabolized chemicals in the intracellular compartment of MCF-7 cells, such as ellagic acid, 2-hydroxymyristic acid, artelinic acid, 10-amino-decanoic acid, nervonic acid, 2,4-dimethyl-2-eicosenoic acid, 2,3,4'- Trihydroxy,4-Methoxybenzophenone and 9-amino-nonanoic acid were identified. Among intracellular dietary chemicals, ellagic acid displayed a strong inhibitory affinity (-8.7 kcal/mol) against c-Raf kinase. The inhibitory potential of ellagic acid was found to be significantly comparable with a known c-Raf kinase inhibitor sorafenib with overlapping inhibitory site residues (ARG450, GLU425, TRP423, VA403). CONCLUSION: Intracellular dietary-derived chemicals such as ellagic acid are suggested for the induction of cell death in MCF-7 cells. Ellagic acid is predicted as an inhibitor of c-Raf kinase and could be explored as an anti-cancer drug.

PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences.

This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suitable alternative to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), which is inherently time-consuming in nature. Initially, principal component analysis (PCA) is used in the proposed scheme to reduce the dimensions of 20 amino acids using seven known chemical characteristics, yielding 20 TP (Total Points) values for each amino acid. The approach of cumulative summing is then used to give a non-degenerate numeric representation of the sequences based on these 20 TP values. A special kind of three-component vector is proposed as a descriptor, which consists of a new type of non-central moment of orders one, two, and three. Subsequently, the proposed model uses Euclidean Distance measures among the descriptors to create a distance matrix. Finally, a phylogenetic tree is constructed using hierarchical agglomerative clustering based on the distance matrix. The results are compared with the UPGMA and other existing methods in terms of the quality and time of constructing the phylogenetic tree. Both qualitative and quantitative analysis are performed as key assessment criteria for analyzing the performance of the proposed model. The qualitative analysis of the phylogenetic tree is performed by considering rationalized perception, while the quantitative analysis is performed based on symmetric distance (SD). On both criteria, the results obtained by the proposed model are more satisfactory than those produced earlier on the same species by other methods. Notably, this method is found to be efficient in terms of both time and space requirements and is capable of dealing with protein sequences of varying lengths.

Protein sequence comparison based on representation on a finite dimensional unit hypercube.

Numerous techniques are used to compare protein sequences based on the values of the physiochemical properties of amino acids. In this work, a single physical/chemical property value based non-binary representation of protein sequences is obtained on a 20 × 20-dimensional unit hypercube. The represented vector expressed in the matrix form is taken as the descriptor. The generalized NTV metric, which is an extension of the NTV metric used for polynucleotide space is taken as a distance measure. Based on this distance measure, a distance matrix is obtained for protein sequence comparison. Using this distance matrix, phylogenetic trees are drawn by using Molecular Evolutionary Genetics Analysis 11 (MEGA11) software applying the neighbor-joining method. Data sets used in this current work are 9-ND4, 9-ND5, 9-ND6, 24 TF-LF proteins, 27 different viruses and 127 proteins from the protein kinase C (PKC) family. Two sets of phylogenetic trees are obtained - one based on property value of polarity and the other based on property value of molecular weight. They are found to be exactly the same. Similar results also hold for other single property value based representation. The present trees are individually tested for efficiency based on the criterion of rationalized perception and computational time. The results of the present method are compared with those obtained earlier by other methods on the same protein sequences using assessment criteria of Symmetric distance (SD), Correlation coefficient, and Rationalized perception. In all the cases, the present results are found to be better than the results of other methods under comparison.Communicated by Ramaswamy H. Sarma.

Non-Coding RNAs in Oral Cancer: Emerging Roles and Clinical Applications.

