Protective Efficacy of Multiple Epitope-Based Vaccine against Hyalomma anatolicum, Vector of Theileria annulata and Crimean-Congo Hemorrhagic Fever Virus.

Hyalomma anatolicum is the principal vector for Theileria annulata, T. equi, and T. Lestoquardi in animals and the Crimean-Congo hemorrhagic fever virus in humans. Due to the gradual loss of efficacy of the available acaricides against field tick populations, the development of phytoacaricides and vaccines has been considered the two most critical components of the integrated tick management strategies. In the present study, in order to induce both cellular and humoral immune responses in the host against H. anatolicum, two multi-epitopic peptides (MEPs), i.e., VT1 and VT2, were designed. The immune-stimulating potential of the constructs was determined by in silicoinvestigation on allergenicity (non-allergen, antigenic (0.46 and 1.0046)), physicochemical properties (instability index 27.18 and 35.46), as well as the interaction of constructs with TLRs by docking and molecular dynamics analysis. The immunization efficacy of the MEPs mixed with 8% MontanideTM gel 01 PR against H. anatolicum larvae was determined as 93.3% and 96.9% in VT1- and VT2-immunized rabbits, respectively. Against adults, the efficacy was 89.9% and 86.4% in VT1- and VT2-immunized rabbits, respectively. A significant (p < 0.001) reduction in the anti-inflammatory cytokine (IL-4) and significantly higher IgG response was observed in a VT1-immunized group of rabbits as compared with the response observed in the control group. However, in the case of the VT2-immunized rabbits, an elevated anti-VT2 IgG and pro-inflammatory cytokine (IL-2) (>30 fold) along with a decreased level of anti-inflammatory cytokine IL-4 (0.75 times) was noted. The efficacy of MEP and its potential immune stimulatory responses indicate that it might be useful for tick management.

Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Immunogenicity and protective efficacy of an inactivated Newcastle disease virus vaccine encapsulated in poly-(lactic-co-glycolic acid) nanoparticles.

An immunization experiment was conducted in specific pathogen-free chickens with the inactivated Newcastle disease virus (NDV) vaccine encapsulated in the poly-(lactic-co-glycolic) acid (PLGA) nanoparticles (NP) to evaluate its immunogenicity and protective efficacy. The NDV vaccine was prepared by inactivating one virulent Indian strain of NDV belonging to Genotype VII by using beta-propiolactone. PLGA nanoparticles encapsulating inactivated NDV were prepared by the solvent evaporation method. Scanning electron microscopy and zeta sizer analysis revealed that the (PLGA+NDV) NP were spherical, with an average size of 300 nm, having a zeta potential of -6 mV. The encapsulation efficiency and loading efficiency were 72% and 2.4%, respectively. On immunization trial in chicken, the (PLGA+NDV) NP induced significantly (P < 0.0001) higher levels of HI and IgY antibodies with the peak HI titer of 28 and higher expression of IL-4 mRNA. The consistency of higher antibody levels suggests slow and pulsatile release of the antigens from the (PLGA+NDV) NP. The nano-NDV vaccine also induced cell mediated immunity with higher expression of IFN-γ indicating strong Th1 mediated immune responses in contrast to the commercial oil adjuvanted inactivated NDV vaccine. Further, the (PLGA+NDV) NP afforded 100% protection against the virulent NDV challenge. Our results suggested that PLGA NP have adjuvant potential on induction of humoral as well as Th1 biased cell mediated immune responses and also enhanced protective efficacy of the inactivated NDV vaccine. This study provides an insight for development of PLGA NP based inactivated NDV vaccine using the same genotype circulating in the field as well as for other avian diseases at exigencies.

EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work. Proposed framework utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely Acc, Sn, Pr, Fs, Sp, Ba, and Mcc obtained by proposed framework for test data is ≈ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

Telmisartan Plus Amlodipine as the Preferred Initial Combination in Newly Diagnosed Indian Patients with Hypertension: An Expert Consensus Statements.

