K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects.

COVID-19, a novel ailment, has received comparatively fewer drugs for its treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues. Hence, SE prediction is essential in COVID-19 drug development. Efficient models are also needed to predict COVID-19 drug SE since most existing research has proposed many classifiers to predict SE for diseases other than COVID-19. This work proposes a novel classifier based on neighbors named K1 K2 Nearest Neighbors (K1K2NN) to predict the SE of the COVID-19 drug from 17 molecules' descriptors and the chemical 1D structure of the drugs. The model is implemented based on the proposition that chemically similar drugs may be assigned similar drug SE, and co-occurring SE may be assigned to chemically similar drugs. The K1K2NN model chooses the first K1 neighbors to the test drug sample by calculating its similarity with the train drug samples. It then assigns the test sample with the SE label having the majority count on the SE labels of these K1 neighbor drugs obtained through a voting mechanism. The model then calculates the SE-SE similarity using the Jaccard similarity measure from the SE co-occurrence values. Finally, the model chooses the most similar K2 SE neighbors for those SE determined by the K1 neighbor drugs and assigns these SE to that test drug sample. The proposed K1K2NN model has showcased promising performance with the highest accuracy of 97.53% on chemical 1D drug structure and outperforms the state-of-the-art multi-label classifiers. In addition, we demonstrate the successful application of the proposed model on gene expression signature datasets, which aided in evaluating its performance and confirming its accuracy and robustness.

Biostimulatory effects of boar seminal gel, saliva and semen on sexual behavior of young boars, gilts and sows.

The present study aimed to identify novel biostimulatory compounds in boar seminal gel (SG), saliva and semen using Gas chromatography-mass spectrometry (GC-MS). The bio-stimulatory effect of SG, SG + saliva and SG + semen on young boar for semen collection as well were employed to study bio-stimulatory effects on gilts and sows. Distilled water (DW) exposure was kept as control. SG, saliva and semen were screened for total 105, 96 and 89 compounds. The highest concentration was of alkanes followed by sugar alcohols, then hydrocarbons, amino acids and fatty acids. Elaidic acid was the novel compound identified in pigs. Significantly higher (p < 0.05) number of males got trained in exposure to SG (80%), SG + saliva (75%) and SG + semen (75%) than control (0%). The time (hrs) taken by young boars to get trained on exposure to combination of SG + saliva (244 ± 22.19) and SG + semen (216 ± 13.14) was lesser (p < 0.05) than SG (356 ± 61.85) alone. Interval (hrs) from initiation of exposure for exhibition of different sexual behaviour by males on exposure to SG, saliva and semen was lesser (p < 0.05) than control. Significantly (p < 0.05) higher number of females showed estrus response to exposure of SG (72.72%), SG + saliva (69.23%) and SG + semen (76.92%) than control (0). Interval (hrs) taken to exhibit estrus was shorter (p < 0.05) in females exposed to SG + saliva (201.88 ± 12.66), SG + semen (198.20 ± 9.42) than SG (262.14 ± 20.06) alone. Interval (hrs) for exhibition of different sexual behaviour by females on exposure to SG + saliva and SG + semen was lesser (p < 0.05) than control. In conclusion, novel compounds were identified in boar seminal gel, saliva and semen with biostimulatory properties have been identified in boar SG, saliva and semen. The combined exposure of SG with saliva and semen has more intense biostimulation effect than SG alone for training of young boars and estrus induction in gilts and sows. Such compounds biostimulatory effects can be exploited for augmenting reproductive efficiency in pigs.

Transcriptome signatures of host tissue infected with African swine fever virus reveal differential expression of associated oncogenes.

