Acquired chemoresistance remains a significant challenge in the clinics as most of the treated cancers eventually emerge as hard-to-treat phenotypes. Therefore, identifying chemoresistance targets is highly warranted to manage the disease better. In this study, we employed a label-free LC-MS/MS-based quantitative proteomics analysis to identify potential targets and signaling pathways underlying acquired chemoresistance in a sub-cell line (A549DR) derived from the parental lung adenocarcinoma cells (A549) treated with gradually increasing doses of doxorubicin (DOX). Our proteomics analysis identified 146 upregulated and 129 downregulated targets in A549DR cells. The KEGG pathway and Gene ontology (GO) analysis of differentially expressed upregulated and downregulated proteins showed that most abundant upregulated pathways were related to metabolic pathways, cellular senescence, cell cycle, and p53 signaling. Meanwhile, the downregulated pathways were related to spliceosome, nucleotide metabolism, DNA replication, nucleotide excision repair, and nuclear-cytoplasmic transport. Further, STRING analysis of upregulated biological processes showed a protein-protein interaction (PPI) between CDK1, AKT2, SRC, STAT1, HDAC1, FDXR, FDX1, NPC1, ALDH2, GPx1, CDK4, and B2M, proteins. The identified proteins in this study might be the potential therapeutic targets for mitigating DOX resistance.

Organophosphates constitute a major class of pesticides widely employed in agriculture to manage insect pests. Their toxicity is attributed to their ability to inhibit the functioning of acetylcholinesterase (AChE), an essential enzyme for normal nerve transmission. Organophosphates, especially chlorpyrifos, have been a key component of the integrated pest management (IPM) in onions, effectively controlling onion maggot Delia antiqua, a severe pest of onions. However, the growing concerns over the use of this insecticide on human health and the environment compelled the need for an alternative organophosphate and a potential microbial agent for bioremediation to mitigate organophosphate pesticide pollution. In the present study, chloropyrifos along with five other organophosphate insecticides, phosmet, primiphos-methyl, isofenphos, iodofenphos and tribuphos, were screened against the target protein AChE of D. antiqua using molecular modeling and docking techniques. The results revealed that iodofenphos showed the best interaction, while tribuphos had the lowest interaction with the AChE based on comparative binding energy values. Further, protein-protein interaction analysis conducted using the STRING database and Cytoscap software revealed that AChE is linked with a network of 10 different proteins, suggesting that the function of AChE is disrupted through interaction with insecticides, potentially leading to disruption within the network of associated proteins. Additionally, an in silico study was conducted to predict the binding efficiency of two organophosphate degrading enzymes, organophosphohydrolase (OpdA) from Agrobacterium radiobacter and Trichoderma harzianum paraoxonase 1 like (ThPON1-like) protein from Trichoderma harzianum, with the selected insecticides. The analysis revealed their potential to degrade the pesticides, offering a promising alternative before going for cumbersome onsite remediation.

A novel MADS-box transcription factor from Pinus radiata D. Don was characterized. PrMADS11 encodes a protein of 165 amino acids for a MADS-box transcription factor belonging to group II, related to the MIKC protein structure. PrMADS11 was differentially expressed in the stems of pine trees in response to 45° inclination at early times (1 h). Arabidopsis thaliana was stably transformed with a 35S::PrMADS11 construct in an effort to identify the putative targets of PrMADS11. A massive transcriptome analysis revealed 947 differentially expressed genes: 498 genes were up-regulated, and 449 genes were down-regulated due to the over-expression of PrMADS11. The gene ontology analysis highlighted a cell wall remodeling function among the differentially expressed genes, suggesting the active participation of cell wall modification required during the response to vertical stem loss. In addition, the phenylpropanoid pathway was also indicated as a PrMADS11 target, displaying a marked increment in the expression of the genes driven to the biosynthesis of monolignols. The EMSA assays confirmed that PrMADS11 interacts with CArG-box sequences. This TF modulates the gene expression of several molecular pathways, including other TFs, as well as the genes involved in cell wall remodeling. The increment in the lignin content and the genes involved in cell wall dynamics could be an indication of the key role of PrMADS11 in the response to trunk inclination.

For microbiological confirmation of pediatric pulmonary tuberculosis (PTB), gastric aspirates (GA) are often operationally unfeasible without hospitalization, and the encapsulated orogastric string test is not easily swallowed in young children. The Combined-NasoGastric-Tube-and-String-Test (CNGTST) enables dual collection of GA and string specimens. In a prospective cohort study in Kenya, we examined its feasibility in children under five with presumptive PTB and compared the bacteriological yield of string to GA. Paired GA and string samples were successfully collected in 95.6 % (281/294) of children. Mycobacterium tuberculosis was isolated from 7.0 % (38/541) of GA and 4.3 % (23/541) of string samples, diagnosing 8.2 % (23/281) of children using GA and 5.3 % (15/281) using string. The CNGTST was feasible in nearly all children. Yield from string was two-thirds that of GA despite a half-hour median dwelling time. In settings where the feasibility of hospitalisation for GA is uncertain, the string component can be used to confirm PTB.

