ABSTRACT Organohalide-respiring bacteria (OHRB), such as Dehalobacter , play key roles in the bioremediation of anoxic environments contaminated with chlorinated aromatic compounds. These obligate anaerobes rely on syntrophic interactions to obtain essential resources—hydrogen, acetate, and corrinoid cofactors—from acetogens and fermenters. However, the metabolic interactions enabling complete reductive dehalogenation of compounds like 1,2,4-trichlorobenzene (1,2,4-TCB) to benzene remain incompletely understood. In this study, we asked: (1) What are the key microbial taxa and their functional roles within a Dehalobacter -containing anaerobic microbial community detoxifying chlorinated benzenes? (2) How do syntrophic interactions enable complete dehalogenation of 1,2,4-TCB to benzene under anaerobic conditions? (3) Can genome-resolved metagenomics and genome-scale metabolic modeling elucidate the metabolic dependencies supporting organohalide respiration in complex consortia? To address these questions, we cultivated microbial communities in batch reactors using methanol as electron donor and either 1,2,4-TCB or monochlorobenzene (MCB) as electron acceptor. In active MCB-fed cultures, benzene increased from 0 to 62.3µmol per bottle while MCB decreased from 88.3 to 22.0µmol per bottle over 120 days, with this pattern repeating across multiple substrate additions. Using genome-resolved metagenomics to identify dominant taxa and select 12 high-quality metagenome-assembled genomes (MAGs) for modeling, we reconstructed genome-scale metabolic models (GEMs) to identify candidate metabolic interactions and predict syntrophic dependencies that may support organohalide respiration in these consortia. Community flux sampling predicted that methanol, H 2 , acetate, and CO 2 formed the dominant exchange backbone of the modeled community, while also indicating competition for shared electron donors between the two Dehalobacter populations. Model-guided minimal-community analysis further identified a narrow dechlorinating core in which all feasible minimal consortia retained a Dehalobacter member together with Methanothrix . These results provide a modeling-informed framework for hypothesis generation and future experimental validation of anaerobic consortia relevant to bioremediation.

High-throughput omics technologies enable detailed molecular profiling of complex microbial communities, yet FAIRification of the resulting datasets is commonly treated as a secondary step performed after data analysis. This approach overlooks the practical benefits of integrating standardized metadata early in the research process: experimental conditions directly determine how raw data should be processed, so capturing interoperable metadata at the outset creates a machine-actionable basis for driving and documenting the entire analytical workflow. Here, we describe a modular, containerized workflow implemented in Common Workflow Language that follows the single-containment principle and records software provenance, tool versions, and parameter settings at each processing step. Using defined mock metagenomic community datasets as controlled benchmarks, we demonstrate how machine-readable experimental metadata can initiate and parameterize multi-step analyses covering read quality control, assembly, binning, quality assessment, and functional annotation. The resulting provenance chains link experimental context, workflow definitions, and derived data products to support traceability and downstream reuse.

Flavonifractor plautii, a prevalent gut commensal, uniquely combines flavonoid degradation with the capacity to produce health-promoting short-chain fatty acids (SCFAs), notably butyrate and propionate. However, its metabolic pathways, ecological roles, and health impacts remain poorly characterized. To explore its probiotic potential and ecological functions, we developed a genome-scale metabolic model, iFP655, using automated reconstruction, deep-learning-based gap-filling, thermodynamic constraints, and transcriptomics. The iFP655 model substantially improved the predictions of growth rates and SCFA profiles compared to previous models. Simulations identified acetyl-CoA pathways as the preferred route for butyrate production, whereas the energetically costly lysine pathway remained inactive despite robust gene expression. Propionate synthesis occurred primarily via the methylmalonyl-CoA pathway. Community metabolic modeling with representative species of a Western minimal gut microbiota highlighted F. plautii's contributions to enhanced SCFA production, especially butyrate, amino acid metabolism, and syntrophic interactions driven by dietary substrates. Our findings indicate that diet-driven syntrophy significantly shapes microbial community structure and function, underscoring the ecological importance of F. plautii in gut microbial interactions and highlighting its potential as a probiotic candidate to beneficially modulate gut microbiota through dietary interventions.

Syngas fermentation is a powerful platform for converting waste streams into sustainable carboxylic acid precursors for value-added biochemicals. Steel mills produce significant syngas, yet industrial microbial syngas valorization remains unrealized. The most promising syngas-converting biocatalysts consist of Clostridia species, such as Clostridium kluyveri, Clostridium autoethanogenum, and Clostridium ljungdahlii. Clostridium luticellarii, a recently discovered species, shares close phylogenetic ties with these organisms. Preliminary metabolic studies suggest its potential for syngas acetogenesis as well as chain elongation. In this study, we create iSJ444, a constraint-based metabolic model of C. luticellarii using iHN637 of a close relative C. ljungdahlii as a starting point. Model predictions support hypothesized methanol and syngas pathways from the metabolic characterization studies; however, the use of propionate could not be accurately predicted. Thermodynamic Flux Analysis (TFA) reveals that C. luticellarii maintains stable energy dissipation across most reactions when exposed to varying pH, with significant increases observed in reactions associated with the Wood-Ljungdahl pathway (WLP), such as the HACD1 reaction, at higher pH (6.5), suggesting an adaptive role in energy management under neutral conditions. Flux sampling simulations exploring metabolic flux distributions show that C. luticellarii might fit into syngas fermenting platforms. In both cases, high hydrogen-to-carbon source ratios result in better production of (iso)butyrate and caproate. We present a minimal genome-scale metabolic model of C. luticellarii as a foundation for further exploration and optimization. Although our predictions of its metabolic behavior await experimental validation, they underscore the potential of C. luticellarii to enhance syngas fermentation platforms.

Members of the Rickettsiella genus (order: Legionellales) are emerging as widespread bacteria associated with insects, arachnids, and crustaceans. While some Rickettsiella strains are highly virulent pathogens, others are maternally inherited endosymbionts that manipulate arthropod phenotypes, including the induction of defensive symbiosis and cytoplasmic incompatibility. However, the genomic diversity of Rickettsiella remains largely unexplored, and their genetic potential to induce complex phenotypes in arthropods is only partially understood. In this study, we sequenced five new Rickettsiella genomes isolated from three tick species. Through comparative genomics, we observed that Rickettsiella members share similar metabolic capabilities, and collectively lack virulence genes from pathogenic Legionellales. Additional analysis of Rickettsiella genomes revealed significant variability in metabolic properties related to endosymbiosis. Specifically, their capacity to biosynthesize certain B vitamins and heme varies, suggesting a functional role of some Rickettsiella strains in the nutrition of their arthropod hosts. Some Rickettsiella genomes harbour homologs of Wolbachia cif genes, the cause of Wolbachia-induced cytoplasmic incompatibility, suggesting that Rickettsiella may use a similar molecular mechanism to manipulate the reproduction of their arthropod hosts. Phylogenomics further revealed that tick-borne Rickettsiella exhibit distinct evolutionary origins within the genus, indicating that Rickettsiella have undergone repeated horizontal transfers between ticks and other arthropods.

Plant natural products play a crucial role in ecological balance, human health, industrial applications, and biodiversity conservation, making them invaluable across various fields. Elucidation of their biosynthetic pathways is important for further synthetic biology applications. Through gene cluster, co-expression, and population association assays, researchers leverage extensive genomics, transcriptomics, and metabolomics data produced by multi-omic technologies to uncover metabolic genes involved in biosynthetic pathways. New techniques such as single-cell sequencing, MS imaging, and machine learning have shown their potential. Here we reviewed multiple omics studies of natural product biosynthesis and discussed the promise and potential of developing techniques for this task.

ABSTRACT Ixodes ricinus ticks act as vectors for numerous pathogens that present substantial health threats. Additionally, they harbor vertically transmitted symbionts, some of which have been linked to diseases. The difficulty of isolating and cultivating these symbionts has hampered our understanding of their biological role, their potential to cause disease, and their modes of transmission. To expand our understanding of the tick symbiont Midichloria mitochondrii and Rickettsia helvetica, which has been linked to disease in humans, we utilized deep sequencing on 16 individual adult female ticks collected from coastal dune and forested areas in the Netherlands. By employing a combination of second- and third-generation sequencing techniques, we successfully reconstructed the complete genomes of M. mitochondrii from 11 individuals, R. helvetica from eight individuals, and the mitochondrial genome from all ticks. Additionally, we visualized the location of R. helvetica in tick organs and constructed genome-scale metabolic models (GEMs) of both symbionts to study their environmental dependencies. Our analysis revealed a strong cophylogeny between M. mitochondrii and mitochondrial genomes, suggesting frequent maternal transmission. In contrast, the absence of cophylogeny between R. helvetica and the mitochondrial genomes, coupled with its presence in the receptaculum seminis of I. ricinus females, raises the possibility of paternal transmission of R. helvetica. Notably, the genetic diversity of R. helvetica was found to be very low, except for the rickA virulence gene, where the presence of up to 13 insertions of a 33 nt-long repeat led to significant variability. However, this variation could not account for the differences in infection prevalence observed across eight distinct locations in the Netherlands. By employing deep sequencing, it becomes feasible to extract complete genomes and genetic data of symbionts directly from their host organisms. This methodology serves as a robust means to gain fresh insights into their interactions. Our observations, which suggest paternal transmission of R. helvetica, a relatively unexplored mode of transmission in ticks, require validation through experimental investigations. The genetic variations identified in the rickA virulence gene of R. helvetica have the potential to influence the infectivity and transmission dynamics of R. helvetica.IMPORTANCETicks are vectors of numerous human pathogens; however, the microbial interactions within ticks and the mechanisms governing pathogen transmission remain poorly understood. This study uses deep sequencing of individual Ixodes ricinus to reconstruct high-quality genomes of endosymbionts and the mitochondrion of the tick, revealing previously undetected microbial dynamics. Notably, we recovered low-abundance Rickettsia and Midichloria genomes from single ticks and present evidence that suggests paternal transmission of R. helvetica. These findings offer novel insights into the ecology and evolution of tick-associated microbes and have implications for understanding the origins and spread of tick-borne diseases.

Availability of microbial genomic data and development of machine learning methods create a unique opportunity to establish associations between genetic information and phenotypes. Here we present a computational workflow for Microbial Genome Prospecting (MiGenPro) that combines phenotype and genomic information. MiGenPro serves as a workflow for the generation of machine learning models that predict microbial traits from genome sequences. Microbial genomes have been consistently annotated and features were stored in a semantic framework. The data was used to train machine learning models and successfully predicted microbial traits such as motility, Gram stain, optimal temperature range, and sporulation capabilities. To ensure robustness, five-fold cross-validation was implemented and demonstrated consistent model performance across iterations and without overfitting. Effectiveness was further validated through comparison with existing models, showing comparable accuracy, with modest variations attributed to differences in datasets rather than methodology. Classification can be further explored using feature importance characterisation to identify biologically relevant genomic features. MiGenPro provides an interoperable workflow to build models and predict phenotypes from microbes based on their genome. ### Competing Interest Statement The authors have declared no competing interest. Novo Nordisk Foundation, https://ror.org/04txyc737, NNFSA210073688 Dutch Research Council, https://ror.org/04jsz6e67, 184.035.007

Surface runoff plays an important role in contaminant transport, nutrient loss, soil erosion and peak discharges in streams and rivers. Because it is the result of a variety of complex hydrological processes, estimating surface runoff using physically based hydrological models is challenging. Upscaling of physical soil properties is necessary to cope with the limits of computational power in surface runoff modelling. In flat landscapes, the (micro)topographic surface controls the onset and progression of surface runoff on saturated soils during rain events. Therefore, its proper representation is crucial when attempting to model and predict surface runoff. In this study, the influence of microtopography (centimetre scale) on estimations of maximum depression storage (MDS), random roughness (RR) and the connectivity threshold (CT) is explored. These properties are selected because they often serve as surface runoff indicators in hydrological modelling. To characterize microtopography, a terrestrial laser scanner (TLS) is used to generate a digital terrain model (DTM) of the study site with a horizontal spatial resolution of 5 cm. MDS, RR and CT are then calculated and compared to the values generated from the publicly available Dutch national DTM dataset with a resolution of 50 cm. Our results show considerable differences in MDS, RR and CT when calculated for the different input resolution datasets. Using DTMs that do not sufficiently capture microtopography leads to underestimation of MDS and RR, and to overestimation of CT. Our findings indicate that surface runoff indicators, and thereby the surface runoff response of a saturated surface to rainfall events, are defined at scales smaller than the scales of typically available DTMs. Understanding surface runoff through modelling studies therefore requires a framework that accounts for this lack of information arising from using coarser resolution DTMs. We demonstrate a linear relationship between MDS values generated from the different resolution DTMs. This opens the possibility of using empirical scaling relationships between high- and lower-resolution DTMs to account for microtopography. Repetition of our measurements on similar surfaces would contribute to establishing such empirical scaling relationships. Our results should be seen as indicative of flat landscapes and surfaces where centimetre scale microtopography is relevant.

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

Isomalto/malto-polysaccharides (IMMPs) are a novel type of soluble dietary fibres with a prebiotic potential promoting growth of beneficial microbes in the gut. However, the mode of action of IMMPs remains unknown. Previous studies on IMMPs showed an increase in total bacteria, especially lactobacilli, and higher production of short chain fatty acids (SCFA) when IMMPs were fed to rats or used during in vitro fermentation. Here we used metatranscriptomics to investigate how IMMPs with different amounts of α − (1 → 6) glycosidic linkages affected microbial function during incubation with human fecal inoculum. We showed that active microbial community dynamics during fermentation varied depending on the type of IMMP used and that the observed changes were reflected in the community gene expression profiles. Based on metatranscriptome analysis, members of Bacteroides, Lactobacillus and Bifidobacterium were the predominant degraders of IMMPs, and the increased gene expression in these bacteria correlated with high amounts of α − (1 → 6) glycosidic linkages. We also noted an increase in relative abundance of these bacteria and an activation of pathways involved in SCFA synthesis. Our findings could provide a baseline for more targeted approaches in designing prebiotics for specific bacteria and to achieve more controlled modulation of microbial activity towards desired health outcomes.

Genome scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses including Flux variability, Reaction deletion, Objective function, and Gene deletion to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community. ### Competing Interest Statement The authors have declared no competing interest.

The fermentative model yeast Saccharomyces cerevisiae has been extensively used to study the genetic basis of stress response and homeostasis. In this study, we performed quantitative trait loci (QTL) analysis of the high-temperature fermentation trait of the progeny from the mating of the S. cerevisiae natural isolate BCC39850 (haploid#17) and the laboratory strain CEN.PK2-1C. A single QTL on chromosome X was identified, encompassing six candidate genes (GEA1, PTK2, NTA1, NPA3, IRT1, and IML1). The functions of these candidates were tested by reverse genetic experiments. Deletion mutants of PTK2, NTA1, and IML1 showed growth defects at 42 °C. The PTK2 knock-out mutant also showed significantly reduced ethanol production and plasma membrane H+ ATPase activity and increased sensitivity to acetic acid, ethanol, amphotericin B (AMB), and β-1,3-glucanase treatment. The CRISPR-Cas9 system was used to construct knock-in mutants by replacement of PTK2, NTA1, IML1, and NPA3 genes with BCC39850 alleles. The PTK2 and NTA1 knock-in mutants showed increased growth and ethanol production titers at 42 °C. These findings suggest an important role for the PTK2 serine/threonine protein kinase in regulating plasma membrane H+ ATPase activity and the NTA1 N-terminal amidase in protein degradation via the ubiquitin-proteasome system machinery, which affects tolerance to heat stress in S. cerevisiae.

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins

Aspergillus niger is a robust microbial cell factory for organic acid production. However, the regulation of many industrially important pathways is still poorly understood. The regulation of the glucose oxidase (Gox) expression system, involved in the biosynthesis of gluconic acid, has recently been uncovered. The results of that study show hydrogen peroxide, a by-product of the extracellular conversion of glucose to gluconate, has a pivotal role as a signaling molecule in the induction of this system. In this study, the facilitated diffusion of hydrogen peroxide via aquaporin water channels (AQPs) was studied. AQPs are transmembrane proteins of the major intrinsic proteins (MIPs) superfamily. In addition to water and glycerol, they may also transport small solutes such as hydrogen peroxide. The genome sequence of A. niger N402 was screened for putative AQPs. Seven AQPs were found and could be classified into three main groups. One protein (AQPA) belonged to orthodox AQP, three (AQPB, AQPD, and AQPE) were grouped in aquaglyceroporins (AQGP), two (AQPC and AQPF) were in X-intrinsic proteins (XIPs), and the other (AQPG) could not be classified. Their ability to facilitate diffusion of hydrogen peroxide was identified using yeast phenotypic growth assays and by studying AQP gene knock-outs in A. niger. The X-intrinsic protein AQPF appears to play roles in facilitating hydrogen peroxide transport across the cellular membrane in both Saccharomyces cerevisiae and A. niger experiments.

Discoloured drinking water, caused by elevated concentrations of organic and inorganic particles, is unacceptable. It occurs due to accumulation and subsequent mobilisation of material from within drinking water distribution infrastructure. Discolouration is currently partially explained by either the theories of cohesive layers or gravitational sedimentation. It is proposed and shown here how the processes behind these two theories both occur and how to integrate them to better explain observed behaviour and inform operational interventions to reduce discolouration. Deficiencies in understanding regarding the process and factors that influence material accumulation are highlighted. Future research addressing these deficiencies will enable determination of long term sustainable management strategies balancing capital investment and operational maintenance to safeguard distribution of high quality drinking water.

Background The Life sciences is an interdisciplinary field of research and one of the the biggest suppliers of scientific data. Reusing and connecting this data can uncover hidden insights and lead to new concepts, provided there is machine-actionable metadata available about the scientific experiments conducted with a degree of completeness that reflect the FAIR guiding principles. While stakeholders have embraced the FAIR principles, in practice there are a limited number of easy to adopt practical implementations available that fulfil the needs of data producers. Findings We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It uses the ISA metadata framework and metadata standards to capture experimental metadata. The FAIR Data Station metadata registration workflow consists of three main modules. Based on the minimal information checklist(s) selected by the user, a web-based “form generation module” creates a standardized metadata template Excel workbook which is used as a familiar environment for offline sample metadata registration. A web-based “validation module” checks the format of the metadata recorded in the workbook. The “resource module” subsequently exports the validated set of recorded metadata into an RDF data file, enabling (cross-project) meta data searches. Conclusions Turning FAIR into reality requires the availability of easy to adopt data FAIRification workflows that provide immediate beneficial incentives to the individual researcher. As such the FAIR Data Station provides in addition to the means to correctly FAIRify sequence data, the means to build searchable databases of (local) projects and can assists in ENA metadata submission of sequence data. The FAIR Data Station is available at http://fairbydesign.nl .

To understand the host-gut microbial relationship, we used multidisciplinary platforms, metagenome, transcriptome, and metabolome analyses, to determine shrimp and intestinal microbial interactions upon 0, 6, 12, 24, and 48 h exposure to 107 CFU/mL of Vibrio harveyi, a shrimp pathogen. The bacterial communities in the intestine of shrimp were disrupted during exposure to V. harveyi. The abundance of Vibrio ASVs in the Harveyi and Vulnificus clades was significantly increased after exposure to the pathogen (6, 12, and 24 h) and decreased later after 48 h. Conversely, Pseudoalteromonas was found in lower abundance (6, 12, and 24 h) but significantly increased after 48 h of the bacterial challenge. Gene expression analysis revealed that genes belonging to several immune-related pathways, including the Toll pathway, the immune deficiency (IMD) pathway, and pattern recognition proteins (PRPs), were significantly upregulated. Early responses in the first 6 h after exposure were the genes involved in phagocytosis, pattern recognition proteins (PRPs), and signal transduction (spa center dot tzle and ankyrin). Late responses (12-48 h) were genes related to proteinases and proteinase inhibitors (PPIs), antimicrobial peptides (AMPs), and oxidative stress. Genes related to lipid metabolisms such as fatty acid metabolism and choline metabolism were also upregulated. Metabolomics analysis also showed an increase in phospholipids, including phosphocholine groups, in the intestine of shrimp exposed to the pathogen. Taken together, the gene expression and metabolomics analyses suggest the importance of lipid metabolism in the defense mechanism against bacterial invasion. Moreover, our metabolomic analysis showed a decrease in tryptophan and indole-3acrylic acid, metabolites related to intestinal immune homeostasis, after bacterial infection. Our observations suggest that pathogenic Vibrio disrupted biological processes in the shrimp intestine, resulting in a decrease in indole-3-acrylic acid and its derivatives which in turn compromised intestinal immunity. In addition, shrimp responded to the bacterial invasion by activating metabolites related to eicosanoid and phospholipid biosynthesis. Our findings on the interactions between shrimp and intestinal microbiota will be an indispensable gateway to systems biology to better understand the function of shrimp gut microbiota and its influence on shrimp innate immunity.

Lactic acid (LA) is a promising bio-based chemical that has broad applications in food, nutraceutical, and bioplastic industries. However, production of the D-form of LA (D-LA) from fermentative organisms is lacking. In this study, Saccharomyces cerevisiae harboring the D-lactate dehydrogenase (DLDH) gene from Leuconostoc mesenteroides was constructed (CEN.PK2_DLDH). To increase D-LA production, the CRISPR/Cas12a system was used for the deletion of gpd1, gpd2, and adh1 to minimize glycerol and ethanol production. Although an improved D-LA titer was observed for both CEN.PK2_DLDHΔgpd and CEN.PK2_DLDHΔgpdΔadh1, growth impairment was observed. To enhance the D-LA productivity, CEN.PK2_DLDHΔgpd was crossed with the weak acid-tolerant S. cerevisiae BCC39850. The isolated hybrid2 showed a maximum D-LA concentration of 23.41 ± 1.65 g/L, equivalent to the improvement in productivity and yield by 2.2 and 1.5 folds, respectively. The simultaneous saccharification and fermentation using alkaline pretreated sugarcane bagasse by the hybrid2 led to an improved D-LA conversion yield on both the washed solid and whole slurry (0.33 and 0.24 g/g glucan). Our findings show the exploitation of natural yeast diversity and the potential strategy of gene editing combined with conventional breeding on improving the performance of S. cerevisiae for the production of industrially potent products.

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.