INTRODUCTION: Hypertension (HTN) remains one of the most important risk factors for cardiovascular (CV) diseases and a leading cause of mortality worldwide. Despite improvement in detection and treatment, poor blood pressure (BP) control rates are observed globally. The situation in India is alarming with only 22.5% of patients maintaining their BP under control. Initiating early and effective treatment for HTN helps control BP within normal limits and reduces associated health risks. In India, currently, there are no guidelines on the choice of dual combination treatment that can be considered an initial treatment for newly diagnosed HTN patients to achieve effective BP control and reduce CV risks. OBJECTIVE: To provide consensus recommendations for preferred initial combinations in newly diagnosed Indian patients with HTN. METHODOLOGY: A core group of 100 experts with HTN expertise conceptualized and formulated the four key questions based on answerability, effectiveness, potential for translation to clinical practice, novelty, and potential impact on the healthcare burden. A mix of Delphi and Child Health and Nutrition Research Initiative (CHNRI) methods was adopted for acceptance or refusal of recommendations. Likert scale 1-9 was used for scoring. A score of ≥7 was considered "statement accepted," >6.50 "near to acceptance" and <6.50 "not accepted." A vote of ≥7 by at least two-thirds of the experts (66.66%) was mandatory for acceptance of the recommendation. CONCLUSION: Combination therapy could be necessary for a majority of newly diagnosed Indian patients for effective BP control. It can manage HTN with better clinical outcomes. Based on mean rating scores from experts, telmisartan plus amlodipine can be considered the preferred initial combination in the management of newly diagnosed Indian patients with HTN to achieve better BP control and improve CV outcomes.

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides.

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Decellularization of Skin Tissue.

Biomaterials science encompasses elements of medicine, biology, chemistry, materials, and tissue engineering. They are engineered to interact with biological systems to treat, augment, repair, or replace lost tissue function. The choice of biomaterial depends on the procedure being performed, the severity of the patient's condition, and the surgeon's preference. Prostheses made from natural-derived biomaterials are often derived from decellularized extracellular matrix (ECM) of animal (xenograft) or human (allograft) origin. Advantages of using ECM include their resemblance in morphology and three-dimensional structures with that of tissue to be replaced. Due to this, scientists all over are now focusing on naturally derived biomaterials which have been shown to possess several advantages compared to synthetic ones, owing to their biocompatibility, biodegradability, and remodeling properties. Advantages of a naturally derived biomaterial enhance their application for replacement or restoration of damaged organs/tissues. They adequately support cell adhesion, migration, proliferation, and differentiation. Naturally derived biomaterials can induce extracellular matrix formation and tissue repair when implanted into a defect by enhancing attachment and migration of cells from surrounding environment. In the current chapter, we will focus on the natural and synthetic dermal matrix development and all of the progress in this field.

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

Mesenchymal stem cell tailored bioengineered scaffolds derived from bubaline diaphragm and aortic matrices for reconstruction of abdominal wall defects.

Bioengineered scaffolds derived from the decellularized extracellular matrix (ECM) obtained from discarded animal organs and tissues are attractive candidates for regenerative medicine applications. Tailoring these scaffolds with stem cells enhances their regeneration potential making them a suitable platform for regenerating damaged tissues. Thus, the study was designed to investigate the potential of mesenchymal stem cells tailored acellular bubaline diaphragm and aortic ECM for the repair of full-thickness abdominal wall defects in a rabbit model. Tissues obtained from bubaline diaphragm and aorta were decellularized and bioengineered by seeding with rabbit bone marrow derived mesenchymal stem cells (r-BMSC). Full-thickness abdominal wall defects of 3 cm × 4 cm size were created in a rabbit model and repaired using five different prostheses, namely, polypropylene sheet, nonseeded diaphragm ECM, nonseeded aorta ECM, r-BMSC bioengineered diaphragm ECM, and r-BMSC bioengineered aorta ECM. Results from the study revealed that biological scaffolds are superior in comparison to synthetic polymer mesh for regeneration in terms of collagen deposition, maturation, neovascularization, and lack of any significant (P > 0.05) adhesions with the abdominal viscera. Seeding with r-BMSC significantly increased (P < 0.05) the collagen deposition and biomechanical strength of the scaffolds. The bioengineered r-BMSC seeded acellular bubaline diaphragm showed even superior biomechanical strength as compared to synthetic polymer mesh. Tailoring of the scaffolds with the r-BMSC also resulted in significant reduction (P < 0.01) in antibody and cell mediated immune reactions to the xenogeneic scaffolds in rabbit model.

