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Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. Objective: This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. Results: The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model's effectiveness was further validated using independent datasets, confirming its potential clinical utility. Discussion: Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. Conclusions: This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers. [ABSTRACT FROM AUTHOR]

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs—piwi-RNAs (piRNAs)—and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers. With this data, we developed a machine learning (ML) algorithm and created 1020 different piRNA sequence descriptors. With the Naïve Bayes Multinomial classifier, we were able to isolate the 27 most influential sequence descriptors and achieve an accuracy of 96.4%. To test the validity of our model, we used data from piRBase with known associations with CRC that we did not use to train the ML model. We were able to achieve an accuracy of 85.7% with these new independent data. To further validate our model, we also tested data from unrelated diseases, including piRNAs with a correlation to breast cancer and no proven correlation to CRC. The model scored 44.4% on these piRNAs, showing that it can identify a difference between biomarkers of CRC and biomarkers of other diseases. The final results show that our model is an effective tool for diagnosing colorectal cancer. We believe that in the future, this model will prove useful for colorectal cancer and other diseases diagnostics. [ABSTRACT FROM AUTHOR]

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. Objectives: The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine learning (ML) model for the early diagnosis of the disease. Methods: We analyzed the blood metabolites of felines with mammary carcinoma using the pathway analysis feature in MetaboAnalyst software, v. 5.0. We utilized machine-learning (ML) methods to recognize FMC using the blood metabolites of sick patients. Results: The metabolic pathways that were elucidated to be associated with this disease include alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein-protein interactions, and MetaboAnalyst 5.0. The best-performing ML model was able to predict metabolite class with an accuracy of 85.11%. Conclusion: Our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma. [ABSTRACT FROM AUTHOR]

Background: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. Methods: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. Results: Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. Conclusions: The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common causes of dementia and movement disorders in the elderly. While progressive accumulation of oligomeric amyloid-β protein (Aβ) has been identified as one of the central toxic events in AD leading to synaptic dysfunction, accumulation of α-synuclein (α-syn) resulting in the formation of oligomers has been linked to PD. Most of the studies in AD have been focused on investigating the role of Aβ and Tau; however, recent studies suggest that α-syn might also play a role in the pathogenesis of AD. For example, fragments of α-syn can associate with amyloid plaques and Aβ promotes the aggregation of α-syn in vivo and worsens the deficits in α-syn tg mice. Moreover, α-syn has also been shown to accumulate in limbic regions in AD, Down’s syndrome, and familial AD cases. Aβ and α-syn might directly interact under pathological conditions leading to the formation of toxic oligomers and nanopores that increase intracellular calcium. The interactions between Aβ and α-syn might also result in oxidative stress, lysosomal leakage, and mitochondrial dysfunction. Thus, better understanding the steps involved in the process of Aβ and α-syn aggregation is important in order to develop intervention strategies that might prevent or reverse the accumulation of toxic proteins in AD.

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis. [ABSTRACT FROM AUTHOR]

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data. [ABSTRACT FROM AUTHOR]

The activity of voltage-gated [K.sup.+] ([K.sub.v]) channels plays an important role in regulating pulmonary artery smooth muscle cell (PASMC) contraction, proliferation, and apoptosis. The highly conserved [NH.sub.2]-terminal tetramerization domain (TI) of [K.sub.v] channels is important for proper channel assembly, association with regulatory [K.sub.v] [beta]-subunits, and localization of the channel to the plasma membrane. We recently reported two nonsynonymous mutations (GI82R and E211D) in the KCNA5 gene of patients with idiopathic pulmonary arterial hypertension, which localize to the TI domain of KCNA5. To study the electrophysiological properties and expression patterns of the mutants compared with the wild-type (WT) channel in vitro, we transfected HEK-293 cells with WT KCNA5, G182R, E211D, or the double mutant G182R/E211D channel. The mutants form functional channels; however, whole cell current kinetic differences between WT and mutant channels exist. Steady-state inactivation curves of the G182R and G182R/E211D channels reveal accelerated inactivation; the mutant channels inactivated at more hyperpolarized potentials compared with the WT channel. Channel protein expression was also decreased by the mutations. Compared with the WT channel, which was present in its mature glycosylated form, the mutant channels are present in greater proportion in their immature form in HEK-293 cells. Furthermore, GI82R protein level is greatly reduced in COS-1 cells compared with WT. Immunostaining data support the hypothesis that, while WT protein localizes to the plasma membrane, mutant protein is mainly retained in intracellular packets. Overall, these data support a role for the T1 domain in channel kinetics as well as in KCNA5 channel subcellular localization. voltage-gated [K.sup.+] channels; T1 domain: pulmonary arterial hypertension; [K.sub.v] 1.5 doi:10.1152/ajpcell.00464.2009

