Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.

Genome-wide identification of DCL, AGO and RDR gene families and their associated functional regulatory elements analyses in banana (Musa acuminata)

RNA silencing is mediated through RNA interference (RNAi) pathway gene families, i.e., Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) and their cis-acting regulatory elements. The RNAi pathway is also directly connected with the post-transcriptional gene silencing (PTGS) mechanism, and the pathway controls eukaryotic gene regulation during growth, development, and stress response. Nevertheless, genome-wide identification of RNAi pathway gene families such as DCL, AGO, and RDR and their regulatory network analyses related to transcription factors have not been studied in many fruit crop species, including banana (Musa acuminata). In this study, we studied in silico genome-wide identification and characterization of DCL, AGO, and RDR genes in bananas thoroughly via integrated bioinformatics approaches. A genome-wide analysis identified 3 MaDCL, 13 MaAGO, and 5 MaRDR candidate genes based on multiple sequence alignment and phylogenetic tree related to the RNAi pathway in banana genomes. These genes correspond to the Arabidopsis thaliana RNAi silencing genes. The analysis of the conserved domain, motif, and gene structure (exon-intron numbers) for MaDCL, MaAGO, and MaRDR genes showed higher homogeneity within the same gene family. The Gene Ontology (GO) enrichment analysis exhibited that the identified RNAi genes could be involved in RNA silencing and associated metabolic pathways. A number of important transcription factors (TFs), e.g., ERF, Dof, C2H2, TCP, GATA and MIKC_MADS families, were identified by network and sub-network analyses between TFs and candidate RNAi gene families. Furthermore, the cis-acting regulatory elements related to light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and other activities (OT) functions were identified in candidate MaDCL, MaAGO, and MaRDR genes. These genome-wide analyses of these RNAi gene families provide valuable information related to RNA silencing, which would shed light on further characterization of RNAi genes, their regulatory elements, and functional roles, which might be helpful for banana improvement in the breeding program.

In silico identification and characterization of AGO, DCL and RDR gene families and their associated regulatory elements in sweet orange (Citrus sinensis L.)

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which are involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families and their regulatory elements. However, these RNAi gene families and their sub-cellular locations, functional pathways and regulatory components were not extensively investigated in the case of economically and nutritionally important fruit plant sweet orange (Citrus sinensis L.). Therefore, in silico characterization, gene diversity and regulatory factor analysis of RNA silencing genes in C. sinensis were conducted by using the integrated bioinformatics approaches. Genome-wide comparison analysis based on phylogenetic tree approach detected 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi candidate genes in C. sinensis corresponding to the RNAi genes of model plant Arabidopsis thaliana. The domain and motif composition and gene structure analyses for all three gene families exhibited almost homogeneity within the same group members. The Gene Ontology enrichment analysis clearly indicated that the predicted genes have direct involvement into the gene-silencing and other important pathways. The key regulatory transcription factors (TFs) MYB, Dof, ERF, NAC, MIKC_MADS, WRKY and bZIP were identified by their interaction network analysis with the predicted genes. The cis-acting regulatory elements associated with the predicted genes were detected as responsive to light, stress and hormone functions. Furthermore, the expressed sequence tag (EST) analysis showed that these RNAi candidate genes were highly expressed in fruit and leaves indicating their organ specific functions. Our genome-wide comparison and integrated bioinformatics analyses provided some necessary information about sweet orange RNA silencing components that would pave a ground for further investigation of functional mechanism of the predicted genes and their regulatory factors.

Side effects of COVID-19 vaccines and perceptions about COVID-19 and its vaccines in Bangladesh: A Cross-sectional study

•

Significant COVID-19 vaccine hesitancy exists globally, mainly due to safety concerns.

•

This study analyzed the side effects of COVID-19 vaccines in Bangladesh.

•

Less than half of those who received a COVID-19 vaccine experienced side effects.

•

The side effects were mild and regular and lasted 1–3 days only.

•

The findings demonstrate the safety of the COVID-19 vaccines.

Objective

Results

Conclusion

Prediction of Protein-protein Interactions in Arabidopsis thaliana Using Partial Training Samples in a Machine Learning Framework

Background:

Protein-protein interactions (PPI) play a vital role in a wide range of biological processes starting from cell-cell interactions to developmental control in all organisms. However, experimental identification of PPI is often laborious, time-consuming and costly compared to computational prediction. There are several computational prediction models in the literature based on complete training samples, but none of them dealt with the partial training samples.