Oral cancer (OC) is among the most prevalent cancers in the world. Certain geographical areas are disproportionately affected by OC cases due to the regional differences in dietary habits, tobacco and alcohol consumption. However, conventional therapeutic methods do not yield satisfying treatment outcomes. Thus, there is an urgent need to understand the disease process and to develop diagnostic and therapeutic strategies for OC. In this review, we discuss the role of various types of ncRNAs in OC, and their promising clinical implications as prognostic or diagnostic markers and therapeutic targets. MicroRNA (miRNA), long ncRNA (lncRNA), circular RNA (circRNA), PIWI-interacting RNA (piRNA), and small nucleolar RNA (snoRNA) are the major ncRNA types whose involvement in OC are emerging. Dysregulated expression of ncRNAs, particularly miRNAs, lncRNAs, and circRNAs, are linked with the initiation, progression, as well as therapy resistance of OC via modulation in a series of cellular pathways through epigenetic, transcriptional, post-transcriptional, and translational modifications. Differential expressions of miRNAs and lncRNAs in blood, saliva or extracellular vesicles have indicated potential diagnostic and prognostic importance. In this review, we have summarized all the promising aspects of ncRNAs in the management of OC.

Computational approaches for identifying cancer miRNA expressions.

MicroRNAs (miRNAs) play a major role in cancer development and also act as a key factor in many other diseases. In this investigation, we propose three methods for handling miRNA expressions. The first two methods determine whether a miRNA is indicating normal or cancer condition, and the third one determines how many miRNAs are supporting the cancer sample/patient. While Method 1 acts as a two-class classifier and is based on normalized average expression value, Method 2 also does the same and is based on the normalized average intraclass distance. Method 3 checks whether a miRNA belongs to the cancer class or not, provides the percentage of supporting miRNAs for a cancer patient, and is based on weighted normalized average intraclass distance. The values of the weights are determined using exhaustive search by maximizing the accuracy in training samples. The proposed methods are tested on the differentially regulated miRNAs in three types of cancers (breast, colon, and melanoma cancer). The performances of Method 1 and Method 2 are evaluated by F score, Matthews Correlation Coefficient (MCC), and plotting "1--specificity versus sensitivity" in Receiver Operating Characteristic (ROC) space and are found to be superior to the kNN and SVM classifiers for breast, colon, and melanoma cancer data sets. It is also observed that both the sensitivity and the specificity of Method 1 and Method 2 are higher than 0.5. For the same data sets, Method 3 achieved an average accuracy of more than 98% in detecting the miRNAs, supporting the cancer condition.

Conformational transitions of the catalytic domain of heme-regulated eukaryotic initiation factor 2α kinase, a key translational regulatory molecule.

In mammalian cells, the heme-regulated inhibitor (HRI) plays a critical role in the regulation of protein synthesis at the initiation step through phosphorylation of α-subunit of the eukaryotic initiation factor 2 (eIF2). In this study we have cloned and performed biophysical characterization of the kinase catalytic domain (KD) of rabbit HRI. The KD described here comprises kinase 1, the kinase insertion domain (KI) and kinase 2. We report here the existence of an active and stable monomer of HRI (KD). The HRI (KD) containing three tryptophan residues was examined for its conformational transitions occurring under various denaturing conditions using steady-state and time-resolved tryptophan fluorescence, circular dichroism (CD) and hydrophobic dye binding. The parameter A and phase diagram analysis revealed multi-state unfolding and existence of three stable intermediates during guanidine hydrochloride (Gdn-HCl) induced unfolding of HRI (KD). The protein treated with 6 M Gdn-HCl showed collisional and static mechanism of acrylamide quenching and the constants (K(sv) = 3.08 M(-1) and K(s)= 5.62 M(-1)) were resolved using time resolved fluorescence titration. Based on pH, guanidine hydrochloride and temperature mediated transitions, HRI (KD) appears to exemplify a rigid molten globule-like intermediate with compact secondary structure, altered tertiary structure and exposed hydrophobic patches at pH 3.0. The results indicate the inherent structural stability of HRI (KD), a member of the class of stress response proteins.

Mathematical Approach to Protein Sequence Comparison Based on Physiochemical Properties.