Cardiological society of India document on safety measure during echo evaluation of cardiovascular disease in the time of COVID-19.

An echocardiographic investigation is one of the key modalities of diagnosis in cardiology. There has been a rising presence of cardiological comorbidities in patients positive for COVID-19. Hence, it is becoming extremely essential to look into the correct safety precautions, healthcare professionals must take while conducting an echo investigation. The decision matrix formulated for conducting an echocardiographic evaluation is based on presence or absence of cardiological comorbidity vis-à-vis positive, suspected or negative for COVID-19. The safety measures have been constructed keeping in mind the current safety precautions by WHO, CDC and MoHFW, India.

Selection of biological prosthesis for abdominal wall repair on the basis of in vitro biocompatibility determination.

Research on prostheses for repairing abdominal wall defects has progressed through past decades for developing an ideal prosthesis. The study was designed to compare different extracellular matrix (ECM) derived biological prostheses as alternate to conventional synthetic polymeric prostheses for the repair of full thickness abdominal wall defects. Five biological scaffolds derived from bovine diaphragm, bovine aorta, bovine gall bladder, porcine gall bladder, and rabbit skin were prepared and screened for their in vitro biocompatibility. Decellularized ECMs were subjected to various biocompatibility analyses, namely, water absorption potential, matrix degradation analysis, biomechanical testing, and cytocompatibility analysis. Though the rabbit skin displayed maximum biomechanical strength, due to its rapid degradation, it failed to fulfill the criteria of an ideal prosthesis. ECMs derived from bovine diaphragm and aorta were found to be superior than others based upon hydration and matrix degradation analysis, with best scores for bovine diaphragm followed by bovine aorta. The bovine diaphragm and aorta also displayed sufficient biomechanical strength, with diaphragm being the second highest (next to rabbit skin), in biomechanical strength followed by aorta. None of the biological prosthesis revealed any cytotoxicity. Thus, bovine diaphragm and aorta derived ECM fulfill the necessary criteria for their use as biological prosthesis. Because these prostheses are biocompatible, apart from their low cost, ease of availability, and simple preparation, they present a potential alternative to synthetic prosthesis for repair of abdominal wall defects, especially in veterinary patients.

Neuropathology mediated through caspase dependent extrinsic pathway in goat kids naturally infected with PPRV.

