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Objectives: Litter size is a crucial economic factor in the sheep industry. Several factors and genes influence litter size, making the identification of genes or loci involved a genetic challenge. Gonadotropin-inhibitory hormone (GnIH) is one of several genes that influence sheep's reproductive traits. Thus, this study aimed to investigate whether variations in the GnIH gene affect the reproductive performance of Awassi and Hamdani ewes.

Methods: DNA was extracted from 99 single-progeny ewes and 101 twin ewes. The polymerase chain reaction (PCR) produced amplicons of 262 bp, 275 bp, and 284 bp from exons 1, 2, and 3 of the GnIH gene. Single-strand conformational polymorphism (SSCP) technique was used for genotyping experiments. Sequencing and in silico analysis were performed on each set of SSCP-resolved bands.

Results: Two genotypes of 262 bp amplicons were found: TT and TC. Sequence analysis revealed a novel missense mutation in the TC genotype at position c.122T>C. Five in silico tools were used to assess the impact of this mutation on GnIH protein structure, function, and stability, all of them demonstrated a deleterious effect. An analysis of statistical data revealed a strong correlation between the c.122T>C single-nucleotide polymorphism (SNP) and reproductive performance. Ewes with the SNP 122T>C exhibited a significant increase in litter size, twinning rates, lambing rates, and days to lambing when compared with ewes with the TT genotype. A lower number of lambs were born to ewes with the TT genotype than those with the TC genotype.

Conclusion: These results concluded that the c.122T>C SNP variant positively influences the reproductive performance of Awassi and Hamdani sheep. Sheep that carry the c.122T>C SNP show higher litter size and increased productivity.

The secreted phospholipase A₂ (sPLA₂) isoform, sPLA₂-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA₂-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA₂-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA₂-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA₂-IIA protein levels in the human population, with implications for a wide range of human diseases.

Small non-coding RNAs (sRNAs) regulate the synthesis of virulence factors and other pathogenic traits, which enables the bacteria to survive and proliferate after host infection. While high-throughput sequencing data have proved useful in identifying sRNAs from the intergenic regions (IGRs) of the genome, it remains a challenge to present a complete genome-wide map of the expression of the sRNAs. Moreover, existing methodologies necessitate multiple dependencies for executing their algorithm and also lack a targeted approach for the de novo sRNA identification. We developed an Isolation Forest algorithm-based method and the tool Prediction Of sRNAs using Isolation Forest for the de novo identification of sRNAs from available bacterial sRNA-seq data (http://posif.ibab.ac.in/). Using this framework, we predicted 1120 sRNAs and 46 small proteins in Mycobacterium tuberculosis. Besides, we highlight the context-dependent expression of novel sRNAs, their probable synthesis, and their potential relevance in stress response mechanisms manifested by M. tuberculosis.

Objectives: Chagas Disease, caused by the parasite Trypanosoma cruzi, remains a significant public health concern, particularly in Latin America. The current standard treatment for Chagas Disease, benznidazole, is associated with various side effects, necessitating the search for alternative therapeutic options. In this study, we aimed to identify potential therapeutics for Chagas Disease through a comprehensive computational analysis.

Methods: A library of compounds derived from Cananga odorata was screened using a combination of pharmacophore modeling, structure-based screening, and quantitative structure-activity relationship (QSAR) analysis. The pharmacophore model facilitated the efficient screening of the compound library, while the structure-based screening identified hit compounds with promising inhibitory potential against the target enzyme, sterol-14-alpha demethylase.

Results: The QSAR model predicted the bioactivity of the hit compounds, revealing one compound to exhibit superior activity compared to benznidazole. Evaluation of the physicochemical, pharmacokinetic, toxicity, and medicinal chemistry properties of the hit compounds indicated their drug-like characteristics, oral bioavailability, ease of synthesis, and reduced toxicity profiles.

Conclusion: Overall, our findings present a promising avenue for the discovery of novel therapeutics for Chagas Disease. The identified hit compounds possess favorable drug-like properties and demonstrate potent inhibitory effects against the target enzyme. Further in vitro and in vivo studies are warranted to validate their efficacy and safety profiles.