Objective:

The objective of this work was to develop an effective PPI prediction model for Arabidopsis Thaliana using partial training samples in a machine learning framework.

Methods:

We proposed an effective computational PPI prediction model by combining random forest (RF) classifier and autocorrelation (AC) sequence encoding features with 1:2 ratio of positive- PPI and unknown-PPI samples.

Results:

We observed that the proposed prediction model produces the highest average performance scores of sensitivity (94.62%), AUC (0.92) and pAUC (0.189) with the training datasets and sensitivity (88.14%), AUC (0.89) and pAUC (0.176) with the test datasets of 5-fold crossvalidation compared to other candidate predictors based on LDA, LOGI, ADA, NB, KNN & SVM classifiers. It also computed the highest performance scores of TPR (91.82%) and pAUC (0.174) at FPR= 20% with AUC (0.948) compared to other candidate predictors.

Conclusion:

Overall performance of the developed model revealed that our proposed predictor might be useful to elucidate the biological function of unseen PPIs from a large number of candidate proteins in Arabidopsis thaliana.

Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework

BACKGROUND

Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a challenging task, since it is laborious, time consuming and expensive. Therefore, computational prediction of PPI is now given emphasis before going to the experimental validation, since it is simultaneously less laborious, time saver and cost minimizer.

OBJECTIVE

The objective of this study is to develop an improved computational method for PPI prediction mapping on Homo sapiens by using the amino acid sequence features in a supervised learning framework.

METHODS

The experimentally validated 91 positive-PPI pairs of human protein sequences were collected from IntAct Molecular Interaction Database. Then we constructed three balanced datasets with ratios 1:1, 1:2 and 1:3 of positive and negative PPI samples. Then we partitioned each dataset into training (80%) and independent test (20%) datasets. Again each training dataset was partitioned into four mutually exclusive groups of equal sizes for interchanging each group with independent test group to perform 5-fold cross validation (CV). Then we trained candidate seven classifiers (NN, SVM, LR, NB, KNN, AB and RF) with each ratio case to obtain the better PPI predictor by comparing their performance scores.

RESULTS

The random forest (RF) based predictor that was trained with 1:2 ratio of positive-PPI and negative-PPI samples based on AAC encoding features provided the most accurate PPI prediction by producing the highest average performance scores of accuracy (93.50%), sensitivity (95.0%), MCC (85.2%), AUC (0.941) and pAUC (0.236) with the 5-fold cross-validation. It also achieved the highest average performance scores of accuracy (92.0%), sensitivity (94.0%), MCC (83.6%), AUC (0.922) and pAUC (0.207) with the independent test datasets in a comparison of the other candidate and existing predictors.

CONCLUSION

The final resultant prediction strongly recommend that the RF based predictor is a better prediction model of PPI mapping on Homo sapiens.

Life-history traits of ten commercially important small indigenous fish species (SIFS) in the Oxbow lake (Southwestern Bangladesh): key for sound management

For the first time, we revealed the life-history traits including growth pattern (length-weight relationships, LWRs), condition factors, form factor (a_3.0), first sexual maturity (L_m), age at first sexual maturity (t_m), life span (t_max), natural mortality (M_w), asymptotic length (L_∞), and optimum catchable length (L_opt) of ten commercially important small indigenous fish species (SIFS) in the Oxbow lake (Baor), southwestern regions of Bangladesh. A total of 1651 specimens were sampled during January to December 2020 with traditional fishing gears including seine nets, gill nets, and lift nets. Individual total length (TL) and body weight (BW) were measured by digital slide calipers and digital balance, respectively. To calculate the L_m, empirical maximum length-based model was considered, and L_opt was calculated based on L_∞. The TL vs. BW relationship indicated positive allometric growth for Chanda nama (Hamilton 1822), Channa punctata (Bloch 1793), Channa striata (Bloch 1793), Lepidocephalichthys guntea (Hamilton 1822), Macrognathus pancalus (Hamilton 1822), and Puntius sophore (Hamilton 1822), but negative allometric growth for Badis badis (Hamilton 1822), Gudusia chapra (Hamilton 1822), Glossogobius giuris (Hamilton 1822), and Hyporhamphus limbatus (Valenciennes, 1847). All r² values exceed 0.910 that indicated all LWRs were highly significant (P < 0.001). According to Spearman correlation test, Fulton's condition factor (K_F) vs. BW was highly correlated (P < 0.001), indicating better well-being for these species. Moreover, a_3.0 indicates B. badis, C. punctata, C. striata, G. giuris, H. limbatus, L. guntea were elongated; C. nama, P. sophore, were short and deep; G. chapra was fusiform, and M. pancalus was eel-like body shape respectively. The minimum t_m and t_max were obtained as 0.74 year and 2.66 year for C. striata and maximum were 0.93 year and 3.31 year for B. badis, respectively. This study provided information on t_m and t_max for ten SIFS that is globally absent. From empirical models, the smallest mean value of L_m was found for B. badis (3.98 cm), and the greatest was found for C. striata (16.96 cm). The minimum L_opt was obtained as 3.78 cm TL for B. badis and maximum was 14.09 cm TL for C. punctata. The minimum M_w was documented as 1.39 for B. badis and maximum was 1.73 for C. striata. The output of this research will be helpful for developing sustainable management policies and protection of SIFS through the application of mesh size based on L_m and L_opt in the Oxbow lakes, Bangladesh and neighboring countries.