Peste des petits ruminant (PPR), a highly contagious viral disease of small ruminants, is characterized by erosive stomatitis and pneumo-enteritis. However, its neurovirulence potential as observed with other morbilliviruses has not been fully investigated. The present study describes the neuropathological alterations induced by PPR virus through apoptotic pathway. A total number of 12 carcasses of local breed goat kids of either sex were received for postmortem examination. The clinical history was described as symptoms of mucopurulent nasal discharge, high to low grade fever, erosive stomatitis, dyspnoea and profuse watery diarrhoea followed by mortality of 35 goat kids within a week. The pathoanatomical lesions and immunohistochemical demonstration of PPRV antigen in lungs, intestine, spleen and lymph nodes confirmed PPR disease in goats. Grossly, five brain specimens showed moderate to severe leptomeningeal congestion during necropsy. Microscopically, brain sections showed leptomeningitis and nonsuppurative encephalitis characterized by vascular congestion, haemorrhages in the parenchyma, perivascular cuffing with mild to moderate mononuclear cells (mainly lymphocytes and few macrophages), focal to diffuse microgliosis, neuronal degeneration, satellitosis and neuronophagia. Immunolabelling of viral antigen was observed in the cytoplasm of neurons and glial cells. The RT-PCR amplification of N gene fragment also confirmed the presence of PPRV in the brain. The strong immunoreactivity of Caspase-3, Caspase-8 and comparatively lower expression of caspase-9 along with the absence of any reactivity for Apaf-1 antigen in the brain sections indicated the role of caspase dependent extrinsic pathway in inducing neuropathological changes. The presence of apoptotic neurons in the brain by TUNEL assay further confirmed the apoptosis and strong immunoreactivity of iNOS in neurons which suggested the generation of oxidative stress, that might have induced the apoptosis. The overall findings confirm the neurovirulence potential of PPR virus, via the extrinsic pathway of apoptosis, in natural cases of PPR disease in goat kids.

Surface plasmon resonance immunosensor for label-free detection of BIRC5 biomarker in spontaneously occurring canine mammary tumours.

We report detection of Baculoviral inhibitor of apoptosis repeat containing-5 (BIRC5) protein biomarker in dog serum by label-free surface plasmon resonance (SPR) immunosensor. Initially, overexpression of BIRC5 in canine mammary tumour (CMT) tissues was confirmed by real-time PCR. Recombinant BIRC5 was produced and protein specific antibodies developed in guinea pig specifically reacted with native protein in immunohistochemistry and immunocytochemistry. SPR immunosensor was developed by fabricating anti-BIRC5 antibodies on gold sensor disc. The equilibrium dissociation constant, (KD = kd/ka) was 12.1 × 10-12 M; which indicates that antibodies are of high affinity with sensitivity in picomolar range. The SPR assay could detect as low as 6.25 pg/ml of BIRC5 protein in a calibration experiment (r2 = 0.9964). On testing real clinical samples, 95% specificity and 73.33% sensitivity were recorded. The average amount of serum BIRC5 in dogs with CMT was 110.02 ± 9.77 pg/ml; whereas, in non-cancerous disease conditions, 44.79 ± 4.28 pg/ml and in healthy dog sera 30.28 ± 2.99 pg/ml protein was detected. The SPR immunosensor for detection of BIRC5 in dog sera is reported for the first time and this may find prognostic and diagnostic applications in management of CMT. In future, 'on-site' sensors can be developed using this technique for near-patient testing.

Gene expression profiling of spontaneously occurring canine mammary tumours: Insight into gene networks and pathways linked to cancer pathogenesis.

Spontaneously occurring canine mammary tumours (CMTs) are the most common neoplasms of unspayed female dogs leading to thrice higher mortality rates than human breast cancer. These are also attractive models for human breast cancer studies owing to clinical and molecular similarities. Thus, they are important candidates for biomarker studies and understanding cancer pathobiology. The study was designed to explore underlying molecular networks and pathways in CMTs for deciphering new prognostic factors and therapeutic targets. To gain an insight into various pathways and networks associated with the development and pathogenesis of CMTs, comparative cDNA microarray expression profiling was performed using CMT tissues and healthy mammary gland tissues. Upon analysis, 1700 and 1287 differentially expressed genes (DEGs, P ≤ 0.05) were identified in malignant and benign tissues, respectively. DEGs identified from microarray analysis were further annotated using the Ingenuity Systems Pathway Analysis (IPA) tool for detection of deregulated canonical pathways, upstream regulators, and networks associated with malignant, as well as, benign disease. Top scoring key networks in benign and malignant mammary tumours were having central nodes of VEGF and BUB1B, respectively. Cyclins & cell cycle regulation and TREM1 signalling were amongst the top activated canonical pathways in CMTs. Other cancer related significant pathways like apoptosis signalling, dendritic cell maturation, DNA recombination and repair, Wnt/ß-catenin signalling, etc. were also found to be altered. Furthermore, seven proteins (ANXA2, APOCII, CDK6, GATC, GDI2, GNAQ and MYH9) highly up-regulated in malignant tissues were identified by two-dimensional gel electrophoresis (2DE) and MALDI-TOF PMF studies which were in concordance with microarray data. Thus, the study has uncovered ample number of candidate genes associated with CMTs which need to be further validated as therapeutic targets and prognostic markers.

