Biomedical subjects

Thomas R Ioerger

Publications and source records attributed to Thomas R Ioerger.

13 recordsLinked to original sources

PubMed2026

Lineage-specific transmission and spatial clustering of Mycobacterium tuberculosis in Kaohsiung, Taiwan, in 2019-23: a population-based genomic study.

BACKGROUND: The epidemiology of tuberculosis in Taiwan has been influenced by the introduction of multiple Mycobacterium tuberculosis lineages and by the ageing of the population. We conducted a population-based study to investigate M tuberculosis transmission in Kaohsiung, a city in southern Taiwan. METHODS: In this study, we performed whole-genome sequencing (WGS) of M tuberculosis isolates from all culture-positive cases of tuberculosis notified in Kaohsiung between Jan 1, 2019 and Dec 31, 2023. We obtained routine epidemiological data for each case collected through the national tuberculosis control programme. We characterised the lineage composition of the isolate collection and evaluated genomic clustering of isolates, defined as a difference of 12 or fewer single-nucleotide polymorphisms. Univariable and multivariable logistic regression analyses were performed to estimate the odds of a case belonging to a genomic cluster based on host factors (age, sex, sputum smear status, and residential region) and pathogen factors (drug resistance status and strain lineage). Spatial aggregation of large genomic clusters (including greater than or equal to ten isolates) was assessed using a non-parametric statistical clustering method. We used a Bayesian transmission tree inference method to explore the patterns of age-dependent transmission. FINDINGS: During the study period, 5667 tuberculosis cases were notified in Kaohsiung, 4916 (86·7%) of which were culture-positive. Of these 4916 cases, whole-genome sequencing was successfully performed for 4168 (84·8%) isolates. 1219 (29·2%) of 4168 individuals were female and 2947 (70·7%) were male; the median age was 69·7 years (IQR 57·4-80·7). The dominant lineages were lineage 1 (1749 [42·0%] of 4168 isolates), lineage 2 (1510 [36·2%]), and lineage 4 (905 [21·7%]). 1069 (25·6%) of 4168 were genomically linked and formed 287 clusters. Lineage 2 isolates had higher odds (aOR 2·15 [95% CI 1·80-2·52]) than lineage 1 isolates of genomic clustering across all regions, whereas lineage 4 isolates had a significantly higher risk (2·75 [1·16-6·89]) of genomic clustering than lineage 1 only in the rural northeast region, inhabited primarily by indigenous populations. Spatial clustering analysis corroborated these lineage-region interactions. Although younger adults (<35 years) had the highest individual-level odds (5·64 [4·16-7·68]) of clustering in the logistic regression analysis compared with those aged 80 years or older, the transmission inference indicated that individuals aged 55-74 years were responsible for a greater proportion of inferred transmission events, contributing 50·8% of all transmission events. INTERPRETATION: This sequencing study revealed that older adults (aged ≥65 years) might have played a substantial and under-recognised role in the transmission of tuberculosis in Taiwan. The lineage-specific clustering and spatial patterns suggested that both pathogen characteristics and host demographics shaped tuberculosis transmission dynamics. These findings support the use of integrated genomic surveillance to guide precision tuberculosis control and motivate further research on age-specific transmission pathways and targeted interventions to advance tuberculosis elimination efforts. FUNDING: Taiwan National Health Research Institutes and Taiwan National Science and Technology Council.

Mycobacterium tuberculosis↗

PubMed2025

Evaluating selection at intermediate scales within genes provides robust identification of genes under positive selection in M. tuberculosis clinical isolates.