African swine fever (ASF) has emerged as a threat to swine production worldwide. Evasion of host immunity by ASF virus (ASFV) is well understood. However, the role of ASFV in triggering oncogenesis is still unclear. In the present study, ASFV-infected kidney tissue samples were subjected to Illumina-based transcriptome analysis. A total of 2463 upregulated and 825 downregulated genes were differentially expressed (p < 0.05). A literature review revealed that the majority of the differentially expressed host genes were key molecules in signaling pathways involved in oncogenesis. Bioinformatic analysis indicated the activation of certain oncogenic KEGG pathways, including basal cell carcinoma, breast cancer, transcriptional deregulation in cancer, and hepatocellular carcinoma. Analysis of host-virus interactions revealed that the upregulated oncogenic RELA (p65 transcription factor) protein of Sus scrofa can interact with the A238L (hypothetical protein of unknown function) of ASFV. Differential expression of oncogenes was confirmed by qRT-PCR, using the H3 histone family 3A gene (H3F3A) as an internal control to confirm the RNA-Seq data. The levels of gene expression indicated by qRT-PCR matched closely to those determined through RNA-Seq. These findings open up new possibilities for investigation of the mechanisms underlying ASFV infection and offer insights into the dynamic interaction between viral infection and oncogenic processes. However, as these investigations were conducted on pigs that died from natural ASFV infection, the role of ASFV in oncogenesis still needs to be investigated in controlled experimental studies.

Incidences of Helicobacter infection in pigs and tracing occupational hazard in pig farmers.

Helicobacter species (spp.) is a gram-negative spiral-shaped motile bacterium that causes gastritis in pigs and also colonizes in the human stomach. The present study assessed the prevalence of Helicobacter spp. in pig gastric mucosa and the stool of pig farmers in Assam, India. A total of 403 stomach samples from pig slaughter points, 74 necropsy samples of pigs from pig farms, and 97 stool samples from pig farmers were collected. Among the pig stomach samples, 43 (20.09%) of those with gastritis showed the presence of Gram-negative, spiral-shaped organisms, while only 3.04% of stomach samples without lesions had these organisms. Scanning Electron Microscopy (SEM) of urease-positive stomach samples revealed tightly coiled Helicobacter bacteria in the mucus lining. Histopathological examination showed chronic gastritis with hemorrhagic necrosis, leucocytic infiltration, and lymphoid aggregates. PCR confirmed the presence of Helicobacter suis in 19.63% of pig stomach samples and 2.08% of pig farmer stool samples. Additionally, 3.12% of the stool samples from pig farmers were positive for Helicobacter pylori. Phylogenetic analysis revealed distinct clusters of Helicobacter suis with other Helicobacter spp. These findings highlight the prevalence of Helicobacter in both pig gastric mucosa and pig farmer stool. The findings highlight the need for improved sanitation and hygiene practices among pig farmers to minimize the risk of Helicobacter infection in humans.

Characterization of the complete mitochondrial genome and identification of signature sequence of Indian wild pig.

Mitochondrial DNA (mtDNA) serves as a valuable molecular marker for constructing matrilineal genealogies and tracing the evolutionary history of animals. This study aimed to characterize the complete mitochondrial genome of the Indian wild pig (IWB) (Sus scrofa cristatus) and identify IWB-specific DNA sequences that could be used as genomic signatures to differentiate IWB from domestic Indian pigs (IDP) in forensic cases. For the purpose, three wild IWB from a rescue centre were used for the characterization of the mitochondrial genome of the IWB. The mitochondrial genome was sequenced by the primer walking technique using 30 overlapping primers. The mitochondrial genome of the IWB was found to be 16,689 bp long containing 37 genes coding for 2 rRNAs, 22 tRNAs, 13 protein coding genes, and 1 D-loop region similar to the mitogenome of other pigs. Sequence analysis of the D-loop of IWB with other IDP indicated some signature sequence for IWB like duplication and transition event from 1090th to 1099th position, deletion of a 10 bp sequence at the 755th position, insertion of (CA) at the 137th position, and substitution of AT to GA at the 638th position. These variations specially the duplication along with transition event causes creation of unique signature sequence (-ACACAAACCT-) in the IWB that could serve as signature sequences for the IWB and be used as markers for differentiation of IWB from IDP breeds in academic as well as forensic or vetero-legal cases. Overall, a total of 36 polymorphic positions were identified in the IWB, with 29 sites being unique to the IWB only and seven being common to the Doom and HDK75 pig breeds. None of the common polymorphic sites were identified in prevailing domestic pig populations. Phylogenetic analysis of the mitochondrial genome revealed the distinct separation of the IWB from IDP. The results of genetic distance evaluation showed that the Doom pig breed was the closest to the IWB. This study provides valuable insights into the mitogenome characterisation, signature sequence and genetic distance analysis of the IWB and establishes a foundation for future studies on the conservation of this protected species.