Knowledge, attitude, and practices toward food safety among students in Bangladesh: A cross-sectional web-based study

Background

Foodborne diseases are a preventable but under-reported public health issue. These illnesses are a public health concern and contribute significantly to healthcare costs. People must understand how their knowledge, attitudes, and practices affect food safety and how they can reduce the risk of foodborne illness. This study aimed at investigating the current situation of knowledge, attitudes, and practices toward food safety among Bangladeshi students and identifying the determinants of having adequate knowledge, favorable attitudes, and good practices.

Methods

The research is based on a cross-sectional anonymous online survey that took place from January 1st to February 15th, 2022. Participants in this survey had to be at least 8th-grade students enrolled in Bangladeshi institutions. Upon description of the study's aim, the questionnaire's concept, assurances regarding respondents' confidentiality, and the study's voluntary nature, informed consent was taken from each participant before starting the survey. Descriptive statistics, Chi-square test, and logistic regression were used to explore the knowledge, attitudes, and practices of students and identify factors affecting them using the statistical software STATA.

Results

A total of 777 students participated in the study, the majority of them were male (63.96%) and aged between 18 and 25 years (60%). Almost half of the respondents were at the undergraduate level and less than half of the participants (45%) lived with their families. Among the participants, around 47% had adequate knowledge, 87% had favorable attitudes, and only 52% had good practices toward food safety. Female students, students having a food safety course/training, and students whose mothers were educated had significantly higher knowledge of food safety. Besides, students at higher education levels, students having a food safety course/training, and students with educated mothers displayed significantly higher odds of possessing favorable attitudes toward food safety. Similarly, female students, having a food safety course/training, students at higher education levels, and students with educated mothers were significantly associated with good practices toward food safety among students.

Conclusion

The study shows that students in Bangladesh lack knowledge of food safety and have poor practices toward food safety. For the student population of Bangladesh, more systematic and targeted food safety education and training are required.

Assessing the exploitation status of main catfish Eutropiichthys vacha based on length based stock assessment models in the Kaptai Lake from Bangladesh

The current study focuses on the detailed data on stock assessments including population structure, growth parameters, mortality, recruitment pattern, exploitation rate (E), maximum sustainable yield (MSY) and relative yield per-recruit of Eutropiichthys vacha (Hamilton, 1822) based on 2512 specimens through regular monthly sampling using gill net, cast net, and square lift net in the Kaptai Lake, located in the hilly region of Bangladesh during January to December 2017. Total length (TL) and body weight (BW) were measured using digital slide calipers and electronic balance with 0.01 cm and 0.01g accuracy for each individual. The asymptotic length (L_∞) was 44.40 cm and growth coefficient (K) was 0.70 year⁻¹. The growth performance index (Ø′) was 3.14. The age at zero length (t₀) was 0.027 year and life-span (t_max) was 2.73 year. We estimated total mortality (Z), natural mortality (M) and fishing mortality (F) as 4.23, 1.27 and 2.96 year⁻¹, respectively. The recruitment pattern was throughout the year with two pick-events during May and September. Length at first capture (L_c) was 20.65 cm TL. The E was 0.70 where the E_max (exploitation rate producing maximum yield) was 0.45 which indicates 25% over fishing. The MSY was estimated as 34257 metric ton. In conclusion, the results of this study would be very operative to execute specific management for E. vacha in Kaptai Lake, Hilly region of Bangladesh.