MYC is a transcription factor crucial for maintaining cellular homeostasis, and its dysregulation is associated with highly aggressive cancers. Despite being considered "undruggable" due to its unstable protein structure, MYC gains stability through its interaction with its partner protein, MAX. The MYC-MAX heterodimer orchestrates the expression of numerous genes that contribute to an oncogenic phenotype. Previous efforts to develop small molecules, disrupting the MYC-MAX interaction, have shown promise in vitro but none have gained clinical approval. Our current computer-aided study utilizes an approach to explore drug repurposing as a strategy for inhibiting the c-MYC-MAX interaction. We have focused on compounds from DrugBank library, including Food and Drug Administration-approved drugs or those under investigation for other medical conditions. First, we identified a potential druggable site on flat interface of the c-MYC protein, which served as the target for virtual screening. Using both activity-based and structure-based screening, we comprehensively assessed the entire DrugBank library. Structure-based virtual screening was performed on AutoDock Vina and Glide docking tools, while activity-based screening was performed on two independent quantitative structure-activity relationship models. We focused on the top 2% of hit molecules from all screening methods. Ultimately, we selected consensus molecules from these screenings-those that exhibited both a stable interaction with c-MYC and superior inhibitory activity against c-MYC-MAX interaction. Among the evaluated molecules, we identified a protein kinase inhibitor (tyrosine kinase inhibitor [TKI]) known as nilotinib as a promising candidate targeting c-MYC-MAX dimer. Molecular dynamic simulations demonstrated a stable interaction between MYC and nilotinib. The interaction with nilotinib led to the stabilization of a region of the MYC protein that is distorted in apo-MYC and is important for MAX binding. Further analysis of differentially expressed gene revealed that nilotinib, uniquely among the tested TKIs, induced a gene expression program in which half of the genes were known to be responsive to c-MYC. Our findings provide the foundation for subsequent in vitro and in vivo investigations aimed at evaluating the efficacy of nilotinib in managing MYC oncogenic activity.

Viral infections and associated illnesses account for approximately 3.5 million global fatalities and public health problems. Medicinal plants, with their wide therapeutic range and minimal side effects, have gained limelight particularly in response to growing concerns about drug resistance and sluggish development of antiviral drugs. This study computationally assessed 11 chemical compounds from Berberis lycium along with two antiviral drugs to inhibit SARS CoV 2 (coronavirus disease 2019 [COVID-19]) RNA-dependent RNA polymerase (RdRP), influenza virus RdRP, and two crucial dengue virus (DENV) enzymes (NS2B/NS3 protease and NS5 polymerase). Berberine and oxyberberine passed all pharmacokinetics analysis filters including Lipinski rule, blood-brain barrier permeant, and cytochrome suppression and demonstrated drug-likeness, bioavailability, and a non-toxic profile. Docking of phytochemicals from B lycium returned promising results with selected viral proteins, ie, DENV NS2BNS3 (punjabine -10.9 kcal/mol), DENV NS5 (punjabine -10.4 kcal/mol), COVID-19 RdRP (oxyacanthine -9.5 kcal/mol), and influenza RdRP (punjabine -10.4 kcal/mol). The optimal pharmacokinetics of berberine exhibited good binding energies with NS2BNS3 (-8.0 kcal/mol), NS5 (-8.3 kcal/mol), COVID RdRP (-7.7 kcal/mol), and influenza RdRP (-8.3 kcal/mol), while molecular dynamics simulation of a 50-ns time scale by GROMACS software package provided insights into the flexibility and stability of the complexes. A hidden treasure trove for antiviral research, berberine, berbamine, berbamunine, oxyberberine, oxyacanthine, baluchistanamine, and sindamine has showed encouraging findings as possible lead compounds. Pharmacological analyses provide credence for the proposed study; nevertheless, as the antiviral mechanisms of action of these phytochemicals are not well understood, additional research and clinical trials are required to demonstrate both their efficacy and toxicity through in vitro and in vivo studies.