Natural co-infection of pigs with African swine fever virus and porcine reproductive and respiratory syndrome virus in India.

Porcine reproductive and respiratory syndrome (PRRS) and African swine fever (ASF) are economically important diseases of pigs throughout the world. During an outbreak, all age groups of animals except piglets < 1 month of age were affected with symptoms of high fever, cutaneous hemorrhages, vomition with blood, diarrhea, poor appetite, ataxia, and death. The outbreak was confirmed by the detection of the N gene of the porcine reproductive and respiratory syndrome virus (PRRSV) and the VP72 gene of the African swine fever virus (ASFV) by PCR in representative blood samples from affected pigs followed by Sanger sequencing. Mixed infection was also confirmed by simultaneous detection of both the viruses using multiplex PCR. Phylogenetic analysis of both the viruses revealed that the outbreak was related to ASFV and PRRSV strains from China which were also closely related to the PRRSV and ASFV strains from the recent outbreak from India. The study confirmed the involvement of genotype II of ASFV and genotype 2 of PRRSV in the present outbreak. Interestingly, PRRSV associated with the present outbreak was characterized as a highly pathogenic PRRSV. Therefore, the present study indicates the possibility of future waves or further outbreaks of these diseases (PRRS and ASF) in this region. This is the first report of ASFV and PRRSV co-infection in pigs from India.

Clinicopathological and ultrastructural study of African swine fever outbreak in North-East India.

The present investigation focuses on examining the clinical, histopathological, and ultrastructural changes that occurred in pig, during an outbreak of African swine fever (ASF) in 2022 in Assam, India. The disease initially manifested as a per-acute case with high mortality but without any evident clinical signs. Subsequently, some animals exhibited an acute form of the disease characterized by high fever (104-106 °F), anorexia, vomiting, respiratory distress, and bleeding from the anal and nasal orifices. During acute African swine fever virus (ASFV) infections, elevated levels of pro-inflammatory IL-1α, IL-1ß, IL-6, TNF, CCL2, CCL5, and CXCL10 were detected in the palatine tonsil, lymph nodes, spleen, and kidney using qPCR assay. These molecular changes were associated with haemorrhages, edemas, and lymphoid depletion. Postmortem examinations revealed prominent features such as splenomegaly with haemorrhages, haemorrhagic lymphadenitis, severe petechial haemorrhage in the kidney, pneumonia in the lungs, and necrotic palatine tonsil. Histopathological analysis demonstrated lymphocyte depletion in lymphoid organs, multi-organ haemorrhages, and interstitial pneumonia in the lungs. Scanning electron microscopy (SEM) further confirmed lymphocyte depletion in lymphoid organs through lymphocyte apoptosis and kidney damage with distorted tubules due to red blood cell destruction. Transmission electron microscopy reaffirmed lymphocyte apoptosis by observing chromatin condensation and nucleus margination in lymphocytes of lymphoid organs. These findings provide comprehensive insights into the clinical, histopathological, and ultrastructural aspects of ASF outbreak in pigs. Understanding the pathological changes associated with ASF can contribute to improved diagnosis, prevention, and control measures for this highly contagious and economically devastating viral disease.

