Biology subjects

Moran-Gilad, J.

Publications and source records attributed to Moran-Gilad, J..

7 recordsLinked to original sources

bioRxiv2026

A synthetic MALDI-TOF MS atlas for microbial classification across hospitals

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (MS) is central to routine clinical microbiology for species identification. Its combination with machine learning is constrained by fragmented public datasets, species imbalance and acquisition-related variability. Synthetic datasets could provide more training data, but they must preserve the spectral information needed to make predictions on real samples. Here, we present an open, synthetic MALDI-TOF MS atlas comprising 4.3 million spectra across 43 clinically relevant bacterial species. To construct the atlas, we harmonised public repositories from three countries and trained a species- and institution-conditioned concept-bottleneck generative variational autoencoder (CBGM-VAE) on 179,086 measured spectra. We assessed the atlas as both a stand-alone training resource and a complement to real data, using bacterial species classification as a functional benchmark. Evaluation comprised 7,269 real spectra from more than 7 countries, 24 institutions and multiple acquisition platforms, covering: temporal, technical, biological and combined shifts. A neural network trained only on synthetic spectra achieved F1 scores ranging from 0.788 to 1.000 across external cohorts. When synthetic spectra were combined with real training data, classification performance improved or matched using real data only. These findings demonstrate that the atlas retains transferable information and can complement measured data. The openly released atlas provides a reusable resource for supervised learning and benchmarking. It can also be explored for pretraining representation-learning and foundation models in MALDI-TOF MS research.

microbiology↗

bioRxiv2026

Machine learning-guided discovery of a conserved plasmid proteomic signature enables MALDI-TOF MS detection of pOXA-48-carrying Enterobacterales

OXA-48 carbapenemases are among the most widespread and important resistance mechanisms in Enterobacterales. Yet detecting carbapenemases by conventional workflows necessitates additional testing, thus delaying optimization of therapy and implementation of infection control measures. Here, we present a machine learning approach that identifies the conserved pOXA-48 plasmid directly from routine MALDI-TOF spectra acquired for species identification. The model detects pOXA-48 carriers with an AUROC of 0.96-0.98 across two independent hospital cohorts and instrument platforms, indicating near-perfect discrimination. Using bottom-up proteomics, plasmid conjugation, and plasmid curing, we link the discriminative MALDI-TOF spectral features to proteins encoded on pOXA-48, with DUF1496 domain-containing protein producing the most discriminative spectral feature. Our approach reframes the resistance prediction task from inferring a resistance phenotype to detecting a conserved plasmid through its expressed proteomic signature and has the potential to enable rapid MALDI-TOF MS-based diagnostics for a wide range of plasmid-based resistance determinants.

microbiology↗

bioRxiv2023

TRAF3 Suppression Encourages B Cell Recruitment and Prolongs Survival of Microbiome-Intact Mice with Ovarian Cancer

BackgroundOvarian cancer (OC) has proven to be the most deadly of all gynecologic cancers. Clinical trials involving the use of immunotherapies in OC patients have produced disappointing outcomes, underscoring the necessity of identifying new immunomodulatory targets for the treatment of this cancer. MethodsWe conducted an in-vivo CRISPR screen of immunodeficient (NSG) and immune-intact wild type (WT) C57/BL6 mice to identify tumor-derived immune-escape mechanisms in a BRAC1- and TP53-deficient murine ID8 OC cell line (designated ITB1). To confirm gene expression and signaling pathway activation in ITB1 cells, we employed western blot, qPCR, immunofluorescent staining, and flow cytometry. Flow cytometry was also used to identify immune cell populations in the peritoneum of ITB1-bearing mice. To determine the presence of IgA-coated bacteria in the peritoneum of ITB1 -bearing mice and the ascites of OC patients, we employed 16S sequencing. Testing for differences was done by using Deseq2 test and two-way ANOVA test. Sequence variants (ASVs) were produced in Qiime2 and analyzed by microeco and phyloseq R packages. ResultsWe identified tumor necrosis factor receptor-associated factor 3 (TRAF3) as a tumor-derived immune suppressive mediator in ITB1 cells. Knockout of TRAF3 (TRAF3KO) activated the type-I interferon pathway and increased MHC-I expression. TRAF3KO tumors exhibited a growth delay in WT mice vs. NSG mice, which was correlated with increased B cell infiltration and activation compared to ITB1 tumors. B cells were found to be involved in the progression of TRAF3KO tumors, and B-cell surface-bound and secreted IgA levels were significantly higher in the ascites of TRAF3KO tumors compared to ITB1. The presence of commensal microbiota was necessary for B-cell activation and for delaying the progression of TRAF3KO tumors in WT mice. Lastly, we observed unique profiles of IgA-coated bacteria in the ascites of OC-bearing mice or the ascites of OC patients. ConclusionsTRAF3 is a tumor-derived immune-suppressive modulator that influences B-cell infiltration and activation, making it a potential target for enhancing anti-tumor B-cell responses in OC.

cancer biology↗

bioRxiv2022

Bacterial communities of premise plumbing systems in four European cities, and their association with culturable Legionella

Legionella species are Gram negative, facultative, intracellular bacteria found in natural and engineered water systems. Understanding the bacterial interactions underlying the success of Legionella in aquatic environments could be beneficial for control. We aimed to profile, by 16S rRNA amplicon sequencing, the bacterial communities in premise plumbing systems of buildings in four European cities (Copenhagen, Warsaw, Rome, Athens), and identify positive and negative associations of specific community members to culturable Legionella. The coarse taxonomic composition was similar across the four cities, but Copenhagen and Warsaw had richer, more diverse communities than Athens and Rome, with a greater number of city-specific amplicon sequence variants (ASVs). The cities had statistically significant differences in bacterial communities at the ASV level, with relatively few shared ASVs. Out of 5,128 ASVs, 73 were classified as Legionella, and one or more of these were detected in most samples from each city (88.1% overall). Interestingly, the relative abundance of Legionella ASVs did not correlate with Legionella culture status. Overall, 44.2% of samples were Legionella culture positive: 71.4% in Warsaw, 62.2% in Athens, 22.2% in Rome, and 15.2% in Copenhagen. 54 specific ASVs and 42 genera had significant positive or negative associations with culturable Legionella. Negative associations included Staphylococcus, Pseudomonas, and Acinetobacter. Positive associations included several Nitrospira ASVs and one classified as Nitrosomodaceae oc32, ASVs in the amoeba-associated genera Craurococcus-Caldovatus and Reyranella, and the predatory genus Bdellovibrio. Some of these associations are well supported by laboratory studies, but others are the opposite of what was expected. This highlights the difficulties in translating pure culture results to into complex real-life scenarios. However, these positive and negative associations held across the four cities, across multiple buildings and plumbing compartments. This is important because developing better control measures, including probiotic approaches, will require an understanding of ecological relationships that can be generalised across different engineered water systems. ImportanceThis study provides a snapshot of the diversity of microbial communities among premise plumbing systems in four European cities, providing new information on bacterial ASVs and genera that have positive or negative associations with culturable Legionella across a broad geographical and climatic range. This could inform studies aimed at confirming both in vitro and real-life scenarios around the role of other microbial community members in modulating Legionella proliferation. It could also help in the development of probiotic approaches to controlling this opportunistic pathogen.

microbiology↗

bioRxiv2020

Scientometric Trends for Coronaviruses and Other Emerging Viral Infections

COVID-19 is the most rapidly expanding coronavirus outbreak in the past two decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential. Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing over 35 million papers from the last 20 years. Our results demonstrate that previous coronavirus outbreaks have been understudied compared to other viruses. We also show that the research volume of emerging infectious diseases is very high after an outbreak and drops drastically upon the containment of the disease. This can yield inadequate research and limited investment in gaining a full understanding of novel coronavirus management and prevention. Independent of the outcome of the current COVID-19 outbreak, we believe that measures should be taken to encourage sustained research in the field.

microbiology↗

bioRxiv2019

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

BackgroundAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a one-stop test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results.\n\nMethodsWe produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ( participants) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.\n\nResultsIndividual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant.\n\nConclusionsWe found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

microbiology↗

bioRxiv2017

chewBBACA: A complete suite for gene-by-gene schema creation and strain identification

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. The software can run in a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA or as a docker image at https://hub.docker.com/r/ummidock/chewbbaca/.\n\nDATA SUMMARYO_LIAssembled genomes used for the tutorial were downloaded from NCBI in August 2016 by selecting those submitted as Streptococcus agalactiae taxon or sub-taxa. All the assemblies have been deposited as a zip file in FigShare (https://figshare.com/s/9cbe1d422805db54cd52), where a file with the original ftp link for each NCBI directory is also available.\nC_LIO_LICode for the chewBBACA suite is available at https://github.com/B-UMMI/chewBBACA while the tutorial example is found at https://github.com/B-UMMI/chewBBACA_tutorial.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTThe chewBBACA software offers a computational solution for the creation, evaluation and use of whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas. It allows researchers to develop wg/cgMLST schemes for any bacterial species from a set of genomes of interest. The alleles identified by chewBBACA correspond to potential coding sequences, possibly offering insights into the correspondence between the genetic variability identified and phenotypic variability. The software performs allele calling in a matter of seconds to minutes per strain in a laptop but is easily scalable for the analysis of large datasets of hundreds of thousands of strains using multiprocessing options. The chewBBACA software thus provides an efficient and freely available open source solution for gene-by-gene methods. Moreover, the ability to perform these tasks locally is desirable when the submission of raw data to a central repository or web services is hindered by data protection policies or ethical or legal concerns.

bioinformatics↗