Multiplexed Autoantibody Signature for Serological Detection of Canine Mammary Tumours.

Spontaneously occurring canine mammary tumours (CMTs) are the most common neoplasms of female unspayed dogs and are of potential importance as models for human breast cancer as well. Mortality rates are thrice higher in dogs as compared to humans with breast cancer, which can partly be attributed to lack of diagnostic techniques for their early detection. Human breast cancer studies reveal role of autoantibodies in early cancer diagnosis and also the usefulness of autoantibody panels in increasing the sensitivity, as well as, specificity of diagnostic assays. Therefore, in this study, we took advantage of high-throughput Luminex technique for developing a multiplex assay to detect autoantibody signatures against 5 canine mammary tumour-associated autoantigens (TAAs). These TAAs were expressed separately as fusion proteins with halo tag at the N-terminus, which allows easy and specific covalent coupling with magnetic microspheres. The multiplex assay, comprising a panel of candidate autoantigens (TPI, PGAM1, MNSOD, CMYC & MUC1) was used for screening circulating autoantibodies in 125 dog sera samples, including 75 mammary tumour sera and 50 healthy dog sera. The area under curve (AUC) of the combined panel of biomarkers is 0.931 (p < 0.0001), which validates the discriminative potential of the panel in differentiating tumour patients from healthy controls. The assay could be conducted in 3hrs using only 1ul of serum sample and could detect clinical cases of canine mammary tumour with sensitivity and specificity of 78.6% and 90%, respectively. In this study, we report for the first time a multiplexed assay for detection of autoantibodies in canine tumours, utilizing luminex technology and halo-tag coupling strategy. Further to the best of our knowledge, autoantibodies to CMYC and MUC1 have been reported for the first time in canines in this study.

Development and evaluation of a gold nanoparticle-based immunochromatographic strip test for the detection of canine parvovirus.

Canine parvovirus (CPV) is the leading viral cause of enteritis in dogs and occurs mainly in 6- to 8-week-old pups. Rapid diagnosis of CPV under field conditions is now possible due to commercially available immunochromatographic (IC) assays. However, these commercial kits are somewhat expensive because they utilize a minimum of two monoclonal antibodies (mAbs) targeting different epitopes as capture and detector antibodies. Using only a single mAb for both capture and detection purpose may reduce the sensitivity of the assay. In the present study, efforts were made to develop an economical assay that can be utilized for diagnosis of CPV under Indian conditions with a high level of confidence. Rabbit polyclonal antibodies (pAbs) generated against recombinant truncated VP2 proteins of CPV were used as capture antibodies because they can be produced economically, while a commercial anti-CPV mAb was used as the detector antibody. The detection limit of the test strip was 6.6×105 TCID50/ml, and it specifically detected CPV-2, CPV-2a and CPV-2b while displaying no cross-reactivity with other common canine enteric pathogens. The relative sensitivity/specificity of pAb based strip test was 71%/92% and 71%/100% in relation to the hemagglutination test and a commercial IC kit, respectively, with substantial agreement. In addition, two commercially available mAbs targeting different epitopes were also used for development of another IC assay, which showed sensitivity, and specificity of 82%/87% and 90%/98% in relation to the hemagglutination test and commercial kit. Hence, the present strip test based on a combination of mAb and pAb provides an acceptable alternative for onsite and cost-effective diagnosis of CPV infection.