Trio-binning of a hinny refines the comparative organization of the horse and donkey X chromosomes and reveals novel species-specific features.

We generated single haplotype assemblies from a hinny hybrid which significantly improved the gapless contiguity for horse and donkey autosomal genomes and the X chromosomes. We added over 15 Mb of missing sequence to both X chromosomes, 60 Mb to donkey autosomes and corrected numerous errors in donkey and some in horse reference genomes. We resolved functionally important X-linked repeats: the DXZ4 macrosatellite and ampliconic Equine Testis Specific Transcript Y7 (ETSTY7). We pinpointed the location of the pseudoautosomal boundaries (PAB) and determined the size of the horse (1.8 Mb) and donkey (1.88 Mb) pseudoautosomal regions (PARs). We discovered distinct differences in horse and donkey PABs: a testis-expressed gene, XKR3Y, spans horse PAB with exons1-2 located in Y and exon3 in the X-Y PAR, whereas the donkey XKR3Y is Y-specific. DXZ4 had a similar ~ 8 kb monomer in both species with 10 copies in horse and 20 in donkey. We assigned hundreds of copies of ETSTY7, a sequence horizontally transferred from Parascaris and massively amplified in equids, to horse and donkey X chromosomes and three autosomes. The findings and products contribute to molecular studies of equid biology and advance research on X-linked conditions, sex chromosome regulation and evolution in equids.

Meta-Analysis of the Prevalence of Porcine Zoonotic Bacterial Pathogens in India: A 13-Year (2010-2023) Study.

The presence of bacterial pathogens such as Brucella spp., Clostridium spp., E. coli, Listeria monocytogenes, Salmonella spp., Staphylococcus spp., and Streptococcus suis not only hampers pig production but also carries significant zoonotic implications. The present study aims to conduct a comprehensive meta-analysis spanning over 13 years (2010-2023) to ascertain the prevalence of these zoonotic bacterial pathogens in Indian pig populations. The study seeks to synthesize data from diverse geographic regions within India and underscores the relevance of the One Health framework. A systematic search of electronic databases was meticulously performed. Inclusion criteria encompassed studies detailing zoonotic bacterial pathogen prevalence in pigs within India during the specified timeframe. Pertinent information including authors, publication year, geographical location, sampling techniques, sample sizes, and pathogen-positive case counts were meticulously extracted. The meta-analysis of zoonotic bacterial pathogens in Indian pig populations (2010-2023) unveiled varying prevalence rates: 9% Brucella spp., 22% Clostridium spp., 19% E. coli, 12% Listeria monocytogenes, 10% Salmonella spp. and Streptococcus suis, and 24% Staphylococcus spp. The application of random effects further revealed additional variability: 6% Brucella spp., 23% Clostridium spp., 24% E. coli, 14% Listeria monocytogenes, 10% Salmonella spp. and Streptococcus suis, and 35% Staphylococcus spp. Notably, the observed heterogeneity (I2) varied significantly from 87% to 99%. The meta-analysis findings underscore the pervasive nature of these diseases throughout India's pig populations, accentuating the substantial impact of these pathogens on pig health and the potential for zoonotic transmission. The present study reinforces the importance of the adoption of a comprehensive One Health approach that acknowledges the intricate interplay between animal, human and environmental health.

Whole genome sequencing-based cataloguing of antibiotic resistant genes in piggery waste borne samples.

The growing use of antibiotics in livestock is one of the main causes of the rapid global spread of antimicrobial resistance (AMR). However, extensive research on AMR in animals is currently absent. In this article, we provide the bacterial antibiotic resistance genes (ARGs) from piggery waste samples in West Bengal, India, based on whole genome sequencing (WGS). According to the study, there are alarmingly high levels of Enterobacteriaceae in piggery waste, especially slaughterhouse waste, that are resistant to beta-lactam, aminoglycoside, sulphonamide, and tetracycline. We found several plasmids carrying multidrug-resistant Enterobacteriaceae including resistant to last-resort medications like colistin and carbapenems. Our findings will serve as a guide for developing AMR management policies for livestock in India and aid in understanding the current AMR profiles of pigs. To grasp the actual situation with AMR in the pig sector, large scale sample screening must be done.