Network Biology Approaches to Uncover Therapeutic Targets Associated with Molecular Signaling Pathways from circRNA in Postoperative Cognitive Dysfunction Pathogenesis

Postoperative cognitive dysfunction (POCD) is a cognitive deterioration and dementia that arise after a surgical procedure, affecting up to 40% of surgery patients over the age of 60. The precise etiology and molecular mechanisms underlying POCD remain uncovered. These reasons led us to employ integrative bioinformatics and machine learning methodologies to identify several biological signaling pathways involved and molecular signatures to better understand the pathophysiology of POCD. A total of 223 differentially expressed genes (DEGs) comprising 156 upregulated and 67 downregulated genes were identified from the circRNA microarray dataset by comparing POCD and non-POCD samples. Gene ontology (GO) analyses of DEGs were significantly involved in neurogenesis, autophagy regulation, translation in the postsynapse, modulating synaptic transmission, regulation of the cellular catabolic process, macromolecule modification, and chromatin remodeling. Pathway enrichment analysis indicated some key molecular pathways, including mTOR signaling pathway, AKT phosphorylation of cytosolic targets, MAPK and NF-κB signaling pathway, PI3K/AKT signaling pathway, nitric oxide signaling pathway, chaperones that modulate interferon signaling pathway, apoptosis signaling pathway, VEGF signaling pathway, cellular senescence, RANKL/RARK signaling pathway, and AGE/RAGE pathway. Furthermore, seven hub genes were identified from the PPI network and also determined transcription factors and protein kinases. Finally, we identified a new predictive drug for the treatment of SCZ using the LINCS L1000, GCP, and P100 databases. Together, our results bring a new era of the pathogenesis of a deeper understanding of POCD, identified novel therapeutic targets, and predicted drug inhibitors in POCD.

Molecular insight into the genomic variation of SARS-CoV-2 strains from current outbreak

Coronavirus disease 2019 (COVID-19) is the newly emerging viral disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The epidemic sparked in December 2019 at Wuhan city, China that causes a large global outbreak and a major public health catastrophe. Till now, more than 129 million positive cases have been reported in which more than 2.81 million were dead, surveyed by Johns Hopkins University, USA. The diverse symptoms of COVID-19 and an increased number of positive cases throughout the world hypothesize that this virus assembles more variants that are preventing the pursuit of its adequate treatment as well as the development of the vaccine. In this study, 715 SARS-CoV-2 genomes were retrieved from the gisaid and NCBI viral resources involving 39 countries and 164 different types of variants were identified based on 108 Single Nucleotide Polymorphisms (SNPs) in which the ancestral type of SARS-CoV-2 was found as the most frequent and the most prevalent in China. Moreover, variant type A104 was identified as the most frequent in the USA and A52 in Japan. The study also recognized the most common SNPs such as 241, 3037, 8782, 11083, 14408, 23403, and 28144 as well as variants regarding base-pair, C > T. A total of 65 non-synonymous SNPs were recognized which were mostly located in nucleocapsid phosphoprotein, Non-structural protein 3(Nsp3), and spike glycoprotein encoding gene. Molecular divergence analysis revealed that this virus was phylogenetically related to Yunnan 2013 bat strain. This study indicates SARS-CoV-2 frequently alters their genetic material, which mostly affects the nucleocapsid phosphoprotein, and spike glycoprotein-encoding gene and makes it very challenging to develop SARS-Cov-2 vaccine and antibody-mediated rapid diagnostic kit.

An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier

BACKGROUND

Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development.

METHODS

In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of k-spaced amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets were collected from two different sources.

RESULTS

The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models.

CONCLUSION

The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.

Genome-wide identification, classification, and characterization of lectin gene superfamily in sweet orange (Citrus sinensis L.)

Lectins are sugar-binding proteins found abundantly in plants. Lectin superfamily members have diverse roles, including plant growth, development, cellular processes, stress responses, and defense against microbes. However, the genome-wide identification and functional analysis of lectin genes in sweet orange (Citrus sinensis L.) remain unexplored. Therefore, we used integrated bioinformatics approaches (IBA) for in-depth genome-wide identification, characterization, and regulatory factor analysis of sweet orange lectin genes. Through genome-wide comparative analysis, we identified a total of 141 lectin genes distributed across 10 distinct gene families such as 68 CsB-Lectin, 13 CsLysin Motif (LysM), 4 CsChitin-Bind1, 1 CsLec-C, 3 CsGal-B, 1 CsCalreticulin, 3 CsJacalin, 13 CsPhloem, 11 CsGal-Lec, and 24 CsLectinlegB.This classification relied on characteristic domain and phylogenetic analysis, showing significant homology with Arabidopsis thaliana's lectin gene families. A thorough analysis unveiled common similarities within specific groups and notable variations across different protein groups. Gene Ontology (GO) enrichment analysis highlighted the predicted genes' roles in diverse cellular components, metabolic processes, and stress-related regulation. Additionally, network analysis of lectin genes with transcription factors (TFs) identified pivotal regulators like ERF, MYB, NAC, WRKY, bHLH, bZIP, and TCP. The cis-acting regulatory elements (CAREs) found in sweet orange lectin genes showed their roles in crucial pathways, including light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and more. These findings will aid in the in-depth molecular examination of these potential genes and their regulatory elements, contributing to targeted enhancements of sweet orange species in breeding programs.