Background: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3‐beta‐glucan synthase is an essential enzyme that builds the cell walls of Candida. Objectives: Targeting CaFKS1, a subunit of the synthase, could be used to fight Candida. Methods: In the present study, a machine‐learning model based on chemical descriptors was trained to recognise drugs that inhibit CaFKS1. The model attained 96.72% accuracy for classifying between active and inactive drug compounds. Descriptors for FDA‐approved and other drugs were calculated, and the model was used to predict the potential activity of these drugs against CaFKS1. Results: Several drugs, including goserelin and icatibant, were detected as active with high confidence. Many of the drugs, interestingly, were gonadotrophin‐releasing hormone (GnRH) antagonists or agonists. A literature search found that five of the predicted drugs inhibit Candida experimentally. Conclusions: This study yields promising drugs to be repurposed to combat Candida albicans infection. Future steps include testing the drugs on fungal cells in vitro. [ABSTRACT FROM AUTHOR]

Successful control of the COVID-19 pandemic depends on vaccines that prevent transmission. The full-length Spike protein is highly immunogenic but the majority of antibodies do not target the virus: ACE2 interface. In an effort to affect the quality of the antibody response focusing it to the receptor-binding motif (RBM) we generated a series of conformationally-constrained immunogens by inserting solvent-exposed RBM amino acid residues into hypervariable loops of an immunoglobulin molecule. Priming C57BL/6 mice with plasmid (p)DNA encoding these constructs yielded a rapid memory response to booster immunization with recombinant Spike protein. Immune sera antibodies bound strongly to the purified receptor-binding domain (RBD) and Spike proteins. pDNA primed for a consistent response with antibodies efficient at neutralizing authentic WA1 virus and three variants of concern (VOC), B.1.351, B.1.617.2, and BA.1. We demonstrate that immunogens built on structure selection can be used to influence the quality of the antibody response by focusing it to a conserved site of vulnerability shared between wildtype virus and VOCs, resulting in neutralizing antibodies across variants. Author summary: Next generation SARS-CoV-2 vaccines need to address the transmission of new virus variants that continue to threaten global health and recovery. This may require new perspectives in immunogen design as well as immune response engineering. Here we applied these ideas in a two-step proof of concept study. First, we designed conformationally-constrained immunogens expressing key residues at the RBM: ACE2 interface to mimic their stereochemical orientation in the native Spike protein. Second, we initiated the process of B cell immunity (priming) using plasmid DNA injected intra-spleen to exploit the spatial organization of a secondary lymphoid organ. Mice given a single booster immunization with intact Spike protein developed a rapid memory antibody response against RBD and Spike proteins. Importantly, immune sera neutralized authentic WA1 virus and the B.1.351/Beta, B.1.617.2/Delta, and BA.1/Omicron variants of concern. Our findings demonstrate that immunogens built on structure selection and lymphoid organ targeting are a powerful way to focus the antibody response to a conserved site of vulnerability shared between wildtype virus and variants of concern. [ABSTRACT FROM AUTHOR]

A number of regulatory circuits in biological systems function through the exchange of phosphoryl groups from one protein to another. Spo0F and Spo0B are components of a phosphorelay that control sporulation in the bacterium Bacillus subtilis through the exchange of a phosphoryl group. Using beryliofluoride as a mimic for phosphorylation, we trapped the interaction of the phosphorylated Spo0F with Spo0B in the crystal lattice. The transition state of phosphoryl transfer continues to be a highly debated issue, as to whether it is associative or dissociative in nature. The geometry of Spo0F binding to Spo0B favors an associative mechanism for phosphoryl transfer. In order to visualize the autophosphorylation of the histidine kinase, KinA, and the subsequent phosphoryl transfer to Spo0F, we generated in silico models representing these reaction steps.

An important goal after structural genomics is to build up the structures of higher-order protein-protein complexes from structures of the individual subunits. Often structures of higher order complexes are difficult to obtain by crystallography. We have used an alternative approach in which the structures of the individual catalytic (C) subunit and [R[sup.l]] regulatory (R) subunit of PKA were first subjected to computational docking, and the top 100,000 solutions were subsequently filtered based on amide hydrogen/ deuterium (H[/[sup.2]]H) exchange interface protection data. The resulting set of filtered solutions forms an ensemble of structures in which, besides the inhibitor peptide binding site, a flat interface between the C-terminal lobe of the C-subunit and the A--and B-helices of [R[sup.1]] is uniquely identified. This holoenzyme structure satisfies all previous experimental data on the complex and allows prediction of new contacts between the two subunits. matrix-assisted laser desorption ionization/time-of-flight | I protein-protein interactions | PKA hotoenzyme (type I)