Characterization of piggery farm waste-borne bacterial transposable elements associated with antimicrobial resistance phenotypes.

Even though there is a link between antibiotic resistance and the presence of transposable elements few research has looked at the prevalence and distribution of transposable elements/ integrons in piggery farm samples. Present study identified the presence of six transposable elements namely Tn6763 (Accession number: OQ565300), Tn6764, (Accession number: OQ565299), Tn6765 (Accession number: OQ409902), Tn2003 (Accession number: OQ503494), Tn6072 (Accession number: OQ565298) and Tn6020 (Accession number: OQ503493) in piggery farm waste from India which are belongs to Enterobacteriaceae family. In a conjugative experiment, Klebsiella isolates carrying Tn6020 having the resistant phenotypes for nalidixic acid was used as donor cells while Escherichia coli DH5α Cells carrying chloramphenicol resistant plasmid was employed as recipient cells. Transconjugant bacterial colonies were shown to carry the Tn6020 transposable elements with both nalidixic acid (donor cell origin) and chloramphenicol (recipient cell origin) resistant antibiotic phenotypes. Given the presence of transposable elements in 21.4% of resistant Enterobacteriaceae strains, preventative measures are vital for avoiding the spread of mobile genetic resistance determinants in the piggery sector and to monitor their emergence.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Japanese Encephalitis Virus Genotype III Strains Detection and Genome Sequencing from Indian Pig and Mosquito Vector.

Japanese encephalitis viruses (JEVs) are globally prevalent as deadly pathogens in humans and animals, including pig, horse and cattle. Japanese encephalitis (JE) still remains an important cause of epidemic encephalitis worldwide and exists in a zoonotic transmission cycle. Assam is one of the highly endemic states for JE in India. In the present study, to understand the epidemiological status of JE circulating in pigs and mosquito, particularly in Assam, India, molecular detection of JEV and the genome sequencing of JEV isolates from pigs and mosquitoes was conducted. The genome analysis of two JEV isolates from pigs and mosquitoes revealed 7 and 20 numbers of unique points of polymorphism of nucleotide during alignment of the sequences with other available sequences, respectively. Phylogenetic analysis revealed that the isolates of the present investigation belong to genotype III and are closely related with the strains of neighboring country China. This study highlights the transboundary nature of the JEV genotype III circulation, which maintained the same genotype through mosquito-swine transmission cycles.

BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs.

In silico machine learning based prediction of drug functions considering the drug properties would substantially enhance the speed and reduce the cost of identifying promising drug leads. The drug function prediction capability of different drug properties happens to be different. So assessing these is advantageous in drug discovery. The task of drug function prediction is multi-label in nature reason being, in case of several drugs, multiple functions are associated with a drug. A number of existing works have ignored this inherent multi-label nature of the problem in context of addressing the issue of class imbalance. In the present work, a computational framework named as BRMCF has been proposed for analysing the prediction capability of chemical and biological properties of drugs toward drug functions in view of multi-label nature of problem. It employs Binary Relevance (BR) approach along with five base classifiers for handling the multi-label prediction task and MLSMOTE for addressing the issue of class imbalance. The proposed framework has been validated and compared with BR, Classifier Chains (CC) and Deep Neural Network (DNN) method on four drug properties datasets: SMILES Strings (SS) dataset, 17 Molecular Descriptors (17MD) dataset, Protein Sequences (PS) dataset and drug perturbed Gene EXpression Profiles (GEX) dataset. The analysis of results shows that the proposed framework BRMCF has outperformed BR, CC and DNN method in terms of exact match ratio, precision, recall, F1-score, ROC-AUC which signifies the effectiveness of MLSMOTE. Further, assessment of prediction capability of different drug properties is done and they are ranked as SS GEX PS 17MD. Additionally, the visualization and analysis of drug function co-occurrences signify the appropriateness of the proposed framework for drug function co-occurrence detection and in signaling the new possible drug leads where the detection rate varies from 94.34% to 99.61%.