Multiple studies have reported genes in the M. tuberculosis (Mtb) genome that are under diversifying selection, based on genetic variants among Mtb clinical isolates. These might reflect adaptions to selection pressures associated with modern clinical treatment of TB. Many, but not all, of these genes under selection are related to drug resistance. Most of these studies have evaluated selection at the gene-level. However, positive selection can be evaluated on different scales, including individual sites (codons) and local regions within an ORF. In this paper, we use GenomegaMap, a Bayesian method for calculating selection, to evaluate selection of genes in the Mtb genome at all three levels. We present evidence that the intermediate analysis (windows of codons) yields the most credible list of candidate genes under selection (excluding PPE and PE_PGRS genes, which are predicted less reliably due to frequent sequencing errors). A further advantage of this approach is that it identifies specific regions within proteins that are under selective pressure, which is useful for structural and functional interpretation. In an analysis of two separate collections of Mtb clinical isolates (from Moldova; and a globally-representative set), we observed 53 and 173 significant genes under selection, with 36% overlap. The lists of genes under selection include many drug-resistance genes, as well as other genes that have previously been reported to be under selection (resR, phoR). The specific regions under selection identified within drug-resistance genes are shown to correspond to protein structural features known to be involved in resistance, supporting accuracy of the method. Positive selection in several ESX-1-related genes was also observed, suggesting adaptation to immune pressure.

adaptation↗

PubMed2025

The Mycobacterium tuberculosis Transposon Sequencing Database (MtbTnDB): A Large-Scale Guide to Genetic Conditional Essentiality.

Characterizing genetic essentiality across various conditions is fundamental for understanding gene function. Transposon sequencing (TnSeq) is a powerful technique to generate genome-wide essentiality profiles in bacteria and has been extensively applied to Mycobacterium tuberculosis (Mtb). Dozens of TnSeq screens have yielded valuable insights into the biology of Mtb in vitro, inside macrophages, and in model host organisms. Despite their value, these Mtb TnSeq profiles have not been standardized or collated into a single, easily searchable database. This results in significant challenges when attempting to query and compare these resources, limiting our ability to obtain a comprehensive and consistent understanding of genetic conditional essentiality in Mtb. We address this problem by building a central repository of publicly available Mtb TnSeq screens, the Mtb transposon sequencing database (MtbTnDB). The MtbTnDB is a living resource that encompasses to date ≈150 standardized TnSeq screens, enabling open access to data, visualizations, and functional predictions through an interactive web app (www.mtbtndb.app). We conduct several statistical analyses on the complete database, such as demonstrating that (i) genes in the same genomic neighborhood have similar TnSeq profiles, and (ii) clusters of genes with similar TnSeq profiles are enriched for genes from similar functional categories. We further analyze the performance of machine learning models trained on TnSeq profiles to predict the functional annotation of orphan genes in Mtb. By facilitating the comparison of TnSeq screens across conditions, the MtbTnDB will accelerate the exploration of conditional genetic essentiality, provide insights into the functional organization of Mtb genes, and help predict gene function in this important human pathogen.

DNA Transposable Elements↗

PubMed2020

Natural brominated phenoxyphenols kill persistent and biofilm-incorporated cells of MRSA and other pathogenic bacteria.

Due to a high unresponsiveness to chemotherapy, biofilm formation is an important medical problem that frequently occurs during infection with many bacterial pathogens. In this study, the marine sponge-derived natural compounds 4,6-dibromo-2-(2',4'-dibromophenoxy)phenol and 3,4,6-tribromo-2-(2',4'-dibromophenoxy)phenol were found to exhibit broad antibacterial activity against medically relevant gram-positive and gram-negative pathogens. The compounds were not only bactericidal against both replicating and stationary phase-persistent planktonic cells of methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa; they also killed biofilm-incorporated cells of both species while not affecting biofilm structural integrity. Moreover, these compounds were active against carbapenemase-producing Enterobacter sp. This simultaneous activity of compounds against different growth forms of both gram-positive and gram-negative bacteria is rare. Genome sequencing of spontaneous resistant mutants and proteome analysis suggest that resistance is mediated by downregulation of the bacterial EIIBC phosphotransferase components scrA and mtlA in MRSA likely leading to a lower uptake of the molecules. Due to their only moderate cytotoxicity against human cell lines, phenoxyphenols provide an interesting new scaffold for development of antimicrobial agents with activity against planktonic cells, persisters and biofilm-incoporated cells of ESKAPE pathogens. KEY POINTS: • Brominated phenoxyphenols kill actively replicating and biofilm-incorporated bacteria. • Phosphotransferase systems mediate uptake of brominated phenoxyphenols. • Downregulation of phosphotransferase systems mediate resistance.

