Mutant phenotypes for thousands of bacterial genes of unknown function

Engineering Pseudomonas putida for production of 3-hydroxyacids using hybrid type I polyketide synthases

Engineered type I polyketide synthases (T1PKSs) are a potentially transformative platform for the biosynthesis of small molecules. Due to their modular nature, T1PKSs can be rationally designed to produce a wide range of bulk or specialty chemicals. While heterologous PKS expression is best studied in microbes of the genus Streptomyces, recent studies have focused on the exploration of non-native PKS hosts. The biotechnological production of chemicals in fast growing and industrial relevant hosts has numerous economic and logistic advantages. With its native ability to utilize alternative feedstocks, Pseudomonas putida has emerged as a promising workhorse for the sustainable production of small molecules. Here, we outline the assessment of P. putida as a host for the expression of engineered T1PKSs and production of 3-hydroxyacids. After establishing the functional expression of an engineered T1PKS, we successfully expanded and increased the pool of available acyl-CoAs needed for the synthesis of polyketides using transposon sequencing and protein degradation tagging. This work demonstrates the potential of T1PKSs in P. putida as a production platform for the sustainable biosynthesis of unnatural polyketides.

DmdA-independent lag phase shortening in Phaeobacter inhibens bacteria under stress conditions

Bacteria can shorten their lag phase by using methyl groups from compounds like dimethylsulfoniopropionate (DMSP), which are incorporated into cellular components via the methionine cycle. However, the role of specific methionine synthases in this process is not fully understood. Using transcriptomics, genetics, and biochemical assays, we investigated methionine synthases involved in lag phase shortening in Phaeobacter inhibens. We focused on a cobalamin-dependent methionine synthase (MetH)-like complex encoded by three genes: a betaine-homocysteine S-methyltransferase (bmt), a cobalamin-binding protein (cbp), and an intermediate methyl carrier (PGA1_c16040). Expression profiling revealed transcriptional decoupling among these genes. Deleting bmt disrupted lag phase shortening in response to DMSP. Functional assays showed that Bmt can directly produce methionine from DMSP and betaine, independent of tetrahydrofolate (THF) or cobalamin. Interestingly, under stress conditions, lag phase shortening occurred even in the absence of dimethylsulfoniopropionate demethylase DmdA, the primary DMSP demethylase. Under osmotic and oxidative stress, bmt expression increased significantly in response to both DMSP and betaine, suggesting an alternative methylation route. This highlights the role of Bmt as both demethylase and a methionine synthase under stress, offering a cost-effective strategy for methyl group assimilation. Our findings reveal a novel stress-responsive pathway for methionine synthesis and demonstrate the role of Bmt in promoting bacterial adaptation by accelerating the lag phase.

High-Throughput Tn-Seq Screens Identify Both Known and Novel Pseudomonas putida KT2440 Genes Involved in Metal Tolerance

Industrial and urban activities release toxic chemical waste into the environment. Pseudomonas putida, a soil bacterium, is known to degrade hydrocarbons and xenobiotics, and possesses numerous genes associated with heavy metal tolerance. Most studies on metal tolerance in P. putida focus solely on over- or underexpressed genes, potentially overlooking important genes with unchanged expression. This study employed a Tn-seq approach to identify the essential genes required for P. putida growth under metal stress. This method enables the identification of mutants with altered fitness in the presence of excess metals. The screen successfully identified a number of known genes implicated in metal resistance, including czcA-1, cadA-3, cadR, and pcoA2, thereby validating the approach. Further analyses using targeted mutagenesis and complementation assays revealed PP_5337 as a putative transcriptional regulator involved in copper tolerance and the two-component system RoxSR (PP_0887/PP_0888) as a key determinant of cadmium tolerance. Additionally, PP_1663 and PP_5002 were identified as contributing to cadmium and cobalt tolerance, respectively. This study provides the first evidence linking these genes to metal tolerance, highlighting gaps in our understanding of metal tolerance mechanisms in P. putida and demonstrating the utility of Tn-seq for identifying novel tolerance determinants.

Enabling next-generation anaerobic cultivation through biotechnology to advance functional microbiome research

Microbiomes are complex communities of microorganisms that are essential for biochemical processes on Earth and for the health of humans, animals and plants. Many environmental and host-associated microbiomes are dominated by anaerobic microbes, some of which cannot tolerate oxygen. Anaerobic microbial communities have been extensively studied over the last 20 years using molecular techniques, especially next-generation sequencing. However, there is a renewed interest in microbial cultivation because isolates provide the basis for understanding the taxonomic and functional units of biodiversity, elucidating novel biochemical pathways and the mechanisms underlying microbe-microbe and microbe-host interactions and opening new avenues for biotechnological and clinical applications. In this Perspective, we present areas of research and applications that will benefit from advancement in anaerobic microbial cultivation. We highlight key technical and infrastructural hurdles associated with the development and deployment of sophisticated cultivation workflows. Improving the performance of cultivation techniques will set new trends in functional microbiome research in the coming years.

Alternate routes to acetate tolerance lead to varied isoprenol production from mixed carbon sources in Pseudomonas putida

Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. Although this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here, we examined the growth and isoprenol production in a Pseudomonas putida strain pre-tolerized ("PT") background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon sources. We found that PT displays impaired growth in minimal medium containing acetate and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, whereas a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled the development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol.IMPORTANCEAcetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential to be used together with sugars, especially in microbes with versatile catabolism such as P. putida. However, the use of mixed carbon streams necessitates additional optimization. Furthermore, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study, we generate acetate-tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, although independent phenotypes, can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing, we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.

A co-conserved gene pair supports Caulobacter iron homeostasis during chelation stress

Synthetic metal chelators are widely used in industrial, clinical, and agricultural settings, leading to their accumulation in the environment. We measured the growth of Caulobacter crescentus, a soil and aquatic bacterium, in the presence of the ubiquitous chelator ethylenediaminetetraacetic acid (EDTA) and found that it restricts growth by lowering intracellular iron levels. Using barcoded transposon sequencing, we identified an operonic gene pair, cciT-cciO, that is required to maintain iron homeostasis in laboratory media during EDTA challenge. cciT encodes one of four TonB-dependent transporters that are regulated by the ferric uptake repressor (Fur) and stands out among this group of genes in its ability to support Caulobacter growth across diverse media conditions. The function of CciT strictly requires cciO, which encodes a cytoplasmic FeII dioxygenase-family protein. Our results thus define a functional partnership between an outer membrane iron receptor and a cytoplasmic dioxygenase that are broadly co-conserved in Proteobacteria. We expanded our analysis to natural environments by examining the growth of mutant strains in freshwater from two lakes, each with biochemical and geochemical profiles that differ markedly from standard laboratory media. In lake water, Caulobacter growth did not require cciT or cciO and was less affected by EDTA treatment. This result aligns with our observation that EDTA toxicity is influenced by common forms of biologically chelated iron and the spectrum of free cations present in the medium. Our study defines a conserved iron acquisition system in Proteobacteria and bridges laboratory-based physiology studies with real-world conditions.IMPORTANCEMetal-chelating chemicals are widely used across industries, including as preservatives in the food sector, but their full impact on microbial physiology is not well understood. We identified two genes, cciT and cciO, that function together to support Caulobacter crescentus iron balance when cells are exposed to the common synthetic chelator, EDTA. CciT is an outer membrane transporter and CciO is a dioxygenase-family protein that are mutually conserved in many bacteria, including human pathogens where mutations in cciT homologs are linked to clinical resistance to the siderophore antibiotic cefiderocol. This study identifies a conserved genetic system that supports iron homeostasis during chelation stress and illuminates the iron acquisition versatility and stress resilience of Caulobacter in freshwater environments.

Are Bacterial Processes Dependent on Global Ribosome Pausing Affected by tRNA Modification Defects?

By integrating a literature review with transcriptomic, proteomic, and phenotypic data from two model bacteria, Escherichia coli and Vibrio cholerae, we put forward the hypothesis that defects in tRNA modification broadly impact processes that are evolutionarily tuned to be sensitive to translation speed. These include the translation of regulatory proteins associated with motility, iron homeostasis, and leader peptide-driven attenuation mechanisms. Some of these translation speed-dependent processes are influenced by the absence of a single modification, while others are affected by the absence of multiple modifications. Although further experiments are needed to clarify the mechanisms involved in each case, this work provides a foundational framework to guide future research.

Genome-scale resources in the infant gut symbiont Bifidobacterium breve reveal genetic determinants of colonization and host-microbe interactions

Bifidobacteria represent a dominant constituent of human gut microbiomes during infancy, influencing nutrition, immune development, and resistance to infection. Despite interest in bifidobacteria as a live biotic therapy, our understanding of colonization, host-microbe interactions, and the health-promoting effects of bifidobacteria is limited. To address these major knowledge gaps, we used a large-scale genetic approach to create a mutant fitness compendium in Bifidobacterium breve. First, we generated a high-density randomly barcoded transposon insertion pool and used it to determine fitness requirements during colonization of germ-free mice and chickens with multiple diets and in response to hundreds of in vitro perturbations. Second, to enable mechanistic investigation, we constructed an ordered collection of insertion strains covering 1,462 genes. We leveraged these tools to reveal community- and diet-specific requirements for colonization and to connect the production of immunomodulatory molecules to growth benefits. These resources will catalyze future investigations of this important beneficial microbe.

Hierarchical glycolytic pathways control the carbohydrate utilization regulator in human gut Bacteroides

Human dietary choices control the gut microbiome. Industrialized populations consume abundant amounts of glucose and fructose, resulting in microbe-dependent intestinal disorders. Simple sugars inhibit the carbohydrate utilization regulator (Cur), a transcription factor in members of the prominent gut bacterial phylum, Bacteroidetes. Cur controls products necessary for carbohydrate utilization, host immunomodulation, and intestinal colonization. Here, we demonstrate how simple sugars decrease Cur activity in the mammalian gut. Our findings in two Bacteroides species show that ATP-dependent fructose-1,6-bisphosphate (FBP) synthesis is necessary for glucose or fructose to inhibit Cur, but dispensable for growth because of an essential pyrophosphate (PPi)-dependent enzyme. Furthermore, we show that ATP-dependent FBP synthesis is required to regulate Cur in the gut but does not contribute to fitness when cur is absent, indicating PPi is sufficient to drive glycolysis in these bacteria. Our findings reveal how sugar-rich diets inhibit Cur, thereby disrupting Bacteroides fitness and diminishing products that are beneficial to the host.

Potential confounding mutations in Keio knockout strains: implications for research accuracy

The Keio library of single-gene knock-out mutants of Escherichia coli is useful for the research community. It has been used to analyze the role of various E. coli genes in alcohol tolerance, multi-drug resistance, and biofilm formation. The current study provides a general overview of potential single nucleotide polymorphisms (SNPs), insertion-deletion of bases (≤50 nucleotides, INDELs) in the genome of a set of 21 knock-out mutants of the Keio collection in comparison to the parent strain. A small number of SNPs and INDELs were predicted in the coding and intergenic regions of the knock-out mutants in comparison to the parental strain through sequencing and bioinformatic analysis. Mutations in the coding regions of genes (different from the actual gene knocked out in the mutants) led to different types of mutations in the affected genes, ranging from nonsense mutations to frameshift mutations, which could affect the functionality of the resulting gene products. These mutations in the intergenic and coding regions could lead to phenotypic differences in the single-gene knock-out mutant strains in comparison to the parent strain, independent of the desired gene deletion. This, in turn, could be misinterpreted by researchers using these strains as differences caused by the missing gene. While this is a preliminary study based on only 21 strains of the Keio collection, the deleted genes in the mutants used in this study were approximately evenly distributed across the entire genome. This study likely indicates the possibility of such mutations in other Keio strains, although a larger sample size of knock-out mutants would be required to understand the likelihood of such mutations across the library.IMPORTANCEThe Keio library of single-gene knock-out mutants of Escherichia coli has been widely used for a variety of studies. However, mutations might appear in the genome of these strains over time, leading to differences in the characteristics of the mutant and parent strains that are independent of the gene deletions of interest. This study predicts the presence of a few SNPs and INDELs in some of the knock-out mutants from the Keio collection, which could potentially alter the phenotypic attributes of the knock-out mutants with no role of the deleted gene towards this change. Therefore, this study highlights the possibility of the presence of such mutations in other strains of the library and the importance of conducting additional steps, such as complementation assays, to confirm the outcomes of studies comparing specific attributes of the knock-out mutants with the parental strain.

Enzyme functional classification using artificial intelligence

Enzymes are essential for cellular metabolism, and elucidating their functions is critical for advancing biochemical research. However, experimental methods are often time consuming and resource intensive. To address this, significant efforts have been directed toward applying artificial intelligence (AI) to enzyme function prediction, enabling high-throughput and scalable approaches. In this review, we discuss advances in AI-driven enzyme functional annotation, transitioning from traditional machine learning (ML) methods to state-of-the-art deep learning approaches. We highlight how deep learning enables models to automatically extract features from raw data without manual intervention, leading to enhanced performance. Finally, we discuss the discovery of novel enzyme functions and generation of de novo enzymes through the integration of generative AIs and bio big data as future research directions.

TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor

Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from enzyme structures and predict Enzyme Commission (EC) classes. Using message-passing frameworks, we include distance and angle information to significantly improve the predictive performance for EC classification (F-score: 0.72) compared to regular 2D graph neural networks. We trained networks without fold bias that can classify enzyme structures for a vast functional space (>800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. We observe that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository on GitHub: https://github.com/IBG4-CBCLab/TopEC and https://doi.org/10.25838/d5p-66 .

Context matters: assessing the impacts of genomic background and ecology on microbial biosynthetic gene cluster evolution

Encoded within many microbial genomes, biosynthetic gene clusters (BGCs) underlie the synthesis of various secondary metabolites that often mediate ecologically important functions. Several studies and bioinformatics methods developed over the past decade have advanced our understanding of both microbial pangenomes and BGC evolution. In this minireview, we first highlight challenges in broad evolutionary analysis of BGCs, including delineation of BGC boundaries and clustering of BGCs across genomes. We further summarize key findings from microbial comparative genomics studies on BGC conservation across taxa and habitats and discuss the potential fitness effects of BGCs in different settings. Afterward, recent research showing the importance of genomic context on the production of secondary metabolites and the evolution of BGCs is highlighted. These studies draw parallels to recent, broader, investigations on gene-to-gene associations within microbial pangenomes. Finally, we describe mechanisms by which microbial pangenomes and BGCs evolve, ranging from the acquisition or origination of entire BGCs to micro-evolutionary trends of individual biosynthetic genes. An outlook on how expansions in the biosynthetic capabilities of some taxa might support theories that open pangenomes are the result of adaptive evolution is also discussed. We conclude with remarks about how future work leveraging longitudinal metagenomics across diverse ecosystems is likely to significantly improve our understanding on the evolution of microbial genomes and BGCs.

The flavonoid-sensing regulator AefR is involved in modulating quorum sensing through repressing the MexEF-OprN efflux pump in Pseudomonas fluorescens

Flavonoids, a major component of plant root exudates, play a crucial role in mediating plant-microbe interactions. However, the mechanisms by which flavonoids are perceived and trigger downstream signaling events in microbes remain largely unknown. In this study, we characterized AefR, a flavonoid-sensing transcriptional regulator from Pseudomonas fluorescens 2P24, a plant growth-promoting rhizobacterium (PGPR) known for its biocontrol properties. AefR was found to repress the expression of the mexEF-oprN efflux pump, which putatively exports N-acylhomoserine lactones (AHLs). This repression attenuates the PcoR/PcoI quorum-sensing system, leading to decreased production of the antibiotic mupirocin in P. fluorescens 2P24. Furthermore, quantitative proteomic analysis revealed that the PcoR/PcoI quorum-sensing system regulates a diverse range of physiological processes, including mupirocin production and denitrification. Collectively, these findings demonstrate a quorum-quenching role of flavonoids in a PGPR strain, establishing that flavonoids can disrupt quorum-sensing by enhancing the efflux of quorum-sensing signaling molecules. These findings have practical implications for the development of sustainable biocontrol strategies, where leveraging natural plant-microbe interactions could enhance the suppression of plant pathogens without the use of synthetic chemicals.IMPORTANCEFlavonoids are key mediators of plant-microbe interactions; however, their role in regulating microbial signaling remains poorly understood. This study identifies AefR as a flavonoid-sensing regulator in Pseudomonas fluorescens 2P24, revealing a novel quorum-quenching mechanism where flavonoids enhance the efflux of quorum-sensing signals. These findings shed light on the molecular basis of flavonoid-mediated microbial regulation and offer new strategies for sustainable plant health management.

The RNA helicase HrpA rescues collided ribosomes in E. coli

Although many antibiotics inhibit bacterial ribosomes, the loss of known factors that rescue stalled ribosomes does not lead to robust antibiotic sensitivity in E. coli, suggesting the existence of additional mechanisms. Here, we show that the RNA helicase HrpA rescues stalled ribosomes in E. coli. Acting selectively on ribosomes that have collided, HrpA uses ATP hydrolysis to split stalled ribosomes into subunits. Cryoelectron microscopy (cryo-EM) structures reveal how HrpA simultaneously binds to two collided ribosomes, explaining its selectivity, and how its helicase module engages downstream mRNA such that, by exerting a pulling force on the mRNA, it would destabilize the stalled ribosome. These studies show that ribosome splitting is a conserved mechanism that allows proteobacteria to tolerate ribosome-targeting antibiotics.

Bacterial peptidoglycan recycling

During growth and division, the bacterial cell wall is remodeled, resulting in the liberation of peptidoglycan (PG) fragments which are typically reinternalized and recycled. Recycling of PG has been studied in a few model species, but its importance and diversity are not yet well understood. Here, we review how bacteria transport and recycle the components of their PG, highlighting updates and new findings.

Comparative Assessment of Protein Large Language Models for Enzyme Commission Number Prediction

BACKGROUND

Protein large language models (LLM) have been used to extract representations of enzyme sequences to predict their function, which is encoded by enzyme commission (EC) numbers. However, a comprehensive comparison of different LLMs for this task is still lacking, leaving questions about their relative performance. Moreover, protein sequence alignments (e.g. BLASTp or DIAMOND) are often combined with machine learning models to assign EC numbers from homologous enzymes, thus compensating for the shortcomings of these models' predictions. In this context, LLMs and sequence alignment methods have not been extensively compared as individual predictors, raising unaddressed questions about LLMs' performance and limitations relative to the alignment methods. In this study, we set out to assess the performance of ESM2, ESM1b, and ProtBERT language models in their ability to predict EC numbers, comparing them with BLASTp, against each other and against models that rely on one-hot encodings of amino acid sequences.

RESULTS

Our findings reveal that combining these LLMs with fully connected neural networks surpasses the performance of deep learning models that rely on one-hot encodings. Moreover, although BLASTp provided marginally better results overall, DL models provide results that complement BLASTp's, revealing that LLMs better predict certain EC numbers while BLASTp excels in predicting others. The ESM2 stood out as the best model among the LLMs tested, providing more accurate predictions on difficult annotation tasks and for enzymes without homologs.

CONCLUSIONS

Crucially, this study demonstrates that LLMs still have to be improved to become the gold standard tool over BLASTp in mainstream enzyme annotation routines. On the other hand, LLMs can provide good predictions for more difficult-to-annotate enzymes, particularly when the identity between the query sequence and the reference database falls below 25%. Our results reinforce the claim that BLASTp and LLM models complement each other and can be more effective when used together.

mSphere of Influence: High-throughput screens to rapidly assign function to microbial genes

Lori Huberman works in the field of fungal genetics, with an emphasis on investigating the genetic mechanisms fungi use to sense and respond to the nutrients and toxins in their environment. In this mSphere of Influence article, she reflects on how "Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons" by K. M. Wetmore, M. N. Price, R. J. Waters, J. S. Lamson, et al. (mBio 6:e00306-15, 2015, https://doi.org/10.1128/mBio.00306-15) made an impact on her by establishing technologies that open realistic possibilities for developing high-throughput screening methods to correlate phenotype to genotype in diverse fungal species.

A co-conserved gene pair supports Caulobacter iron homeostasis during chelation stress

Synthetic metal chelators are widely used in industrial, clinical, and agricultural settings, leading to their accumulation in the environment. We measured the growth of Caulobacter crescentus, a soil and aquatic bacterium, in the presence of the ubiquitous chelator ethylenediaminetetraacetic acid (EDTA) and found that it restricts growth by lowering intracellular iron levels. Using barcoded transposon sequencing, we identified an operonic gene pair, cciT-cciO, that is required to maintain iron homeostasis in laboratory media during EDTA challenge. cciT encodes one of four TonB-dependent transporters that are regulated by the ferric uptake repressor (Fur) and stands out among this group of genes in its ability to support Caulobacter growth across diverse media conditions. The function of CciT strictly requires cciO, which encodes a cytoplasmic FeII dioxygenase-family protein. Our results thus define a functional partnership between an outer membrane iron receptor and a cytoplasmic dioxygenase that are broadly co-conserved in Proteobacteria. We expanded our analysis to natural environments by examining the growth of mutant strains in freshwater from two lakes, each with biochemical and geochemical profiles that differ markedly from standard laboratory media. In lake water, Caulobacter growth did not require cciT or cciO and was less affected by EDTA treatment. This result aligns with our observation that EDTA toxicity is influenced by common forms of biologically chelated iron and the spectrum of free cations present in the medium. Our study defines a conserved iron acquisition system in Proteobacteria and bridges laboratory-based physiology studies with real-world conditions.

IMPORTANCE

Metal-chelating chemicals are widely used across industries, including as preservatives in the food sector, but their full impact on microbial physiology is not well understood. We identified two genes, cciT and cciO, that function together to support Caulobacter crescentus iron balance when cells are exposed to the common synthetic chelator, EDTA. CciT is an outer membrane transporter and CciO is a dioxygenase-family protein that are mutually conserved in many bacteria, including several human pathogens, where mutations in cciT homologs are linked to clinical resistance to the siderophore antibiotic, cefiderocol. This study identifies a conserved genetic system that supports iron homeostasis during chelation stress and illuminates the iron acquisition versatility and stress resilience of Caulobacter in freshwater environments.

Learning maximally spanning representations improves protein function annotation

Automated protein function annotation is a fundamental problem in computational biology, crucial for understanding the functional roles of proteins in biological processes, with broad implications in medicine and biotechnology. A persistent challenge in this problem is the imbalanced, long-tail distribution of available function annotations: a small set of well-studied function classes account for most annotated proteins, while many other classes have few annotated proteins, often due to investigative bias, experimental limitations, or intrinsic biases in protein evolution. As a result, existing machine learning models for protein function prediction tend to only optimize the prediction accuracy for well-studied function classes overrepresented in the training data, leading to poor accuracy for understudied functions. In this work, we develop MSRep, a novel deep learning-based protein function annotation framework designed to address this imbalance issue and improve annotation accuracy. MSRep is inspired by an intriguing phenomenon, called neural collapse (NC), commonly observed in high-accuracy deep neural networks used for classification tasks, where hidden representations in the final layer collapse to class-specific mean embeddings, while maintaining maximal inter-class separation. Given that NC consistently emerges across diverse architectures and tasks for high-accuracy models, we hypothesize that inducing NC structure in models trained on imbalanced data can enhance both prediction accuracy and generalizability. To achieve this, MSRep refines a pre-trained protein language model to produce NC-like representations by optimizing an NC-inspired loss function, which ensures that minority functions are equally represented in the embedding space as majority functions, in contrast to conventional classification methods whose embedding spaces are dominated by overrepresented classes. In evaluations across four protein function annotation tasks on the prediction of Enzyme Commission numbers, Gene3D codes, Pfam families, and Gene Ontology terms, MSRep demonstrates superior predictive performance for both well- and underrepresented classes, outperforming several state-of-the-art annotation tools. We anticipate that MSRep will enhance the annotation of understudied functions and novel, uncharacterized proteins, advancing future protein function studies and accelerating the discovery of new functional proteins. The source code of MSRep is available at https://github.com/luo-group/MSRep.

Gene transfer agents: The ambiguous role of selfless viruses in genetic exchange and bacterial evolution

Gene transfer agents (GTAs) are genetic elements derived from ancestral bacteriophages that have become domesticated by the host. GTAs are present in diverse prokaryotic organisms, where they can facilitate horizontal gene transfer under certain conditions. Unlike typical bacteriophages, GTAs do not exhibit any preference for the replication or transfer of the genes encoding them; instead, they exhibit a remarkable capacity to package chromosomal, and sometimes extrachromosomal, DNA into virus-like capsids and disseminate it to neighboring cells. Because GTAs resemble defective prophages, identification of novel GTAs is not trivial. The detection of candidates relies on the genetic similarity to known GTAs, which has been fruitful in α-proteobacterial lineages but challenging in more distant bacteria. Here we consider several fundamental questions: What is the true prevalence of GTAs in prokaryote genomes? Given there are high costs for GTA production, what advantage do GTAs provide to the bacterial host to justify their maintenance? How is the bacterial chromosome recognized and processed for inclusion in GTA particles? This article highlights the challenges in comprehensively understanding GTAs' prevalence, function and DNA packaging method. Going forward, broad study of atypical GTAs and use of ecologically relevant conditions are required to uncover their true impact on bacterial chromosome evolution.

Genome-wide identification of novel flagellar motility genes in Pseudomonas syringae pv. tomato DC3000

Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) is a plant pathogenic bacterium that possesses complicated motility regulation pathways including a typical chemotaxis system. A significant portion of our understanding about the genes functioning in Pst DC3000 motility is based on comparison to other bacteria. This leaves uncertainty about whether gene functions are conserved, especially since specific regulatory modules can have opposite functions in sets of Pseudomonas. In this study, we used a competitive selection to enrich for mutants with altered swimming motility and used random barcode transposon-site sequencing (RB-TnSeq) to identify genes with significant roles in swimming motility. Besides many of the known or predicted chemotaxis and motility genes, our method identified PSPTO_0406 (dipA), PSPTO_1042 (chrR) and PSPTO_4229 (hypothetical protein) as novel motility regulators. PSPTO_0406 is a homolog of dipA, a known cyclic di-GMP degrading enzyme in P. aeruginosa. PSPTO_1042 is part of an extracytoplasmic sensing system that controls gene expression in response to reactive oxygen species, suggesting that PSPTO_1042 may function as part of a mechanism that enables Pst DC3000 to alter motility when encountering oxidative stressors. PSPTO_4229 encodes a protein containing an HD-related output domain (HDOD), but with no previously identified functions. We found that deletion and overexpression of PSPTO_4229 both reduce swimming motility, suggesting that its function is sensitive to expression level. We used the overexpression phenotype to screen for nonsense and missense mutants of PSPTO_4229 that no longer reduce swimming motility and found a pair of conserved arginine residues that are necessary for motility suppression. Together these results provide a global perspective on regulatory and structural genes controlling flagellar motility in Pst DC3000.

Characterization of YdgH: a mediator of beta-lactam susceptibility in Enterobacterales

Beta-lactam antibiotics are often the treatment of choice for serious bacterial infections. In a previous screen for novel genetic mediators affecting beta-lactam susceptibility, we discovered that deletion of ydgH, a conserved gene of unknown function, leads to increased resistance to beta-lactams, as well as increased susceptibility to detergent compounds. Here, we further characterize YdgH in Serratia marcescens, Enterobacter cloacae, and Escherichia coli using a combination of biochemical and cell biological approaches. We find that YdgH fractionates with periplasmic proteins, and this periplasmic localization is necessary for its function. Using purified recombinant protein, we demonstrate that YdgH is a relatively compact, globular monomer. The YdgH polypeptide contains three tandem DUF1471 domains. In a ΔydgH background, overexpression of polypeptides containing both the second and the third, but not the first DUF1471 domain, is necessary to rescue the deletion phenotype. To determine how YdgH function influences beta-lactam and detergent susceptibility, we tested several targeted hypotheses. We found that deletion of ydgH neither affects ompC or ompF transcript levels, nor does it alter the processing of lipopolysaccharide, nor does it activate the sigma E regulon alone or in combination with mutations in other periplasmic proteins. Finally, we delineate the results of a genetic screen for spontaneous mutants that complement the detergent susceptibility phenotype, the results of which may fuel the further studies that are necessary to determine the precise role YdgH plays in bacterial physiology.IMPORTANCEBeta-lactams such as penicillins and cephalosporins are the most commonly prescribed antibiotics for serious bacterial infections. Increasing antibiotic resistance threatens their effectiveness. We previously identified the uncharacterized gene ydgH as a modifier of beta-lactam susceptibility in Gram-negative bacteria. To begin to understand the specific role of YdgH, in this study, we perform initial characterizations of this protein. We also test hypotheses as to how the function of YdgH contributes to beta-lactam physiology.

Functional protein mining with conformal guarantees

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

Systematic mapping of antibiotic cross-resistance and collateral sensitivity with chemical genetics

By acquiring or evolving resistance to one antibiotic, bacteria can become cross-resistant to a second antibiotic, which further limits therapeutic choices. In the opposite scenario, initial resistance leads to collateral sensitivity to a second antibiotic, which can inform cycling or combinatorial treatments. Despite their clinical relevance, our knowledge of both interactions is limited. We used published chemical genetics data of the Escherichia coli single-gene deletion library in 40 antibiotics and devised a metric that discriminates between known cross-resistance and collateral-sensitivity antibiotic interactions. Thereby we inferred 404 cases of cross-resistance and 267 of collateral-sensitivity, expanding the number of known interactions by over threefold. We further validated 64/70 inferred interactions using experimental evolution. By identifying mutants driving these interactions in chemical genetics, we demonstrated that a drug pair can exhibit both interactions depending on the resistance mechanism. Finally, we applied collateral-sensitive drug pairs in combination to reduce antibiotic-resistance development in vitro.

Niche Partitioning and Intraspecific Variation of Thaumarchaeota in Deep Ocean Sediments

Deep-sea sediments contain a large number of Thaumarchaeota that are phylogenetically distinct from their pelagic counterparts. However, their ecology and evolutionary adaptations are not well understood. Metagenomic analyses were conducted on samples from various depths of a 750-cm sediment core collected from the Mariana Trench Challenger Deep. The abundance of Thaumarchaeota and archaeal amoA generally decreased with depth, except for an unexpected peak midway through the core. The thaumarchaeotal metagenome-assembled genomes were classified into diverse phylogenetic clusters associated with amoA-NP-γ, amoA-NP-θ, and amoA-NP-δ of ammonia-oxidising Thaumarchaeota and non-ammonia-oxidising lineages. The most abundant group was within amoA-NP-γ, which is usually found in coastal and shallow habitats, indicating potential niche expansion from marine shallow to hadal environments. This benthic group showed within-species genomic variations compared to the previously identified Hadal water group, suggesting microdiversification of hadal Thaumarchaeota along with niche separation between benthic and pelagic environments. Evolutionary adaptations associated with the benthic-to-pelagic transition included reduced genome size, loss of motility/cell adhesion, altered energy metabolism, and different mechanisms for substrate acquisition and regulation (e.g., ammonium). These findings offer new insights into the evolution of hadal Thaumarchaeota and demonstrate, for the first time, intraspecies-level genomic variation in Thaumarchaeota related to the benthic-versus-pelagic niche partitioning in the deep ocean.

Acarbose impairs gut Bacteroides growth by targeting intracellular glucosidases

Acarbose is a type 2 diabetes medicine that prevents dietary starch breakdown into glucose by inhibiting host amylase and glucosidase enzymes. Numerous gut species in the Bacteroides genus enzymatically break down starch and change in relative abundance within the gut microbiome in acarbose-treated individuals. To mechanistically explain this observation, we used two model starch-degrading Bacteroides, Bacteroides ovatus (Bo), and Bacteroides thetaiotaomicron (Bt). Bt growth on starch polysaccharides is severely impaired by acarbose, whereas Bo growth is much less affected by the drug. The Bacteroides use a starch utilization system (Sus) to grow on starch. We hypothesized that Bo and Bt Sus enzymes are differentially inhibited by acarbose. Instead, we discovered that although acarbose primarily targets the Sus periplasmic GH97 enzymes in both organisms, the drug affects starch processing at multiple other points. Acarbose competes for transport through the TonB-dependent SusC proteins and binds to the Sus transcriptional regulators. Furthermore, Bo expresses a non-Sus GH97 (BoGH97D) when grown in starch with acarbose. The Bt homolog, BtGH97H, is not expressed in the same conditions, nor can overexpression of BoGH97D complement the Bt growth inhibition in the presence of acarbose. This work informs us about unexpected complexities of Sus function and regulation in Bacteroides, including variation between related species. Furthermore, this indicates that the gut microbiome may be a source of variable response to acarbose treatment for diabetes.

IMPORTANCE

Acarbose is a type 2 diabetes medication that works primarily by stopping starch breakdown into glucose in the small intestine. This is accomplished by the inhibition of host enzymes, leading to better blood sugar control via reduced ability to derive glucose from dietary starches. The drug and undigested starch travel to the large intestine where acarbose interferes with the ability of some bacteria to grow on starch. However, little is known about how gut bacteria interact with acarbose, including microbes that can use starch as a carbon source. Here, we show that two gut species, Bacteroides ovatus (Bo) and Bacteroides thetaiotaomicron (Bt), respond differently to acarbose: Bt growth is inhibited by acarbose, while Bo growth is less affected. We reveal a complex set of mechanisms involving differences in starch import and sensing behind the different Bo and Bt responses. This indicates the gut microbiome may be a source of variable response to acarbose treatment for diabetes via complex mechanisms in common gut microbes.

Geography, Antimicrobial Resistance, and Genomics of Salmonella enterica (Serotypes Newport and Anatum) from Meat in Mexico (2021-2023)

Salmonella enterica non-typhoidal is a major contributor to diarrheal diseases, with over 2600 serovars identified across diverse environments. In Mexico, serovars Newport and Anatum have shown a marked increase, especially in foodborne disease, posing a public health problem. We conducted a cross-sectional study from 2021 to 2023 using active epidemiological surveillance to assess contamination in ground beef and pork at butcher shops nationwide. It involved isolation, phenotypic antimicrobial resistance, comparative genomics, spatial distribution, antimicrobial-resistance genes, and pangenome analysis. A total of 402 non-typhoidal S. enterica strains were isolated, including 59 Newport and 50 Anatum. After curating for redundancy, 45 Newport and 32 Anatum strains remained. We found that 75% of Newport strains exhibited multidrug resistance (MDR), compared to 25% of Anatum strains. Salmonella Newport also showed a broader distribution and stronger antibiotic-resistance capacity, particularly due to genes such as mphA and ramA. Our pangenome analysis showed a predominance of cell maintenance and survival-process genes in the accessory genome of both serotypes. Considering unique genes, Salmonella Anatum and Newport showed a notorious abundance of genes with functions related to replication, recombination, and repair. The substantial rise of Anatum and Newport strains in meat samples for human consumption presents an epidemiological alert, highlighting the critical need for stringent surveillance programs to mitigate human and ecosystem health risks.

Gene transfer agents: structural and functional properties of domesticated viruses

Horizontal exchange of DNA between bacteria and archaea is prevalent and has major potential implications for genome evolution, plasticity, and population fitness. Several transfer mechanisms have been identified, including gene transfer agents (GTAs). GTAs are intricately regulated domesticated viruses that package host DNA into virus-like capsids and transfer this DNA throughout the bacterial community. Several important advances have recently been made in our understanding of these unusual particles. In this review, we highlight some of these findings, primarily for the model GTA produced by Rhodobacter capsulatus but also for newly identified GTA producers. We provide key insights into these important genetic elements, including the differences between GTAs from their ancestral bacteriophages, their regulation and control, and their elusive evolutionary function.

A cyanobacterial sigma factor F controls biofilm-promoting genes through intra- and intercellular pathways

Cyanobacteria frequently constitute integral components of microbial communities known as phototrophic biofilms, which are widespread in various environments. Moreover, assemblages of these organisms, which serve as an expression platform, simplify harvesting the biomass, thereby holding significant industrial relevance. Previous studies of the model cyanobacterium Synechococcus elongatus PCC 7942 revealed that its planktonic growth habit results from a biofilm-suppression mechanism that depends on an extracellular inhibitor, an observation that opens the door to investigating cyanobacterial intercellular communication. Here, we demonstrate that the RNA polymerase sigma factor SigF1, is required for this biofilm-suppression mechanism whereas the S. elongatus paralog SigF2 is not involved in biofilm regulation. Comprehensive transcriptome analyses identified distinct regulons under the control of each of these sigma factors. sigF1 inactivation substantially lowers transcription of genes that code for the primary pilus subunit and consequently prevents pilus assembly. Moreover, additional data demonstrate absence of the biofilm inhibitor from conditioned medium of the sigF1 mutant, further validating involvement of the pilus assembly complex in secretion of the biofilm inhibitor. Consequently, expression is significantly upregulated for the ebfG-operon that encodes matrix components and the genes that encode the corresponding secretion system, which are repressed by the biofilm inhibitor in the wild type. Thus, this study uncovers a basic regulatory component of cyanobacterial intercellular communication, a field that is in its infancy. Elevated expression of biofilm-promoting genes in a sigF1 mutant supports an additional layer of regulation by SigF1 that operates via an intracellular mechanism.

Correction of a widespread bias in pooled chemical genomics screens improves their interpretability

Chemical genomics is a powerful and increasingly accessible technique to probe gene function, gene-gene interactions, and antibiotic synergies and antagonisms. Indeed, multiple large-scale pooled datasets in diverse organisms have been published. Here, we identify an artifact arising from uncorrected differences in the number of cell doublings between experiments within such datasets. We demonstrate that this artifact is widespread, show how it causes spurious gene-gene and drug-drug correlations, and present a simple but effective post hoc method for removing its effects. Using several published datasets, we demonstrate that this correction removes spurious correlations between genes and conditions, improving data interpretability and revealing new biological insights. Finally, we determine experimental factors that predispose a dataset for this artifact and suggest a set of experimental and computational guidelines for performing pooled chemical genomics experiments that will maximize the potential of this powerful technique.

Community standards and future opportunities for synthetic communities in plant-microbiota research

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. To address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

High-throughput protein characterization by complementation using DNA barcoded fragment libraries

Our ability to predict, control, or design biological function is fundamentally limited by poorly annotated gene function. This can be particularly challenging in non-model systems. Accordingly, there is motivation for new high-throughput methods for accurate functional annotation. Here, we used complementation of auxotrophs and DNA barcode sequencing (Coaux-Seq) to enable high-throughput characterization of protein function. Fragment libraries from eleven genetically diverse bacteria were tested in twenty different auxotrophic strains of Escherichia coli to identify genes that complement missing biochemical activity. We recovered 41% of expected hits, with effectiveness ranging per source genome, and observed success even with distant E. coli relatives like Bacillus subtilis and Bacteroides thetaiotaomicron. Coaux-Seq provided the first experimental validation for 53 proteins, of which 11 are less than 40% identical to an experimentally characterized protein. Among the unexpected function identified was a sulfate uptake transporter, an O-succinylhomoserine sulfhydrylase for methionine synthesis, and an aminotransferase. We also identified instances of cross-feeding wherein protein overexpression and nearby non-auxotrophic strains enabled growth. Altogether, Coaux-Seq's utility is demonstrated, with future applications in ecology, health, and engineering.

The energy metabolism of Cupriavidus necator in different trophic conditions

The "knallgas" bacterium Cupriavidus necator is attracting interest due to its extremely versatile metabolism. C. necator can use hydrogen or formic acid as an energy source, fixes CO2 via the Calvin-Benson-Bassham (CBB) cycle, and grows on organic acids and sugars. Its tripartite genome is notable for its size and duplications of key genes (CBB cycle, hydrogenases, and nitrate reductases). Little is known about which of these isoenzymes and their cofactors are actually utilized for growth on different substrates. Here, we investigated the energy metabolism of C. necator H16 by growing a barcoded transposon knockout library on succinate, fructose, hydrogen (H2/CO2), and formic acid. The fitness contribution of each gene was determined from enrichment or depletion of the corresponding mutants. Fitness analysis revealed that (i) some, but not all, molybdenum cofactor biosynthesis genes were essential for growth on formate and nitrate respiration. (ii) Soluble formate dehydrogenase (FDH) was the dominant enzyme for formate oxidation, not membrane-bound FDH. (iii) For hydrogenases, both soluble and membrane-bound enzymes were utilized for lithoautotrophic growth. (iv) Of the six terminal respiratory complexes in C. necator H16, only some are utilized, and utilization depends on the energy source. (v) Deletion of hydrogenase-related genes boosted heterotrophic growth, and we show that the relief from associated protein cost is responsible for this phenomenon. This study evaluates the contribution of each of C. necator's genes to fitness in biotechnologically relevant growth regimes. Our results illustrate the genomic redundancy of this generalist bacterium and inspire future engineering strategies.IMPORTANCEThe soil bacterium Cupriavidus necator can grow on gas mixtures of CO2, H2, and O2. It also consumes formic acid as carbon and energy source and various other substrates. This metabolic flexibility comes at a price, for example, a comparatively large genome (6.6 Mb) and a significant background expression of lowly utilized genes. In this study, we mutated every non-essential gene in C. necator using barcoded transposons in order to determine their effect on fitness. We grew the mutant library in various trophic conditions including hydrogen and formate as the sole energy source. Fitness analysis revealed which of the various energy-generating iso-enzymes are actually utilized in which condition. For example, only a few of the six terminal respiratory complexes are used, and utilization depends on the substrate. We also show that the protein cost for the various lowly utilized enzymes represents a significant growth disadvantage in specific conditions, offering a route to rational engineering of the genome. All fitness data are available in an interactive app at https://m-jahn.shinyapps.io/ShinyLib/.

Metagenomic analysis during capecitabine therapy reveals microbial chemoprotective mechanisms and predicts drug toxicity in colorectal cancer patients

Purpose

Unpredictable chemotherapy side effects are a major barrier to successful treatment. Cell culture and mouse experiments indicate that the gut microbiota is influenced by and influences anti-cancer drugs. However, metagenomic data from patients paired to careful side effect monitoring remains limited. Herein, we focus on the oral fluoropyrimidine capecitabine (CAP). We investigate CAP-microbiome interactions through metagenomic sequencing of longitudinal stool sampling from a cohort of advanced colorectal cancer (CRC) patients.

Methods

We established a prospective cohort study including 56 patients with advanced CRC treated with CAP monotherapy across 4 centers in the Netherlands. Stool samples and clinical questionnaires were collected at baseline, during cycle 3, and post-treatment. Metagenomic sequencing to assess microbial community structure and gene abundance was paired with transposon mutagenesis, targeted gene deletion, and media supplementation experiments. An independent US cohort was used for model validation.

Results

CAP treatment significantly altered gut microbial composition and pathway abundance, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation confirmed that menaquinol biosynthesis protects Escherichia coli from drug toxicity. Microbial menaquinol biosynthesis genes were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted hand-foot syndrome and dose reductions in an independent cohort.

Conclusion

These results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells, with implications for toxicities outside the gastrointestinal tract. We provide a proof-of-concept for the use of microbiome profiling and machine learning to predict drug toxicities across independent cohorts. These observations provide a foundation for future human intervention studies, more in-depth mechanistic dissection in preclinical models, and extension to other cancer treatments.

Systematic, high-throughput characterization of bacteriophage gene essentiality on diverse hosts

Understanding core and conditional gene essentiality is crucial for decoding genotype-phenotype relationships in organisms. We present PhageMaP, a high-throughput method to create genome-scale phage knockout libraries for systematically assessing gene essentiality in bacteriophages. Using PhageMaP, we generate gene essentiality maps across hundreds of genes in the model phage T7 and the non-model phage Bas63, on diverse hosts. These maps provide fundamental insights into genome organization, gene function, and host-specific conditional essentiality. By applying PhageMaP to a collection of anti-phage defense systems, we uncover phage genes that either inhibit or activate eight defenses and offer novel mechanistic hypotheses. Furthermore, we engineer synthetic phages with enhanced infectivity by modular transfer of a PhageMaP-discovered defense inhibitor from Bas63 to T7. PhageMaP is generalizable, as it leverages homologous recombination, a universal cellular process, for locus-specific barcoding. This versatile tool advances bacteriophage functional genomics and accelerates rational phage design for therapy.

XylR regulates genes at xyl cluster, involved in D-xylose catabolism in Herbaspirillum seropedicae Z69

D-xylose, one of the most abundant sugars in lignocellulosic biomass, is not widely used to produce bioproducts with added value, in part due to the absence of industrial microorganisms able to metabolize it efficiently. Herbaspirillum seropedicae Z69 is a β-proteobacterium able to accumulate poly-3-hydroxybutyrate, a biodegradable thermoplastic biopolymer, with contents higher than 50%. It metabolizes D-xylose by non-phosphorylative pathways. In the genome of Z69, we found the genes xylFGH (ABC D-xylose transporter), xylB, xylD, and xylC (superior non-phosphorylative pathway), and the transcriptional regulator xylR, forming the xyl cluster. We constructed the knock-out mutant Z69ΔxylR that has a reduced growth in D-xylose and in D-glucose, compared with Z69. In addition, we analyzed the expression of xyl genes by RT-qPCR and promoter fusion. These results suggest that XylR activates the expression of genes at the xyl cluster in the presence of D-xylose. On the other hand, XylR does not regulate the expression of xylA, mhpD (lower non-phosphorylative pathways) and araB (L-arabinose dehydrogenase) genes. The participation of D-glucose in the regulation mechanism of these genes must still be elucidated. These results contribute to the development of new strains adapted to consume lignocellulosic sugars for the production of value-added bioproducts.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.

High-throughput genetics enables identification of nutrient utilization and accessory energy metabolism genes in a model methanogen

Archaea are widespread in the environment and play fundamental roles in diverse ecosystems; however, characterization of their unique biology requires advanced tools. This is particularly challenging when characterizing gene function. Here, we generate randomly barcoded transposon libraries in the model methanogenic archaeon Methanococcus maripaludis and use high-throughput growth methods to conduct fitness assays (RB-TnSeq) across over 100 unique growth conditions. Using our approach, we identified new genes involved in nutrient utilization and response to oxidative stress. We identified novel genes for the usage of diverse nitrogen sources in M. maripaludis including a putative regulator of alanine deamination and molybdate transporters important for nitrogen fixation. Furthermore, leveraging the fitness data, we inferred that M. maripaludis can utilize additional nitrogen sources including ʟ-glutamine, ᴅ-glucuronamide, and adenosine. Under autotrophic growth conditions, we identified a gene encoding a domain of unknown function (DUF166) that is important for fitness and hypothesize that it has an accessory role in carbon dioxide assimilation. Finally, comparing fitness costs of oxygen versus sulfite stress, we identified a previously uncharacterized class of dissimilatory sulfite reductase-like proteins (Dsr-LP; group IIId) that is important during growth in the presence of sulfite. When overexpressed, Dsr-LP conferred sulfite resistance and enabled use of sulfite as the sole sulfur source. The high-throughput approach employed here allowed for generation of a large-scale data set that can be used as a resource to further understand gene function and metabolism in the archaeal domain.IMPORTANCEArchaea are widespread in the environment, yet basic aspects of their biology remain underexplored. To address this, we apply randomly barcoded transposon libraries (RB-TnSeq) to the model archaeon Methanococcus maripaludis. RB-TnSeq coupled with high-throughput growth assays across over 100 unique conditions identified roles for previously uncharacterized genes, including several encoding proteins with domains of unknown function (DUFs). We also expand on our understanding of carbon and nitrogen metabolism and characterize a group IIId dissimilatory sulfite reductase-like protein as a functional sulfite reductase. This data set encompasses a wide range of additional conditions including stress, nitrogen fixation, amino acid supplementation, and autotrophy, thus providing an extensive data set for the archaeal community to mine for characterizing additional genes of unknown function.

The RNA helicase HrpA rescues collided ribosomes in E. coli

Although many antibiotics inhibit bacterial ribosomes, loss of known factors that rescue stalled ribosomes does not lead to robust antibiotic sensitivity in E. coli, suggesting the existence of additional mechanisms. Here, we show that the RNA helicase HrpA rescues stalled ribosomes in E. coli. Acting selectively on ribosomes that have collided, HrpA uses ATP hydrolysis to split stalled ribosomes into subunits. Cryo-EM structures reveal how HrpA simultaneously binds to two collided ribosomes, explaining its selectivity, and how its helicase module engages downstream mRNA, such that by exerting a pulling force on the mRNA, it would destabilize the stalled ribosome. These studies show that ribosome splitting is a conserved mechanism that allows proteobacteria to tolerate ribosome-targeting antibiotics.