Development of multiplex PCR assay for simultaneous detection of African swine fever, porcine circo and porcine parvo viral infection from clinical samples.

A diagnostic method for simultaneously detecting and distinguishing African Swine Fever (ASF), porcine circovirus type 2 (PCV2), and porcine parvovirus (PPV) in clinical specimens is critical for differential diagnosis, monitoring, and control in the field. Three primer pairs were designed and used to create a multiplex PCR assay. In addition, 356 porcine post mortem tissue samples from various parts of India's North Eastern region were tested by the developed multiplex PCR assay to demonstrate its accuracy. Using the designed primers, each of the ASF, PCV2 and PPV target genes was amplified, but no other porcine virus genes were detected. The assay's limit of detection was 102 copies/µl of PCV2, PPV, or ASFV. The detection of PCV2, PPV, and ASF in postmortem tissue samples revealed that they are co-circulating in India's North-Eastern region. The percentage positivity (PP) for PCV2, PPV and ASF single infection were 7.02% (25/356), 3.93% (14/356), and 3.37% (12/356), respectively, while the PP for PCV2& PPV co-infection was 2.80% (10/356), ASF & PCV2 co infection was 1.4% (5/356) and the ASF, PPV& PCV2 co-infection was1.40% (5/356). The results also indicate that the ASF can infect pigs alongside PCV and PPV.

Development of CD163 receptor-based enzyme-linked immunosorbent assay for diagnosis of porcine reproductive and respiratory syndrome virus.

Porcine reproductive and respiratory syndrome (PRRS) is an important economical disease in the global swine industry. The accurate detection of the PRRS virus (PRRSV) antigen is essential for the disease control and prevention programme. In this study, an indirect enzyme-linked immunosorbent test (PRRSVCD163-iELISA) was developed for the detection of the PRRSV antigen in samples of post-mortem swine tissue using the recombinant pig CD163 receptor protein as the capture ligand. The test was found to be specific for PRRSV, with no cross-reactions with other prevalent pig viral pathogens. The assay was validated by testing 217 post-mortem porcine tissue samples and the results were found to be satisfactory with a relative accuracy of 88.88%. Our assay is also quite precise, with intra- and inter-assay CVs of 6% and 10%, respectively. These findings imply that the PRRSVCD163-iELISA developed is capable of detecting the PRRSV antigen in swine post-mortem tissue samples. This research showed that porcine CD163, the PRRSV cellular receptor, can be exploited to build a diagnostic technique for the detection of PRRSV antigen. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03376-z.

Association of CD209 (DC-SIGN) rs735240 SNV with paucibacillary leprosy in the Brazilian population and its functional effects.

BACKGROUND: Leprosy, caused by Mycobacterium leprae, is a public health problem in Brazil that affects peripheral nerves, resulting in physical disabilities. During host-pathogen interactions, the immune response determines leprosy outcomes from a localised (paucibacillary) form to a disseminated (multibacillary) form. The recognition of M. leprae involves the DC-SIGN receptor, which is present on the dendritic cells (DCs) and participates in immune activation. OBJECTIVES: To evaluate the association of polymorphisms in the promoter region of the gene encoding DC-SIGN (CD209) and the clinical form of leprosy, and to investigate its functional effects. METHODS: The study population included 406 leprosy patients from an endemic area in Brazil [310 multibacillary (MB); 96 paucibacillary (PB)]. A functional evaluation based on the effects of the single nucleotide variant (SNV) associated with PB leprosy on the specific immune response was also performed. RESULTS: The GA genotype and the presence of the A allele of rs735240 (-939G>A) were associated with PB leprosy [OR: 2.09 (1.18-3.69) and 1.84 (1.07-3.14), respectively]. Carriers of the A allele showed reduced expression of CD209 and TGF-ß1 in leprosy lesions in comparison with individuals with GG genotype, in addition to a higher response to the Mitsuda test. CONCLUSION: These data suggest that rs735240 influences the immune response against M. leprae and clinical presentation of leprosy.