The Nipah virus (NiV) belongs to the Henipavirus genus is a serious public health concern causing numerous outbreaks with higher fatality rate. Unfortunately, there is no effective medication available for NiV. To investigate possible inhibitors of NiV infection, we used in silico techniques to discover treatment candidates in this work. As there are not any approved treatments for NiV infection, the NiV-enveloped attachment glycoprotein was set as target for our study, which is responsible for binding to and entering host cells. Our in silico drug design approach included molecular docking, post-docking molecular mechanism generalised born surface area (MM-GBSA), absorption, distribution, metabolism, excretion/toxicity (ADME/T), and molecular dynamics (MD) simulations. We retrieved 418 phytochemicals associated with the neem plant (Azadirachta indica) from the IMPPAT database, and molecular docking was used to ascertain the compounds' binding strength. The top 3 phytochemicals with binding affinities of -7.118, -7.074, and -6.894 kcal/mol for CIDs 5280343, 9064, and 5280863, respectively, were selected for additional study based on molecular docking. The post-docking MM-GBSA of those 3 compounds was -47.56, -47.3, and -43.15 kcal/mol, respectively. As evidence of their efficacy and safety, all the chosen drugs had favorable toxicological and pharmacokinetic (Pk) qualities. We also performed MD simulations to confirm the stability of the ligand-protein complex structures and determine whether the selected compounds are stable at the protein binding site. All 3 phytochemicals, Quercetin (CID: 5280343), Cianidanol (CID: 9064), and Kaempferol (CID: 5280863), appeared to have outstanding binding stability to the target protein than control ribavirin, according to the molecular docking, MM-GBSA, and MD simulation outcomes. Overall, this work offers a viable approach to developing novel medications for treating NiV infection.

Nucleotide base composition plays an influential role in the molecular mechanisms involved in gene function, phenotype, and amino acid composition. GC content (proportion of guanine and cytosine in DNA sequences) shows a high level of variation within and among species. Many studies measure GC content in a small number of genes, which may not be representative of genome-wide GC variation. One challenge when assembling extensive genomic data sets for these studies is the significant amount of resources (monetary and computational) associated with data processing, and many bioinformatic tools have not been optimized for resource efficiency. Using a high-performance computing (HPC) cluster, we manipulated resources provided to the targeted gene assembly program, automated target restricted assembly method (aTRAM), to determine an optimum way to run the program to maximize resource use. Using our optimum assembly approach, we assembled and measured GC content of all of the protein-coding genes of a diverse group of parasitic feather lice. Of the 499 426 genes assembled across 57 species, feather lice were GC-poor (mean GC = 42.96%) with a significant amount of variation within and between species (GC range = 19.57%-73.33%). We found a significant correlation between GC content and standard deviation per taxon for overall GC and GC₃, which could indicate selection for G and C nucleotides in some species. Phylogenetic signal of GC content was detected in both GC and GC₃. This research provides a large-scale investigation of GC content in parasitic lice laying the foundation for understanding the basis of variation in base composition across species.

Kinase enzymes play an important role in cellular proliferation, and inhibition of their activity is a major goal of cancer therapy. Protein kinase inhibitors as benzimidazole derivatives can be applied for prevention or treatment of cancers through inhibition of cell proliferation. To evaluate their protein kinase inhibitory effects, as well as the in silico study for active benzimidazole derivatives. Benzimidazole derivatives has presented significant therapeutic potential against several disorders and known to have numerous biological activities (such as antibacterial, antiviral and anti-inflammatory). Benzimidazole derivatives have shown significant potential in the reduction of viral load as well as in enhancing immunity. To forecast absorption, distribution, metabolism, excretion and toxicity, simply known as ADMET and the Lipinski rule of five parameters of the examined substances, the admetSAR and Swiss ADME were used. The ADMET predictions revealed that the compounds had good and safe pharmacokinetic features, making them acceptable for further development as therapeutic candidates in clinical trials. This study primarily focused on blocking 2 key targets of kinase proteins (CDK4/CycD1 and Aurora B). 2-Phenylbenzimidazole has shown the greatest inhibitory potential (with a binding energy of -8.2 kcal/mol) against protein kinase inhibitors. This study results would pave the potential lead medication for anticancer therapeutic strategies.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.