Bacterial Infection

Harnessing Interpretable Deep Learning to Predict Meropenem Resistance in Klebsiella pneumoniae

Antimicrobial resistance represents an escalating global healthcare threat, complicating treatment and increasing both morbidity and mortality. Deep learning offers promising solutions, particularly for bacterial profiling using omics data. For instance, classifying bacterial strains as resistant or susceptible to antibiotics depends on identifying genomic signatures associated with resistance mec...

Skin lipid chemistry influences host-microbiome-pathogen interactions in snake fungal disease (ophidiomycosis)

Within host-microbiome-pathogen systems, the host chemical microenvironment is often overlooked despite its inherent role in host physiology. We used a multifaceted experimental approach encompassing culture-dependent and independent methods, metagenomic and genomic data, and deep neural network modeling to assess the impact of host skin lipid chemistry and the bacterial microbiome on the growth o...

Microcompartments in archaeal ancestors of eukaryotes: a bioenergetic engine that could have fuelled eukaryogenesis

Eukaryotic intracellular compartmentalization is a key innovation in the evolution of complex cellular life. While microcompartments enable metabolic ...

Substrate-enhanced filamentation of 3-methylcrotonyl-CoA carboxylase in Legionella pneumophila

3-Methylcrotonyl-CoA carboxylase (MCC) is a biotin-dependent carboxylase that metabolizes the amino acid leucine. MCC is present in bacteria, fungi, p...

Hierarchical Machine Learning Uncovers Topological Signatures of Autophagy Regulation by Oral Bacteria in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) progression has been increasingly linked to dysbiosis of the oral microbiome. We hypothesized that pathogenic vers...

Quantitative profiling of millions of nucleotides reveals sequence-encoded interactions that govern plasmid propagation

Plasmids are central to modern biotechnology, especially therapeutic development, yet their propagation in Escherichia coli remains difficult to predi...

B-PPI: A Cross-Attention Model for Large-Scale Bacterial Protein-Protein Interaction Prediction

Protein-protein interactions (PPIs) are essential for the study of cellular function, yet computational prediction of bacterial PPIs remains limited. ...

Designing minimal E. coli genomes using variational autoencoders

Designing minimal bacterial genomes remains a key challenge in synthetic biology. There is currently a lack of efficient tools for the rapid generatio...

Fast and accurate taxonomic domain assignment of short metagenomic reads using BBERT

Metagenomes from complex environments such as soil contain vast biodiversity, yet most short reads cannot be taxonomically or functionally annotated b...

A Reinforcement Learning Approach for Modeling Organic Compound-Induced Antimicrobial Resistance Dynamics

This study investigates the use of reinforcement learning (RL) to model antimicrobial resistance (AMR) dynamics driven by copper exposure. In a simula...

Bioactivity-Driven Prediction of Antibacterial Synergy Using Machine Learning Models

Predicting antibacterial drug synergy remains difficult due to strain variability and the limited scale of experimentally tested combinations. Existin...

Pili are essential for conjugation also in many Gram-positive bacteria

Type IV secretion systems (T4SS) enable the spread of antibiotic resistance and other virulence factors. In Gram-positive bacteria, T4SSs have long be...

Evolutionary Decoding of the Bacillus subtilis Secretome: Insights from Pan-Genomics and Deep Learning

Bacillus subtilis serves as a crucial host for industrial protein production, where the efficiency and regulation of its secretion system represent a ...

Ω-Loop mutations control dynamics of the active site by modulating the hydrogen-bonding network in PDC-3 β-lactamase

The expression of antibiotic-inactivating enzymes, such as Pseudomonas-derived cephalosporinase-3 (PDC-3), is a major mechanism of intrinsic resistanc...

Genome-Level Hierarchical Attention Transformer with Multi-Head Attention Weighted Sum for Broad-Spectrum Antimicrobial Resistance Prediction and Discovery of Resistance-Related Genomic Contexts

Antimicrobial resistance is a growing global health concern, requiring reliable tools for predicting resistance across a wide range of bacteria and an...

PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery

As biology becomes increasingly data-driven, so too does the field of phage lysins, enzymes that degrade bacterial cell walls and hold promise as alte...

MARISMa: a routine MALDI-TOF MS dataset from 2018 to 2024 from Spain

Clinical microbiology laboratories play a crucial role in identifying pathogens, guiding antibiotic treatment, and managing antimicrobial resistance (...

Machine learning for estimating and comparing clinical rules for treating diarrheal illness with antibiotics

Acute diarrheal disease is one of the leading causes of death in children under age 5, disproportionately impacting children in low-resource settings....

Exploring multidrug resistance patterns in community-acquired E. coli urinary tract infections with machine learning

While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remai...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning

Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study ...