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

A computational biology approach for the identification of potential SARS-CoV-2 main protease inhibitors from natural essential oil compounds

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription. Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7). Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide. Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

Effect of water quality parameters and climatic influences on reproductive phenology of Macrognathus pancalus in wetland ecosystem: recommending a long-term management approach and conservation policy

The study conducted an investigation into the reproductive biology of M. pancalus and assessed the influence of water quality parameters and environmental factors on the spawning pattern within the Gajner Beel ecosystem in Bangladesh. A total of 1218 individuals of M. pancalus (46.39% males and 53.61% females) were collected monthly from the Gajner Beel during January to December 2018 using various fishing gears. The total length (TL) of each individual was measured using digital slide calipers, and the whole body weight (BW) was measured using an electronic balance. Fulton's conditions factor (KF) showed significant differences between males and females. The calculated Lm were 11.11 cm, 11.30 cm, and 11.10 cm based on maximum length, gonadosomatic index (GSI), and the logistic model. The spawning season extended from May through August, with June and July being peak months. The average total fecundity was 1495.52 ± 840.24, with a range of 370 to 4069. During peak spawning season, the average temperature and rainfall were 27°C and 370 mm, respectively. Rainfall, dissolved oxygen, total alkalinity, and pH all had a significant (p < 0.01) positive effect whereas temperature and TDS all had a significant (p > 0.01) negative effect on GSI. Annual air temperature in the study area increased by 0.053 °C/year, with a regression coefficient value (r2 = 0.1695), while annual mean rainfall decreased by 5.97mm/year (r2 = 0.076). This research will contribute to the development of conservation and management approaches of Mastacembelidae fish in relation to current climate variability in sub-tropical waters.

Genome-wide identification of DCL, AGO and RDR gene families and their associated functional regulatory element analyses in sunflower (Helianthus annuus)

RNA interference (RNAi) regulates a variety of eukaryotic gene expressions that are engaged in response to stress, growth, and the conservation of genomic stability during developmental phases. It is also intimately connected to the post-transcriptional gene silencing (PTGS) process and chromatin modification levels. The entire process of RNA interference (RNAi) pathway gene families mediates RNA silencing. The main factors of RNA silencing are the Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families. To the best of our knowledge, genome-wide identification of RNAi gene families like DCL, AGO, and RDR in sunflower (Helianthus annuus) has not yet been studied despite being discovered in some species. So, the goal of this study is to find the RNAi gene families like DCL, AGO, and RDR in sunflower based on bioinformatics approaches. Therefore, we accomplished an inclusive in silico investigation for genome-wide identification of RNAi pathway gene families DCL, AGO, and RDR through bioinformatics approaches such as (sequence homogeneity, phylogenetic relationship, gene structure, chromosomal localization, PPIs, GO, sub-cellular localization). In this study, we have identified five DCL (HaDCLs), fifteen AGO (HaAGOs), and ten RDR (HaRDRs) in the sunflower genome database corresponding to the RNAi genes of model plant Arabidopsis thaliana based on genome-wide analysis and a phylogenetic method. The analysis of the gene structure that contains exon-intron numbers, conserved domain, and motif composition analyses for all HaDCL, HaAGO, and HaRDR gene families indicated almost homogeneity among the same gene family. The protein-protein interaction (PPI) network analysis illustrated that there exists interconnection among identified three gene families. The analysis of the Gene Ontology (GO) enrichment showed that the detected genes directly contribute to the RNA gene-silencing and were involved in crucial pathways. It was observed that the cis-acting regulatory components connected to the identified genes were shown to be responsive to hormone, light, stress, and other functions. That was found in HaDCL, HaAGO, and HaRDR genes associated with the development and growth of plants. Finally, we are able to provide some essential information about the components of sunflower RNA silencing through our genome-wide comparison and integrated bioinformatics analysis, which open the door for further research into the functional mechanisms of the identified genes and their regulatory elements.