Alzheimer\'s disease (AD) is the leading cause of dementia worldwide with therapeutic lacunae till date. The beta-amyloid (Aβ) accumulation triggers AD pathogenesis, though clinical trials lowering Aβ have not altered disease outcomes suggesting other interacting factors to be identified for drug design of AD. Therefore, it is of interest to identify potential hub proteins interlinked with disease-driving pathways using a network-based approach for AD therapeutic designing. Literature mining was done to identify proteins implicated in AD etiology. Protein-protein interactions (PPIs) were retrieved from the STRING database and merged into a single network using Cytoscape 3.10.1. The hub proteins involved in AD etiology were predicted based on the topological algorithms of CytoHubba. Six major proteins, with STRING database identifiers - APP, BACE1, PSEN1, MAPT, APOE4 and TREM2, were identified to be involved in AD pathogenesis. The merged network of PPIs of these proteins contained 51 nodes and 211 edges, as predicted by Analyzer module of Cytoscape. The Amyloid precursor protein (APP) emerged as the highest-scoring hub protein across multiple centrality measures and topological algorithms. Thus, current data provides evidence to support the ongoing investigation of APP\'s multifaceted functions and therapeutic potential for AD.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature.

Leishmaniasis, a parasitic disease caused by different species of the protozoa parasite Leishmania, is a neglected tropical human disease that is endemic in about a hundred countries worldwide. According to the World Health Organization (WHO), the annual incidence of cutaneous leishmaniasis (CL) is estimated to be 0.7-1.2 million cases globally, whereas the annual incidence of visceral leishmaniasis is estimated to be 0.2-0.4 million cases. In many eukaryotic organisms, including human beings and protozoan parasites, centrin genes encode proteins that play essential roles within the centrosome or basal body. Human microRNAs (miRNAs) have been linked to several infectious and non-infectious diseases associated with pathogen-host interactions, and they play the emphatic roles as gene expression regulators. In this study, we used the MirTarget bioinformatics tool, which is a machine learning-based approach implemented in miRDB, to predict the target of human miRNAs in Leishmania donovani centrin genes. For cross-validation, we utilized additional prediction algorithms, namely, RNA22 and RNAhybrid, targeting all five centrin isotypes. The centrin-3 (LDBPK_342160) and putative centrin-5 (NC_018236.1) genes in L. donovani were targeted by eight and twelve human miRNAs, respectively, among 2,635 known miRNAs (miRBase). hsa-miR-5193 consistently targeted both genes. Using TargetScan, TarBase, miRecords, and miRTarBase, we identified miRNA targets and off-targets in human homologs of centrin, inflammation, and immune-responsive genes. Significant targets were screened based on GO terminologies and KEGG pathway-enrichment analysis (Log10 p-value >0.0001). In silico tools that predict the biological roles of human miRNAs as primary gene regulators in pathogen-host interactions help unravel the regulatory patterns of these miRNAs, particularly in the early stages of inflammatory responses. It is also noted that these miRNAs played an important role in the late phase of adaptive immune response, inclusively their impacts on the immune system\'s response to L. donovani.

Alzheimer\'s disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI\'s anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.Communicated by Ramaswamy H. Sarma.

O-GlcNAcylation is a dynamic modulator of signaling pathways, equal in magnitude to the widely studied phosphorylation. With the rapid development of tools for its detection at the single protein level, the O-GlcNAc modification rapidly emerged as a novel diagnostic and therapeutic target in human diseases. Yet, mapping the human O-GlcNAcome in various tissues is essential for generating relevant biomarkers. In this study, we used human banked tissue as a sample source to identify O-GlcNAcylated protein targets relevant to human diseases. Using human term placentas, we propose (1) a method to clean frozen banked tissue of blood proteins; (2) an optimized protocol for the enrichment of O-GlcNAcylated proteins using immunoaffinity purification; and (3) a bioinformatic workflow to identify the most promising O-GlcNAc targets. As a proof-of-concept, we used 45 mg of banked placental samples from two pregnancies to generate intracellular protein extracts depleted of blood protein. Then, antibody-based O-GlcNAc enrichment on denatured samples yielded over 2000 unique HexNAc PSMs and 900 unique sites using 300 μg of protein lysate. Due to efficient sample cleanup, we also captured 82 HexNAc proteins with high placental expression. Finally, we provide a bioinformatic tool (CytOVS) to sort the HexNAc proteins based on their cellular localization and extract the most promising O-GlcNAc targets to explore further. To conclude, we provide a simple 3-step workflow to generate a manageable list of O-GlcNAc proteins from human tissue and improve our understanding of O-GlcNAcylation\'s role in health and diseases.