Porcine Circovirus type 2 infected myocardial tissue transcriptome signature.

The goal of this study was to compare the global gene expression profile in cardiac tissues of pig infected with porcine circovirus 2 (PCV2) to that of healthy cells. Since PCV2 infection causes severe cardiovascular lesions, the myocardial tissue model was chosen for this study. In High-throughput transcriptome analysis, DESeq2 and CLC genomics workbench analyses revealed a total of 196 significantly differentially expressed genes (DEGs) (p-value < 0.05). Furthermore, 194 transcripts were upregulated, while only two were downregulated (HSPA6 and DNAJA1), with fold changes ranging from 16.293 to -10.002. Among the KEGG canonical pathways targeted by the DEGs in the functional analysis, adrenergic signalling in cardiomyocytes, Cardiac Muscle Contraction, Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) tends to be enriched. The differentially expressed highly connected (DEHC) biomarker genes in pathogenicity of PCV2 infection, such as LDB3, MYOZ2, CASQ2, TNNT2, MLC2V, MYBPC3, ACTC1, TCAP, TNNI3, TRDN, CSRP3, MYL3, RYR2, LMOD2, MYH7, etc., were identified using protein-protein interaction (PPI) network analysis. The study might provide detailed information on the dysregulated genes and biological pathways in infected myocardial tissues that may be essential for PCV2-related heart pathology.

Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.

The prediction of adverse drug reactions (ADR) is an important step of drug discovery and design process. Different drug properties have been employed for ADR prediction but the prediction capability of drug properties and drug functions in integrated manner is yet to be explored. In the present work, a multi-label deep neural network and MLSMOTE based methodology has been proposed for ADR prediction. The proposed methodology has been applied on SMILES Strings data of drugs, 17 molecular descriptors data of drugs and drug functions data individually and in integrated manner for ADR prediction. The experimental results shows that the SMILES Strings + drug functions has outperformed other types of data with regards to ADR prediction capability.

Association of CD209 (DC-SIGN) rs735240 SNV with paucibacillary leprosy in the Brazilian population and its functional effects

BACKGROUND Leprosy, caused by Mycobacterium leprae, is a public health problem in Brazil that affects peripheral nerves, resulting in physical disabilities. During host-pathogen interactions, the immune response determines leprosy outcomes from a localised (paucibacillary) form to a disseminated (multibacillary) form. The recognition of M. leprae involves the DC-SIGN receptor, which is present on the dendritic cells (DCs) and participates in immune activation. OBJECTIVES To evaluate the association of polymorphisms in the promoter region of the gene encoding DC-SIGN (CD209) and the clinical form of leprosy, and to investigate its functional effects. METHODS The study population included 406 leprosy patients from an endemic area in Brazil [310 multibacillary (MB); 96 paucibacillary (PB)]. A functional evaluation based on the effects of the single nucleotide variant (SNV) associated with PB leprosy on the specific immune response was also performed. RESULTS The GA genotype and the presence of the A allele of rs735240 (-939G>A) were associated with PB leprosy [OR: 2.09 (1.18-3.69) and 1.84 (1.07-3.14), respectively]. Carriers of the A allele showed reduced expression of CD209 and TGF-β1 in leprosy lesions in comparison with individuals with GG genotype, in addition to a higher response to the Mitsuda test. CONCLUSION These data suggest that rs735240 influences the immune response against M. leprae and clinical presentation of leprosy.