Machine Learning-Based Characterization and Identification of Tertiary Lymphoid Structures Using Spatial Transcriptomics Data.

Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells in non-lymphoid tissues and are associated with a favorable prognosis in tumors. However, TLS markers remain inconsistent, and the utilization of machine learning techniques for this purpose is limited. To tackle this challenge, we began by identifying TLS markers through bioinformatics analysis and machine learning techniques.

Unveiling IL6R and MYC as Targeting Biomarkers in Imatinib-Resistant Chronic Myeloid Leukemia through Advanced Non-Invasive Apoptosis Detection Sensor Version 2 Detection.

The management of chronic myelogenous leukemia (CML) has seen significant progress with the introduction of tyrosine kinase inhibitors (TKIs), particularly Imatinib. However, a notable proportion of CML patients develop resistance to Imatinib, often due to the persistence of leukemia stem cells and resistance mechanisms independent of BCR::ABL1 This study investigates the roles of IL6R, IL7R, and MYC in Imatinib resistance by employing CRISPR/Cas9 for gene editing and the Non-Invasive Apoptosis Detection Sensor version 2 (NIADS v2) for apoptosis assessment. The results indicate that Imatinib-resistant K562 cells (K562-IR) predominantly express IL6R, IL7R, and MYC, with IL6R and MYC playing crucial roles in cell survival and sensitivity to Imatinib.

Catalytic role of the enol ether intermediate in the intramolecular Stetter reaction: a computational perspective.

The intramolecular Stetter reaction catalyzed by a carbene is investigated by density functional theory (DFT) calculations and kinetic simulations. Catalyst 1 first reacts with aldehyde 2 to give the primary adduct (PA). The PA undergoes the intramolecular oxa-Michael reaction to irreversibly generate enol ether intermediate 9.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped.

Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.

RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies.

Inhibition of ADAM9 promotes the selective degradation of KRAS and sensitizes pancreatic cancers to chemotherapy.

Kirsten rat sarcoma virus (KRAS) signaling drives pancreatic ductal adenocarcinoma (PDAC) malignancy, which is an unmet clinical need. Here, we identify a disintegrin and metalloproteinase domain (ADAM)9 as a modulator of PDAC progression via stabilization of wild-type and mutant KRAS proteins. Mechanistically, ADAM9 loss increases the interaction of KRAS with plasminogen activator inhibitor 1 (PAI-1), which functions as a selective autophagy receptor in conjunction with light chain 3 (LC3), triggering lysosomal degradation of KRAS.

Inhibition of dengue viruses by N-methylcytisine thio derivatives through targeting viral envelope protein and NS2B-NS3 protease.

Dengue virus (DENV) is a significant global health threat, causing millions of cases worldwide each year. Developing antiviral drugs for DENV has been a challenging endeavor. Our previous study identified anti-DENV properties of two (-)-cytisine derivatives contained substitutions within the 2-pyridone core from a pool of 19 (-)-cytisine derivatives.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.

Tuberculosis (TB) is a severe disease caused by that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB.

Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations.

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g.

Discovery and characterization of genes conferring natural resistance to the antituberculosis antibiotic capreomycin.

Metagenomic-based studies have predicted an extraordinary number of potential antibiotic-resistance genes (ARGs). These ARGs are hidden in various environmental bacteria and may become a latent crisis for antibiotic therapy via horizontal gene transfer. In this study, we focus on a resistance gene cph, which encodes a phosphotransferase (Cph) that confers resistance to the antituberculosis drug capreomycin (CMN).

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles.

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics.

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner.

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative.

Dual Effects of 3--betulin from in Suppressing SARS-CoV-2-Induced Inflammation and Inhibiting Virus Entry.

The continuous emergence of SARS-CoV-2 variants has led to a protracted global COVID-19 pandemic with significant impacts on public health and global economy. While there are currently available SARS-CoV-2 vaccines and therapeutics, most of the FDA-approved antiviral agents directly target viral proteins. However, inflammation is the initial immune pathogenesis induced by SARS-CoV-2 infection, there is still a need to find additional agents that can control the virus in the early stages of infection to alleviate disease progression for the next pandemic.

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning.

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues.

Subdomain dynamics enable chemical chain reactions in non-ribosomal peptide synthetases.

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states; yet how these molecular machines orchestrate such biochemical sequences has remained elusive.

The Antiviral Activity of Varenicline against Dengue Virus Replication during the Post-Entry Stage.

Dengue virus (DENV) poses a significant global health challenge, with millions of cases each year. Developing effective antiviral drugs against DENV remains a major hurdle. Varenicline is a medication used to aid smoking cessation, with anti-inflammatory and antioxidant effects.

Molecular insight into the specific enzymatic properties of TREX1 revealing the diverse functions in processing RNA and DNA/RNA hybrids.

In various autoimmune diseases, dysfunctional TREX1 (Three prime Repair Exonuclease 1) leads to accumulation of endogenous single-stranded DNA (ssDNA), double-stranded DNA (dsDNA) and DNA/RNA hybrids in the cytoplasm and triggers immune activation through the cGAS-STING pathway. Although inhibition of TREX1 could be a useful strategy for cancer immunotherapy, profiling cellular functions in terms of its potential substrates is a key step. Particularly important is the functionality of processing DNA/RNA hybrids and RNA substrates.

Mechanical codes of chemical-scale specificity in DNA motifs.

In gene transcription, certain sequences of double-stranded (ds)DNA play a vital role in nucleosome positioning and expression initiation. That dsDNA is deformed to various extents in these processes leads us to ask: Could the genomic DNA also have sequence specificity in its chemical-scale mechanical properties? We approach this question using statistical machine learning to determine the rigidity between DNA chemical moieties. What emerges for the polyA, polyG, TpA, and CpG sequences studied here is a unique trigram that contains the quantitative mechanical strengths between bases and along the backbone.

A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions.

A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology.

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria.