Biology subjects

Pupko, T.

Publications and source records attributed to Pupko, T..

18 recordsLinked to original sources

bioRxiv2026

Phylogenetic tree inference using generative models

Accurate inference of phylogenetic trees is fundamental to evolutionary biology, yet existing methods rely on complex pipelines involving multiple sequence alignment, explicit evolutionary models, and computationally intensive tree search procedures. Here, we present BetaInfer, a generative framework that reformulates phylogenetic tree inference as a sequence transduction problem. BetaInfer leverages hybrid transformer-based architectures to directly map sets of unaligned sequences to phylogenetic trees represented in Newick format. Trained on large-scale simulated evolutionary data with known ground truth, BetaInfer learns to capture complex evolutionary signals directly from sequence data. Ensemble-based generation of multiple candidate trees further improves robustness, reducing reconstruction error by over 30% relative to single predictions. Across extensive evaluations on both simulated and empirical datasets, BetaInfer achieves competitive performance relative to state-of-the-art phylogenetic pipelines, matching, and in some cases exceeding, the accuracy of established likelihood-based and distance-based methods under a wide range of conditions. Interpretability analyses reveal that BetaInfer leverages internal pairwise-distance computations to synthesize evolutionary relationships into an integrated, global representation that supports direct tree generation. Together, these results demonstrate that generative models can serve as a viable and scalable alternative to standard phylogenetic pipelines.

bioinformatics↗

bioRxiv2026

Identification of protein secretion systems and type III effectors in wood-associated bacteria of the genus Xylophilus

To shed light into the biology of bacteria belonging to the genus Xylophilus, including the grapevine pathogen Xylophilus ampelinus, we scrutinized all available genomes of the genus for the presence of type III secretion and flagellar systems. We found three different flagellar systems in the genus, one of which was present in all twelve strains with good-quality genome sequences were available. The other two flagellar systems were only detected in one or two strains. We also identified two types of type III secretion systems, likely under control of the AraC-type transcriptional activator HrpX. One system with resemblance to systems from plant-pathogenic bacteria was only found in the grapevine pathogen. The other system was found in three strains of Xylophilus, all isolated from plant material. We predicted genes that are co-regulated with the type III secretion systems, as supported by the presence of strongly conserved HrpX-binding promoter elements. We identified about 40 type III effectors in the grapevine pathogen with homologs in plant pathogenic bacteria. In contrast, a rhododendron flower isolate had only two type III effector gene candidates with conserved HrpX-binding promoter elements but many genes without homologs beyond the species. Finally, we predicted and confirmed three novel effector candidates from X. ampelinus to contain a functional type III secretion signal using an AvrBs1 reporter approach. The presence of type III effectors suggests that effector-triggered immunity may exist in grapevine or non-host plants and that strategies targeting type III effectors for resistance engineering may contribute to suitable control measures.

microbiology↗

bioRxiv2026

A deep-learning-based score to evaluate multiple sequence alignments

Multiple sequence alignment (MSA) inference is a central task in molecular evolution and comparative genomics, and the reliability of downstream analyses, including phylogenetic inference, depends critically on alignment quality. Despite this importance, most widely used MSA methods optimize the sum-of-pairs (SP) score, and relatively little attention has been paid to whether this objective function accurately reflects alignment accuracy. Here, we evaluate the performance of the SP score using simulated and empirical benchmark alignments. For each dataset, we compare alternative MSAs derived from the same unaligned sequences and quantify the relationship between their SP scores and their distances from a reference alignment. We show that the alignment with the optimal SP score often does not correspond to the most accurate alignment. To address this limitation, we develop deep-learning-based scoring functions that integrate a collection of MSA features. We first introduce Model 1, a regression model that predicts the distance of a given MSA from the reference alignment. Across simulated and empirical datasets, this learned score correlates more strongly with true alignment accuracy than the SP score. However, Model 1 is less effective at identifying the best alignment among alternatives. We therefore develop Model 2, which takes as input a set of alternative MSAs generated from the same sequences and predicts their relative ranking. Model 2 more accurately identifies the top-ranking MSA than the SP score, Model 1, and several widely used alignment programs. Using simulations, we show that selecting MSAs based on our approach leads to more accurate phylogenetic reconstructions.

bioinformatics↗

bioRxiv2026

Ancestral sequence reconstruction using generative models

Ancestral sequence reconstruction (ASR) is a foundational task in evolutionary biology, providing insights into the molecular past and guiding studies of protein function and adaptation. Conventional ASR methods rely on a multiple sequence alignment (MSA), a phylogenetic tree, and an evolutionary model. However, the underlying alignments and trees are often uncertain, and existing models typically focus on substitutions and do not explicitly account for insertion-deletion (indel) processes. Here, we introduce BetaReconstruct, a novel generative approach to ASR that harnesses recent advances in natural language processing (NLP) and hybrid transformer architectures. Our model was initially trained on large-scale simulated datasets with gold-standard ancestral sequences and subsequently on real-world protein sequences. The reconstruction requires neither MSAs nor phylogenetic trees. We demonstrate that BetaReconstruct generalizes robustly across diverse evolutionary scenarios and reconstructs ancestral sequences more accurately than maximum-likelihood-based pipelines. We additionally provide evidence that the generative-model ASR approach is also more accurate when analyzing empirical datasets. This work provides a scalable, alignment-free strategy for ASR and highlights the ability of data-driven models to capture evolutionary signals beyond the reach of traditional methods.

bioinformatics↗

bioRxiv2025

The role of plant polyploidy in the structure of plant-pollinator communities

Polyploidization is a major macromutation, bearing notable genomic and ecological consequences. While the impact of polyploidy on plant abiotic niches is well studied, our understanding of its consequences on biotic interactions, and particularly pollination, is lacking and hardly considers its role in shaping community structure. Here, we integrate hundreds of plant-pollinator networks, ploidy inferences, reproductive traits, and climatic attributes to ascertain whether a general pattern characterizes the link between polyploid frequency and community structure. We further examine whether environmental factors and plant traits known to be associated with polyploidy mediate this relationship. Our analysis reveals that an increased frequency of polyploid species is positively associated with network nestedness while being negatively associated modularity. Structural equation modeling reveals that these associations are partially mediated via the frequency of self-compatible plants and to a lesser extent by differences in flower shape. Despite these alterations in community structure, the heightened abundance of polyploids appears to have minimal impact on network connectance and resilience to extinction. Our findings imply that unlike abiotic interactions, the impact of polyploidy on biotic interactions is less predictable, and is affected synergistically by both phenotypic and environmental factors. Collectively, however, polyploidy still exerts an influence on community structure.

evolutionary biology↗

bioRxiv2024

Protein2Text: Providing Rich Descriptions for Protein Sequences

Understanding the functionality of proteins has been a focal point of biological research due to their critical roles in various biological processes. Unraveling protein functions is essential for advancements in medicine, agriculture, and biotechnology, enabling the development of targeted therapies, engineered crops, and novel biomaterials. However, this endeavor is challenging due to the complex nature of proteins, requiring sophisticated experimental designs and extended timelines to uncover their specific functions. Public large language models (LLMs), though proficient in natural language processing, struggle with biological sequences due to the unique and intricate nature of biochemical data. These models often fail to accurately interpret and predict the functional and structural properties of proteins, limiting their utility in bioinformatics. To address this gap, we introduce BetaDescribe, a collection of models designed to generate detailed and rich textual descriptions of proteins, encompassing properties such as function, catalytic activity, involvement in specific metabolic pathways, subcellular localizations, and the presence of particular domains. The trained BetaDescribe model receives protein sequences as input and outputs a textual description of these properties. BetaDescribes starting point was the LLAMA2 model, which was trained on trillions of tokens. Next, we trained our model on datasets containing both biological and English text, allowing biological knowledge to be incorporated. We demonstrate the utility of BetaDescribe by providing descriptions for proteins that share little to no sequence similarity to proteins with functional descriptions in public datasets. We also show that BetaDescribe can be harnessed to conduct in-silico mutagenesis procedures to identify regions important for protein functionality without needing homologous sequences for the inference. Altogether, BetaDescribe offers a powerful tool to explore protein functionality, augmenting existing approaches such as annotation transfer based on sequence or structure similarity.

bioinformatics↗

bioRxiv2024

Deciphering the mitochondrial genome of Oikopleura dioica (Chordata: Tunicata: Appendicularia)

Sequencing the mitochondrial genome of the tunicate Oikopleura dioica is a challenging task due to the presence of long poly-A/T homopolymer stretches, which impair sequencing and assembly. Here, we report on the sequencing and annotation of the majority of the mitochondrial genome of O. dioica by means of combining several DNA and amplicon reads obtained by Illumina and MinIon Oxford Nanopore Technologies (ONT) with public RNA sequences. We document extensive RNA editing, since all homopolymer stretches present in the mitochondrial DNA correspond to 6U-regions in the mitochondrial RNA. Out of the 13 canonical protein-coding genes, we were able to detect eight, plus an unassigned ORF that lacked sequence similarity to canonical mitochondrial protein-coding genes. We show that the nad3 gene has been transferred to the nucleus and acquired a mitochondria-targeting signal. In addition to two very short rRNAs, we could only identify a single tRNA (tRNA-Met), suggesting multiple losses of tRNA genes, supported by a corresponding loss of mitochondrial aminoacyl-tRNA synthetases in the nuclear genome. Based on the eight canonical protein-coding genes identified, we reconstructed maximum likelihood and Bayesian phylogenetic trees and inferred an extreme evolutionary rate of this mitochondrial genome. The phylogenetic position of appendicularians among tunicates, however, could not be accurately determined. SignificanceSequencing and annotating the mitochondrial genome of fast-evolving organisms is difficult because they often present unusual characteristics. The tunicate Oikopleura dioica is a model species for understanding tunicate and chordate genome evolution. However, no complete annotated mitochondrial genome for this species has been published to date. Here, we determined the major part of the mitochondrial genome of O. dioica. Our results indicate the presence of highly modified rRNA genes and the absence of all tRNAs except tRNA-Met. Moreover, we show that the mitochondrial genome undergoes editing at the RNA level. Our study demonstrates that utilizing a combination of public RNA data and DNA from long-and short-read sequencing platforms significantly improves our ability to study mitochondrial genomes with atypical characteristics.

evolutionary biology↗

bioRxiv2024

BetaAlign: a deep learning approach for multiple sequence alignment

The multiple sequence alignment (MSA) problem is a fundamental pillar in bioinformatics, comparative genomics, and phylogenetics. Here we characterize and improve BetaAlign, the first deep learning aligner, which substantially deviates from conventional algorithms of alignment computation. BetaAlign draws on natural language processing (NLP) techniques and trains transformers to map a set of unaligned biological sequences to an MSA. We show that our approach is highly accurate, comparable and sometimes better than state-of-the-art alignment tools. We characterize the performance of BetaAlign and the effect of various aspects on accuracy; for example, the size of the training data, the effect of different transformer architectures, and the effect of learning on a subspace of indel-model parameters (subspace learning). We also introduce a new technique that leads to improved performance compared to our previous approach. Our findings further uncover the potential of NLP-based approaches for sequence alignment, highlighting that AI-based methodologies can substantially challenge classic tasks in phylogenomics and bioinformatics.

bioinformatics↗

bioRxiv2023

Effect of Tokenization on Transformers for Biological Sequences

Deep learning models are transforming biological research. Many bioinformatics and comparative genomics algorithms analyze genomic data, either DNA or protein sequences. Examples include sequence alignments, phylogenetic tree inference and automatic classification of protein functions. Among these deep learning algorithms, models for processing natural languages, developed in the natural language processing (NLP) community, were recently applied to biological sequences. However, biological sequences are different than natural languages, such as English, and French, in which segmentation of the text to separate words is relatively straightforward. Moreover, biological sequences are characterized by extremely long sentences, which hamper their processing by current machine-learning models, notably the transformer architecture. In NLP, one of the first processing steps is to transform the raw text to a list of tokens. Deep-learning applications to biological sequence data mostly segment proteins and DNA to single characters. In this work, we study the effect of alternative tokenization algorithms on eight different tasks in biology, from predicting the function of proteins and their stability, through nucleotide sequence alignment, to classifying proteins to specific families. We demonstrate that applying alternative tokenization algorithms can increase accuracy and at the same time, substantially reduce the input length compared to the trivial tokenizer in which each character is a token. Furthermore, applying these tokenization algorithms allows interpreting trained models, taking into account dependencies among positions. Finally, we trained these tokenizers on a large dataset of protein sequences containing more than 400 billion amino acids, which resulted in over a three-fold decrease in the number of tokens. We then tested these tokenizers trained on large-scale data on the above specific tasks and showed that for some tasks it is highly beneficial to train database-specific tokenizers. Our study suggests that tokenizers are likely to be a critical component in future deep-network analysis of biological sequence data.