Genome-Wide Comprehensive Identification and In Silico Characterization of Lectin Receptor-Like Kinase Gene Family in Barley (Hordeum vulgare L.)

Lectin receptor-like kinases (LecRLKs) are a significant subgroup of the receptor-like kinases (RLKs) protein family. They play crucial roles in plant growth, development, immune responses, signal transduction, and stress tolerance. However, the genome-wide identification and characterization of LecRLK genes and their regulatory elements have not been explored in a major cereal crop, barley (Hordeum vulgare L.). Therefore, in this study, integrated bioinformatics tools were used to identify and characterize the LecRLK gene family in barley. Based on the phylogenetic tree and domain organization, a total of 113 LecRLK genes were identified in the barley genome (referred to as HvlecRLK) corresponding to the LecRLK genes of Arabidopsis thaliana. These putative HvlecRLK genes were classified into three groups: 62 G-type LecRLKs, 1 C-type LecRLK, and 50 L-type LecRLKs. They were unevenly distributed across eight chromosomes, including one unknown chromosome, and were predominantly located in the plasma membrane (G-type HvlecRLK (96.8%), C-type HvlecRLK (100%), and L-type HvlecRLK (98%)). An analysis of motif composition and exon-intron configuration revealed remarkable homogeneity with the members of AtlecRLK. Notably, most of the HvlecRLKs (27 G-type, 43 L-type) have no intron, suggesting their rapid functionality. The Ka/Ks and syntenic analysis demonstrated that HvlecRLK gene pairs evolved through purifying selection and gene duplication was the major factor for the expansion of the HvlecRLK gene family. Exploration of gene ontology (GO) enrichment indicated that the identified HvlecRLK genes are associated with various cellular processes, metabolic pathways, defense mechanisms, kinase activity, catalytic activity, ion binding, and other essential pathways. The regulatory network analysis identified 29 transcription factor families (TFFs), with seven major TFFs including bZIP, C2H2, ERF, MIKC_MADS, MYB, NAC, and WRKY participating in the regulation of HvlecRLK gene functions. Most notably, eight TFFs were found to be linked to the promoter region of both L-type HvleckRLK64 and HvleckRLK86. The promoter cis-acting regulatory element (CARE) analysis of barley identified a total of 75 CARE motifs responsive to light responsiveness (LR), tissue-specific (TS), hormone responsiveness (HR), and stress responsiveness (SR). The maximum number of CAREs was identified in HvleckRLK11 (25 for LR), HvleckRLK69 (17 for TS), and HvleckRLK80 (12 for HR). Additionally, HvleckRLK14, HvleckRLK16, HvleckRLK33, HvleckRLK50, HvleckRLK52, HvleckRLK56, and HvleckRLK110 were predicted to exhibit higher responses in stress conditions. In addition, 46 putative miRNAs were predicted to target 81 HvlecRLK genes and HvlecRLK13 was the most targeted gene by 8 different miRNAs. Protein-protein interaction analysis demonstrated higher functional similarities of 63 HvlecRLKs with 7 Arabidopsis STRING proteins. Our overall findings provide valuable information on the LecRLK gene family which might pave the way to advanced research on the functional mechanism of the candidate genes as well as to develop new barley cultivars in breeding programs.

Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool

BACKGROUND

To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores.

OBJECTIVE

This work aims to provide an improved sophisticated predictor of citrullination sites using a benchmark dataset in a machine learning platform.

METHODS

This study presents a reliable citrullination site predictor based on a benchmark dataset containing a 1:1 ratio of positive and negative samples. We classified citrullination sites using the Composition of the K-Spaced Amino Acid Pairs (CKSAAP) and Support Vector Machine (SVM).

RESULTS

We developed PCS predictors using integrated machine-learning methods that produced the highest average scores. Using 10-fold cross-validation on test datasets, the True Positive Rate (TPR) was 98.34%, the True Negative Rate (TNR) was 99.44%, the accuracy was 98.89%, the Mathew Correlation Coefficient (MCC) was 98.21%, the Area Under the ROC Curve (AUC) was 0.999, and the partial Area Under the ROC Curve (pAUC) was 0.1968.

CONCLUSION

According to overall performance, our developed predictor has a significantly higher implementation in comparison with the current tools on the same benchmark dataset. Moreover, it showed better performance metrics on both test and training datasets. Our developed predictor is promising and can be implemented as a complementary technique for identifying fast and precise citrullination sites.