Animals↗

PubMed2006

Improving amino-acid identification, fit and C(alpha) prediction using the Simplex method in automated model building.

Automated methods for protein model building in X-ray crystallography typically use a two-phased approach that involves first modeling the protein backbone followed by building in the side chains. The latter phase requires the identification of the amino-acid side-chain type as well as fitting of the side-chain model into the observed electron density. While mistakes in identification of individual side chains are common for a number of reasons, sequence alignment can sometimes be used to correct errors by mapping fragments into the true (expected) amino-acid sequence and exploiting contiguity constraints among neighbors. However, side chains cannot always be confidently aligned; this depends on having sufficient accuracy in the initial calls. The recognition of amino-acid side-chains based on the surrounding pattern of electron density, whether by features, density correlation or free atoms, can be sensitive to inaccuracies in the coordinates of the predicted backbone C(alpha) atoms to which they are anchored. By incorporating a Nelder-Mead Simplex search into the side-chain identification and model-building routines of TEXTAL, it is demonstrated that this form of residue-by-residue rigid-body real-space refinement (in which the C(alpha) itself is allowed to shift) can improve the initial accuracy of side-chain selection by over 25% on average (from 25% average identity to 32% on a test set of five representative proteins, without corrections by sequence alignment). This improvement in amino-acid selection accuracy in TEXTAL is often sufficient to bring the pairwise amino-acid identity of chains in the model out of the so-called ;twilight zone' for sequence-alignment methods. When coupled with sequence alignment, use of the Simplex search yielded improvements in side-chain accuracy on average by over 13 percentage points (from 64 to 77%) and up to 38 percentage points (from 40 to 78%) in one case compared with using sequence alignment alone.

Amino Acid Sequence↗

PubMed2005

Determining relevant features to recognize electron density patterns in x-ray protein crystallography.

High-throughput computational methods in X-ray protein crystallography are indispensable to meet the goals of structural genomics. In particular, automated interpretation of electron density maps, especially those at mediocre resolution, can significantly speed up the protein structure determination process. TEXTAL(TM) is a software application that uses pattern recognition, case-based reasoning and nearest neighbor learning to produce reasonably refined molecular models, even with average quality data. In this work, we discuss a key issue to enable fast and accurate interpretation of typically noisy electron density data: what features should be used to characterize the density patterns, and how relevant are they? We discuss the challenges of constructing features in this domain, and describe SLIDER, an algorithm to determine the weights of these features. SLIDER searches a space of weights using ranking of matching patterns (relative to mismatching ones) as its evaluation function. Exhaustive search being intractable, SLIDER adopts a greedy approach that judiciously restricts the search space only to weight values that cause the ranking of good matches to change. We show that SLIDER contributes significantly in finding the similarity between density patterns, and discuss the sensitivity of feature relevance to the underlying similarity metric.

Absorptiometry, Photon↗

PubMed2004

Crystal structure of circadian clock protein KaiA from Synechococcus elongatus.

The circadian clock found in Synechococcus elongatus, the most ancient circadian clock, is regulated by the interaction of three proteins, KaiA, KaiB, and KaiC. While the precise function of these proteins remains unclear, KaiA has been shown to be a positive regulator of the expression of KaiB and KaiC. The 2.0-A structure of KaiA of S. elongatus reported here shows that the protein is composed of two independently folded domains connected by a linker. The NH(2)-terminal pseudo-receiver domain has a similar fold with that of bacterial response regulators, whereas the COOH-terminal four-helix bundle domain is novel and forms the interface of the 2-fold-related homodimer. The COOH-terminal four-helix bundle domain has been shown to contain the KaiC binding site. The structure suggests that the KaiB binding site is covered in the dimer interface of the KaiA "closed" conformation, observed in the crystal structure, which suggests an allosteric regulation mechanism.