bioinformatics↗

bioRxiv2023

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an antibody can facilitate fast and precise antibody characterization and epitope prediction, with important diagnostic and clinical implications. In the current study, we evaluate the ability of AlphaFold to predict the structures of 222 recently published, non-redundant, high resolution Fab heavy and light chain structures of antibodies from different species (human, Macaca mulatta, mouse, rabbit, rat) directed against different antigens. Our analysis reveals that while the overall prediction quality of antibody chains is in line with the results available in CASP14, other antibody regions like the complementarity-determining regions (CDRs) of the heavy chain, which are prone to higher genetic variation, generate a less accurate prediction. Moreover, we discovered that AlphaFold often mis-predicts the bending angles between the variable and constant domains within a Fab. To evaluate the ability of AlphaFold to model antibody:antigen interactions based only on sequence, we used AlphaFold-multimer in combination with ZDOCK docking to predict the structures of 26 known antibody:antigen complexes. ZDOCK succeeded in predicting 11, and AlphaFold only two, out of 26 models with medium or high accuracy, with significant deviations in the docking contacts predicted in the rest of the molecules. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict antibody:antigen interactions and suggests areas for possible improvement. Key PointsO_LIAlphaFold was used to predict 222 new 3D hi-res atomic structures of Ab chains. C_LIO_LILow accuracy was observed in the prediction of HC-CDR3 and the elbow angles. C_LIO_LIPredicting Ab-Ag complexes and epitope mapping using AlphaFold-Multimer was limited. C_LI

immunology↗

bioRxiv2023

Genetic and functional diversity help explain pathogenic, weakly pathogenic, and commensal lifestyles in the genus Xanthomonas

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their co-occurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life-history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically, the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We gathered evidence for gene flow among co-occurring pathogenic and nonpathogenic strains, suggesting the potential of nonpathogenic strains to act as a reservoir of adaptive traits for pathogenic strains and vice versa. We further identified traits enriched in nonpathogens that suggest a strategy of stress tolerance, rather than avoidance, during their association with a broad range of host plants.

ecology↗

bioRxiv2023

Identification of selective sweeps in bacteria

Selective sweeps occur when a beneficial mutation spreads rapidly throughout the population due to natural selection. Searching for selective sweeps has proved to be one of the most fruitful ways to detect the footprints selection leaves on the genome. With a plethora of detection tools, the study of selective sweeps in eukaryotic systems is a well-established field of research. However, the search for fragment-specific selective sweeps among bacterial strains received little to no attention so far. In our work, we demonstrate that inter-strains locus-specific selective sweeps can be detected in bacteria. We introduce the SINCOPA algorithm, the first phylogeny-based method for soft and incomplete selective sweeps detection. We use SINCOPA to explore inter-strains locus-specific selective sweeps in a dataset containing more than 500 microbial genomes. We observe strong evidence in several loci for locus-specific selective sweeps including genes involved in biofilm formation and others that are related to coping with various unfavorable environmental conditions. SINCOPA is freely accessible as a user-friendly web server application at https://sincopa.tau.ac.il/.

bioinformatics↗

bioRxiv2022

The evolutionary dynamics that retain long neutral genomic sequences in face of indel deletion bias: a model and its application to human introns

Insertions and deletions (indels) of short DNA segments are common evolutionary events. Numerous studies showed that deletions occur more often than insertions in both prokaryotes and eukaryotes. It raises the question why neutral sequences are not eradicated from the genome. We suggest that this is due to a phenomenon we term border-induced selection. Accordingly, a neutral sequence is bordered between conserved regions. Deletions occurring near the borders occasionally protrude to the conserved region and are thereby subject to strong purifying selection. Thus, for short neutral sequences, an insertion bias is expected. Here, we develop a set of increasingly complex models of indel-dynamics that incorporate border-induced selection. Furthermore, we show that short conserved sequences within the neutrally evolving sequence help explain: (1) the presence of very long sequences; (2) the high variance of sequence lengths; (3) the possible emergence of multimodality in sequence length distributions. Finally, we fitted our models to the human intron length distribution, as introns are thought to be mostly neutral and bordered by conserved exons. We show that when accounting for the occurrence of short conserved sequences within introns, we reproduce the main features, including the presence of long introns and the multimodality of intron distribution.

