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Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.

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Porphyromonas gingivalis (P. gingivalis), a key pathogen in periodontitis, is associated with neuroinflammation. Periodontal disease increases with age; 70.1% of adults 65 years and older have periodontal problems. However, the P. gingivalis- lipopolysaccharide (LPS)induced mitochondrial dysfunction in neurodegenerative diseases remains elusive. In this study, we investigated the possible role of P. gingivalis-LPS in mitochondrial dysfunction during neurodegeneration. We found that P. gingivalis-LPS treatment activated toll-like receptor (TLR) 4 signaling and upregulated the expression of Alzheimer’s disease-related dementia and neuroinflammatory markers. Furthermore, the LPS treatment significantly exacerbated the production of reactive oxygen species and reduced the mitochondrial membrane potential. Our study highlighted the pivotal role of P. gingivalis-LPS in the repression of serum response factor (SRF) and its co-factor p49/STRAP that regulate the actin cytoskeleton. The LPS treatment repressed the genes involved in mitochondrial function and biogenesis. P. gingivalis-LPS negatively altered oxidative phosphorylation and glycolysis and reduced total adenosine triphosphate (ATP) production. Additionally, it specifically altered the mitochondrial functions in complexes I, II, and IV of the mitochondrial electron transport chain. Thus, it is conceivable that P. gingivalis-LPS causes mitochondrial dysfunction through oxidative stress and inflammatory events in neurodegenerative diseases.

The present work focusses on development of a safe, inexpensive, and more accessible source for biosynthesis of silver nanoparticles. Four different in-house probiotic isolates, i.e., Lactobacillus pentosus S6, Lactobacillus plantarum F22, Lactobacillus crustorum F11, and Lactobacillus paraplantarum KM1 isolated from different food sources, were used in the current study to check their ability to synthesize silver nanoparticles. All the probiotic-synthesized silver nanoparticles show maximum surface plasmon resonance (SPR) at a peak of 450 nm, which confirms the formation of silver nanoparticles. Scanning electron microscopy (SEM) analysis identified the shape and distribution of silver nanoparticles. Transmission electron microscopy (TEM) revealed the average size of synthesized nanoparticles in the range of 10–50 nm, with the smallest size of 5 nm for silver nanoparticles synthesized by L. crustorum F11. Further, Fourier-transform infrared spectroscopy (FTIR) detected the presence of different functional groups responsible for reduction of silver ion to form silver nanoparticles. The antimicrobial activity of these AgNPs was also found to be effective against different bacterial and fungal pathogens, viz., antibiotic-resistant Staphylococcus aureus, Bacillus cereus, Listeria monocytogenes, Pythium aphanidermatum, Fusarium oxysporum, and Phytopthora parasitica. However, L. crustorum F11–synthesized AgNP showed maximum inhibition against all the bacterial and fungal pathogens, with highest against S. aureus (20 ± 0.61 mm) and F. oxysporum (23 ± 0.37). Findings from this study provide a durable and eco-friendly method for the biosynthesis of silver nanoparticles, having strong antimicrobial activity against different multidrug-resistant microorganisms. Graphical abstract

Mammalian Quaking (QKI) protein, a member of STAR family of proteins is a mRNA-binding protein, which post-transcriptionally modulates the target RNA. QKI protein possesses a maxi-KH domain composed of single heterogeneous nuclear ribonucleoprotein K homology (KH) domain and C-terminal QUA2 domain, that binds a sequence-specific QKI RNA recognition element (QRE), CUAAC. To understand the binding specificities for different mRNA sequences of the KH-QUA2 domain of QKI protein, we introduced point mutations at different positions in the QRE resulting in twelve different mRNA sequences with single nucleotide change. We carried out long unbiased molecular dynamics simulations using two different sets of recently updated forcefield parameters: AMBERff14SB+RNAχOL3 and CHARMM36 (with CMAP correction). We analyzed the changes in intermolecular dynamics as a result of mutation. Our results show that AMBER forcefields performed better to model the interactions between mRNA and protein. We also calculated the binding affinities of different mRNA sequences and found that the relative order correlates to the reported experimental studies. Our study shows that the favorable binding with the formation of stable complex will occur when there is an increase of the total intermolecular contacts between mRNA and protein, but without the loss of native contacts within the KH-QUA domain.