Allosteric Site↗

PubMed2004

Weighting features to recognize 3D patterns of electron density in X-ray protein crystallography.

Feature selection and weighting are central problems in pattern recognition and instance-based learning. In this work, we discuss the challenges of constructing and weighting features to recognize 3D patterns of electron density to determine protein structures. We present SLIDER, a feature-weighting algorithm that adjusts weights iteratively such that patterns that match query instances are better ranked than mismatching ones. Moreover, SLIDER makes judicious choices of weight values to be considered in each iteration, by examining specific weights at which matching and mismatching patterns switch as nearest neighbors to query instances. This approach reduces the space of weight vectors to be searched. We make the following two main observations: (1) SLIDER efficiently generates weights that contribute significantly in the retrieval of matching electron density patterns; (2) the optimum weight vector is sensitive to the distance metric i.e. feature relevance can be, to a certain extent, sensitive to the underlying metric used to compare patterns.

Absorptiometry, Photon↗

PubMed2003

Recent developments in the PHENIX software for automated crystallographic structure determination.

A new software system called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is being developed for the automation of crystallographic structure solution. This will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model, and facilitate structure solution for both the novice and expert crystallographer. Here, the features of PHENIXare reviewed and the recent advances in infrastructure and algorithms are briefly described.

Algorithms↗

PubMed2003

TEXTAL system: artificial intelligence techniques for automated protein model building.

Explore the source record for details and available documents.

Bacterial Proteins↗

PubMed2003

MOPAC: motif finding by preprocessing and agglomerative clustering from microarrays.

We propose a novel strategy for discovering motifs from gene expression data. The gene expression data in our experiments comes from DNA Microarray analysis of the bacterium E. coli in response to recovery from nutrient starvation. We have annotated the data and identified the upregulated genes. Our interest is to find common regulatory motifs that are responsible for the upregulation of these specific genes. We assume that a common motif that a regulatory protein can bind to will be present in the upstream region of the upregulated genes and will not be present in the upstream regions of genes that showed a constant level of expression over time. Our objective is to find the common motifs that are present in at least some of the upstream sequences of upregulated genes and not present in the control set, which is the set of genes whose expression remained the same. Because it is possible that there could be several subsets of co-regulated genes under different control mechanisms among the co-expressed genes, we do not want to require motifs to be present in all upregulated sequences. Therefore, we propose a new algorithm for finding such motifs through stages of pre-processing, denoising, agglomerative clustering and consensus checking. Through this process, we have found some motifs that are good candidates for further validation.

Algorithms↗

PubMed2002

Automatic modeling of protein backbones in electron-density maps via prediction of Calpha coordinates.

Most crystallographers today solve protein structures by first building as much of the protein backbone as possible and then modeling the side chains. Automating the determination of backbone coordinates by computer-based interpretation of the electron density would enhance the speed and possibly improve the accuracy of the structure-solution process. In this paper, a new computational procedure called CAPRA is described that predicts coordinates of Calpha atoms in density maps and outputs chains of Calpha atoms representing the backbone of the protein. The result constitutes a significant step beyond tracing the density, because there is ideally a one-to-one correspondence between atoms predicted in the chains output by CAPRA and Calpha atoms in the true structure (refined model). CAPRA is based on pattern-recognition techniques, including extraction of rotation-invariant numeric features to represent patterns in the density and use of a neural network to predict which pseudo-atoms in the trace are closest to true Calpha atoms. Experiments with several MAD and MIR electron-density maps of 2.4-2.8 A resolution reveal that CAPRA is capable of building approximately 90% of the backbone of a protein molecule, with an r.m.s. error for Calpha coordinates of around 0.9 A.

Automation↗

PubMed2002

PHENIX: building new software for automated crystallographic structure determination.

Structural genomics seeks to expand rapidly the number of protein structures in order to extract the maximum amount of information from genomic sequence databases. The advent of several large-scale projects worldwide leads to many new challenges in the field of crystallographic macromolecular structure determination. A novel software package called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is therefore being developed. This new software will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model and to facilitate structure solution for both the novice and expert crystallographer.

Algorithms↗