bioinformatics↗

bioRxiv2022

Harnessing machine translation methods for sequence alignment

The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a novel methodology for aligning sequences using a natural language processing (NLP) approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to outstanding alignment accuracy, often outperforming commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, and MUSCLE. Notably, the utilization of deep-learning techniques for the sequence alignment problem brings additional advantages, such as automatic feature extraction that can be leveraged for a variety of downstream analysis tasks.

evolutionary biology↗

bioRxiv2020

A probabilistic model for indel evolution: differentiating insertions from deletions

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.

bioinformatics↗

bioRxiv2020

Harnessing machine learning to unravel protein degradation in Escherichia coli

Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to find actively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptors to train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-lives and have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets.

microbiology↗

bioRxiv2020

ModelTeller: model selection for optimal phylogenetic reconstruction using machine learning

Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. While model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, while these methods are dedicated to revealing the processes that underlie the sequence data, in most cases they do not produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate model for branch-length estimation accuracy. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared to existing strategies. We show that on datasets simulated under simple homogenous substitution models ModelTeller leads to branch-length estimation that is as accurate as the statistical model selection criteria. We then demonstrate that ModelTeller outperforms these criteria when more intricate patterns - that aim at mimicking realistic processes - are considered.

evolutionary biology↗

bioRxiv2019

Show me your secret(ed) weapons: a multifaceted approach reveals novel type III-secreted effectors of a plant pathogenic bacterium

Many Gram-negative plant and animal pathogenic bacteria employ a type III secretion system (T3SS) to secrete protein effectors into the cells of their hosts and promote disease. The plant pathogen Acidovorax citrulli requires a functional T3SS for pathogenicity. As with Xanthomonas and Ralstonia spp., an AraC-type transcriptional regulator, HrpX, regulates expression of genes encoding T3SS components and type III-secreted effectors (T3Es) in A. citrulli. A previous study reported eleven T3E genes in this pathogen, based on the annotation of a sequenced strain. We hypothesized that this was an underestimation. Guided by this hypothesis, we aimed at uncovering the T3E arsenal of the A. citrulli model strain, M6. We carried out a thorough sequence analysis searching for similarity to known T3Es from other bacteria. This analysis revealed 51 A. citrulli genes whose products are similar to known T3Es. Further, we combined machine learning and transcriptomics to identify novel T3Es. The machine learning approach ranked all A. citrulli M6 genes according to their propensity to encode T3Es. RNA-Seq revealed differential gene expression between wild-type M6 and a mutant defective in HrpX. Data combined from these approaches led to the identification of seven novel T3E candidates, that were further validated using a T3SS-dependent translocation assay. These T3E genes encode hypothetical proteins, do not show any similarity to known effectors from other bacteria, and seem to be restricted to plant pathogenic Acidovorax species. Transient expression in Nicotiana benthamiana revealed that two of these T3Es localize to the cell nucleus and one interacts with the endoplasmic reticulum. This study not only uncovered the arsenal of T3Es of an important pathogen, but it also places A. citrulli among the \"richest\" bacterial pathogens in terms of T3E cargo. It also revealed novel T3Es that appear to be involved in the pathoadaptive evolution of plant pathogenic Acidovorax species.\n\nAuthor summaryAcidovorax citrulli is a Gram-negative bacterium that causes bacterial fruit blotch (BFB) disease of cucurbits. This disease represents a serious threat to cucurbit crop production worldwide. Despite the agricultural importance of BFB, the knowledge about basic aspects of A. citrulli-plant interactions is rather limited. As many Gram-negative plant and animal pathogenic bacteria, A. citrulli employs a complex secretion system, named type III secretion system, to deliver protein virulence effectors into the host cells. In this work we aimed at uncovering the arsenal of type III-secreted effectors (T3Es) of this pathogen by combination of bioinformatics and experimental approaches. We found that this bacterium possesses at least 51 genes that are similar to T3E genes from other pathogenic bacteria. In addition, our study revealed seven novel T3Es that seem to occur only in A. citrulli strains and in other plant pathogenic Acidovorax species. We found that two of these T3Es localize to the plant cell nucleus while one partially interacts with the endoplasmic reticulum. Further characterization of the novel T3Es identified in this study may uncover new host targets of pathogen effectors and new mechanisms by which pathogenic bacteria manipulate their hosts.

microbiology↗