The difficult aspect of developing new protein sequence comparison techniques is coming up with a method that can quickly and effectively handle huge data sets of various lengths in a timely manner. In this work, we first obtain two numerical representations of protein sequences separately based on one physical property and one chemical property of amino acids. The lengths of all the sequences under comparison are made equal by appending the required number of zeroes. Then, fast Fourier transform is applied to this numerical time series to obtain the corresponding spectrum. Next, the spectrum values are reduced by the standard inter coefficient difference method. Finally, the corresponding normalized values of the reduced spectrum are selected as the descriptors for protein sequence comparison. Using these descriptors, the distance matrices are obtained using Euclidian distance. They are subsequently used to draw the phylogenetic trees using the UPGMA algorithm. Phylogenetic trees are first constructed for 9 ND4, 9 ND5, and 9 ND6 proteins using the polarity value as the chemical property and the molecular weight as the physical property. They are compared, and it is seen that polarity is a better choice than molecular weight in protein sequence comparison. Next, using the polarity property, phylogenetic trees are obtained for 12 baculovirus and 24 transferrin proteins. The results are compared with those obtained earlier on the identical sequences by other methods. Three assessment criteria are considered for comparison of the results-quality based on rationalized perception, quantitative measures based on symmetric distance, and computational speed. In all the cases, the results are found to be more satisfactory.

Sensing the interactions between carbohydrate-binding agents and N-linked glycans of SARS-CoV-2 spike glycoprotein using molecular docking and simulation studies.

A recent surge in finding new candidate vaccines and potential antivirals to tackle atypical pneumonia triggered by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) needs new and unexplored approaches in solving this global pandemic. The homotrimeric transmembrane spike (S) glycoprotein of coronaviruses which facilitates virus entry into the host cells is covered with N-linked glycans having oligomannose and complex sugars. These glycans provide a unique opportunity for their targeting via carbohydrate-binding agents (CBAs) which have shown their antiviral potential against coronaviruses and enveloped viruses. However, CBA-ligand interaction is not fully explored in developing novel carbohydrate-binding-based antivirals due to associated unfavorable responses with CBAs. CBAs possess unique carbohydrate-binding specificity, therefore, CBAs like mannose-specific plant lectins/lectin-like mimic Pradimicin-A (PRM-A) can be used for targeting N-linked glycans of S glycoproteins. Here, we report studies on the binding and stability of lectins (NPA, UDA, GRFT, CV-N and wild-type and mutant BanLec) and PRM-A with the S glycoprotein glycans via docking and MD simulation. MM/GBSA calculations were also performed for docked complexes. Interestingly, stable BanLec mutant (H84T) also showed similar docking affinity and interactions as compared to wild-type BanLec, thus, confirming that uncoupling the mitogenic activity did not alter the lectin binding activity of BanLec. The stability of the docked complexes, i.e. PRM-A and lectins with SARS-CoV-2 S glycoprotein showed favorable intermolecular hydrogen-bond formation during the 100 ns MD simulation. Taking these together, our predicted in silico results will be helpful in the design and development of novel CBA-based antivirals for the SARS-CoV-2 neutralization.Communicated by Ramaswamy H. Sarma.

Protective role of host complement system in Aspergillus fumigatus infection.

Invasive aspergillosis (IA) is a life-threatening fungal infection for immunocompromised hosts. It is, therefore, necessary to understand the immune pathways that control this infection. Although the primary infection site is the lungs, aspergillosis can disseminate to other organs through unknown mechanisms. Herein we have examined the in vivo role of various complement pathways as well as the complement receptors C3aR and C5aR1 during experimental systemic infection by Aspergillus fumigatus, the main species responsible for IA. We show that C3 knockout (C3-/-) mice are highly susceptible to systemic infection of A. fumigatus. Intriguingly, C4-/- and factor B (FB)-/- mice showed susceptibility similar to the wild-type mice, suggesting that either the complement pathways display functional redundancy during infection (i.e., one pathway compensates for the loss of the other), or complement is activated non-canonically by A. fumigatus protease. Our in vitro study substantiates the presence of C3 and C5 cleaving proteases in A. fumigatus. Examination of the importance of the terminal complement pathway employing C5-/- and C5aR1-/- mice reveals that it plays a vital role in the conidial clearance. This, in part, is due to the increased conidial uptake by phagocytes. Together, our data suggest that the complement deficiency enhances the susceptibility to systemic infection by A. fumigatus.