Interactive tools for functional annotation of bacterial genomes

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein's function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein's function. Ideally, accurate annotations would allow us to predict a bacterium's capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest. Database URL: http://papers.genomics.lbl.gov.

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

Simvastatin induces human gut bacterial cell surface genes

Drugs intended to target mammalian cells can have broad off-target effects on the human gut microbiota with potential downstream consequences for drug efficacy and side effect profiles. Yet, despite a rich literature on antibiotic resistance, we still know very little about the mechanisms through which commensal bacteria evade non-antibiotic drugs. Here, we focus on statins, one of the most prescribed drug types in the world and an essential tool in the prevention and treatment of high circulating cholesterol levels. Prior work in humans, mice, and cell culture support an off-target effect of statins on human gut bacteria; however, the genetic determinants of statin sensitivity remain unknown. We confirmed that simvastatin inhibits the growth of diverse human gut bacterial strains grown in communities and in pure cultures. Drug sensitivity varied between phyla and was dose-dependent. We selected two representative simvastatin-sensitive species for more in-depth analysis: Eggerthella lenta (phylum: Actinobacteriota) and Bacteroides thetaiotaomicron (phylum: Bacteroidota). Transcriptomics revealed that both bacterial species upregulate genes in response to simvastatin that alter the cell membrane, including fatty acid biogenesis (E. lenta) and drug efflux systems (B. thetaiotaomicron). Transposon mutagenesis identified a key efflux system in B. thetaiotaomicron that enables growth in the presence of statins. Taken together, these results emphasize the importance of the bacterial cell membrane in countering the off-target effects of host-targeted drugs. Continued mechanistic dissection of the various mechanisms through which the human gut microbiota evades drugs will be essential to understand and predict the effects of drug administration in human cohorts and the potential downstream consequences for health and disease.

Comprehensive double-mutant analysis of the Bacillus subtilis envelope using double-CRISPRi

Understanding bacterial gene function remains a major biological challenge. Double-mutant genetic interaction (GI) analysis addresses this challenge by uncovering the functional partners of targeted genes, allowing us to associate genes of unknown function with novel pathways and unravel connections between well-studied pathways, but is difficult to implement at the genome-scale. Here, we develop and use double-CRISPRi to systematically quantify genetic interactions at scale in the Bacillus subtilis envelope, including essential genes. We discover > 1000 known and novel genetic interactions. Our analysis pipeline and experimental follow-ups reveal the distinct roles of paralogous genes such as the mreB and mbl actin homologs, and identify new genes involved in the well-studied process of cell division. Overall, our study provides valuable insights into gene function and demonstrates the utility of double-CRISPRi for high-throughput dissection of bacterial gene networks, providing a blueprint for future studies in diverse bacterial species.

Barcoded overexpression screens in gut Bacteroidales identify genes with roles in carbon utilization and stress resistance

A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to-phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we develop Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron. From hundreds of experiments, we identify new functions and phenotypes for 29 genes important for carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a D-glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to ceftriaxone and bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assays.

Genes required for phosphosphingolipid formation in Caulobacter crescentus contribute to bacterial virulence

Sphingolipids are ubiquitous in membranes of eukaryotes and are associated with important cellular functions. Although sphingolipids occur scarcely in bacteria, for some of them they are essential and, in other bacteria, they contribute to fitness and stability of the outer membrane, such as in the well-studied α-proteobacterium Caulobacter crescentus. We previously defined five structural genes for ceramide synthesis in C. crescentus, among them the gene for serine palmitoyltransferase, the enzyme that catalyzes the committed step of sphingolipid biosynthesis. Other mutants affected in genes of this same genomic region show cofitness with a mutant deficient in serine palmitoyltransferase. Here we show that at least two phosphosphingolipids are produced in C. crescentus and that at least another six gene products are needed for the decoration of ceramide upon phosphosphingolipid formation. All eleven genes participating in phosphosphingolipid formation are also required in C. crescentus for membrane stability and for displaying sensitivity towards the antibiotic polymyxin B. The genes for the formation of complex phosphosphingolipids are also required for C. crescentus virulence on Galleria mellonella insect larvae.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

Large-scale investigation of species-specific orphan genes in the human gut microbiome elucidates their evolutionary origins

Species-specific genes, also known as orphans, are ubiquitous across life's domains. In prokaryotes, species-specific orphan genes (SSOGs) are mostly thought to originate in external elements such as viruses followed by horizontal gene transfer, whereas the scenario of native origination, through rapid divergence or de novo, is mostly dismissed. However, quantitative evidence supporting either scenario is lacking. Here, we systematically analyzed genomes from 4644 human gut microbiome species and identified more than 600,000 unique SSOGs, representing an average of 2.6% of a given species' pangenome. These sequences are mostly rare within each species yet show signs of purifying selection. Overall, SSOGs use optimal codons less frequently, and their proteins are more disordered than those of conserved genes (i.e., non-SSOGs). Importantly, across species, the GC content of SSOGs closely matches that of conserved ones. In contrast, the ∼5% of SSOGs that share similarity to known viral sequences have distinct characteristics, including lower GC content. Thus, SSOGs with similarity to viruses differ from the remaining SSOGs, contrasting an external origination scenario for most of them. By examining the orthologous genomic region in closely related species, we show that a small subset of SSOGs likely evolved natively de novo and find that these genes also differ in their properties from the remaining SSOGs. Our results challenge the notion that external elements are the dominant source of prokaryotic genetic novelty and will enable future studies into the biological role and relevance of species-specific genes in the human gut.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

Analysis of the key genes of Lactobacillus reuteri strains involved in the protection against alcohol-induced intestinal barrier damage

Gastrointestinal inflammation and intestinal barrier function have important effects on human health. Alcohol, an important foodborne hazard factor, damages the intestinal barrier, increasing the risk of disease. Lactobacillus reuteri strains have been reported to reduce gastrointestinal inflammation and strengthen the intestinal barrier. In this study, we selected three anti-inflammatory L. reuteri strains to evaluate their role in the protection of the intestinal barrier and their immunomodulatory activity in a mouse model of gradient alcohol intake. Among the three strains tested (FSCDJY33M3, FGSZY33L6, and FCQHCL8L6), L. reuteri FSCDJY33M3 was found to protect the intestinal barrier most effectively, possibly due to its ability to reduce the expression of interleukin (IL)-1β, IL-6, and tumor necrosis factor-alpha (TNF-α) and increase the expression of tight junction proteins (occludin, claudin-3). Genomic analysis suggested that the protective effects of L. reuteri FSCDJY33M3 may be related to functional genes and glycoside hydrolases associated with energy production and conversion, amino acid transport and metabolism, carbohydrate transport and metabolism, and DNA replication, recombination, and repair. These genes include COG2856, COG1804, COG2071, and COG1061, which encode adenine deaminase, acyl-CoA transferases, glutamine amidotransferase, RNA helicase, and glycoside hydrolases, including GH13_20, GH53, and GH70. Our results identified functional genes that may be related to protection against alcohol-induced intestinal barrier damage, which might be useful for screening lactic acid bacterial strains that can protect the intestinal barrier.