Temporal dynamics and metagenomics of phosphorothioate epigenomes in the human gut microbiome.

Background: Epigenetic regulation of gene expression and host defense is well established in microbial communities, with dozens of DNA modifications comprising the epigenomes of prokaryotes and bacteriophage. Phosphorothioation (PT) of DNA, in which a chemically-reactive sulfur atom replaces a non-bridging oxygen in the sugar-phosphate backbone, is catalyzed by dnd and ssp gene families widespread in bacteria and archaea. However, little is known about the role of PTs or other microbial epigenetic modifications in the human microbiome. Here we optimized and applied fecal DNA extraction, mass spectrometric, and metagenomics technologies to characterize the landscape and temporal dynamics of gut microbes possessing PT modifications. Results: Exploiting the nuclease-resistance of PTs, mass spectrometric analysis of limit digests of PT-containing DNA reveals PT dinucleotides as part of genomic consensus sequences, with 16 possible dinucleotide combinations. Analysis of mouse fecal DNA revealed a highly uniform spectrum of 11 PT dinucleotides in all littermates, with PTs estimated to occur in 5-10% of gut microbes. Though at similar levels, PT dinucleotides in fecal DNA from 11 healthy humans possessed signature combinations and levels of individual PTs. Comparison with a widely distributed microbial epigenetic mark, m6dA, suggested temporal dynamics consistent with expectations for gut microbial communities based on Taylor's Power Law. Application of PT-seq for site-specific metagenomic analysis of PT-containing bacteria in one fecal donor revealed the larger consensus sequences for the PT dinucleotides in Bacteroidota, Firmicutes, Actinobacteria, and Proteobacteria, which differed from unbiased metagenomics and suggested that the abundance of PT-containing bacteria did not simply mirror the spectrum of gut bacteria. PT-seq further revealed low abundance PT sites not detected as dinucleotides by mass spectrometry, attesting to the complementarity of the technologies. Conclusions: The results of our studies provide a benchmark for understanding the behavior of an abundant and chemically-reactive epigenetic mark in the human gut microbiome, with implications for inflammatory conditions of the gut.

Application of unimodal probability distribution models for morphological phenotyping of budding yeast.

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

Discovery adductomics provides a comprehensive portrait of tissue-, age- and sex-specific DNA modifications in rodents and humans.

DNA damage causes genomic instability underlying many diseases, with traditional analytical approaches providing minimal insight into the spectrum of DNA lesions in vivo. Here we used untargeted chromatography-coupled tandem mass spectrometry-based adductomics (LC-MS/MS) to begin to define the landscape of DNA modifications in rat and human tissues. A basis set of 114 putative DNA adducts was identified in heart, liver, brain, and kidney in 1-26-month-old rats and 111 in human heart and brain by 'stepped MRM' LC-MS/MS. Subsequent targeted analysis of these species revealed species-, tissue-, age- and sex-biases. Structural characterization of 10 selected adductomic signals as known DNA modifications validated the method and established confidence in the DNA origins of the signals. Along with strong tissue biases, we observed significant age-dependence for 36 adducts, including N2-CMdG, 5-HMdC and 8-Oxo-dG in rats and 1,N6-ϵdA in human heart, as well as sex biases for 67 adducts in rat tissues. These results demonstrate the potential of adductomics for discovering the true spectrum of disease-driving DNA adducts. Our dataset of 114 putative adducts serves as a resource for characterizing dozens of new forms of DNA damage, defining mechanisms of their formation and repair, and developing them as biomarkers of aging and disease.

Targeted Mutations Produce Divergent Characteristics in Pedigreed Sake Yeast Strains.

Modification of the genetic background and, in some cases, the introduction of targeted mutations can play a critical role in producing trait characteristics during the breeding of crops, livestock, and microorganisms. However, the question of how similar trait characteristics emerge when the same target mutation is introduced into different genetic backgrounds is unclear. In a previous study, we performed genome editing of AWA1, CAR1, MDE1, and FAS2 on the standard sake yeast strain Kyokai No. 7 to breed a sake yeast with multiple excellent brewing characteristics. By introducing the same targeted mutations into other pedigreed sake yeast strains, such as Kyokai strains No. 6, No. 9, and No. 10, we were able to create sake yeasts with the same excellent brewing characteristics. However, we found that other components of sake made by the genome-edited yeast strains did not change in the exact same way. For example, amino acid and isobutanol contents differed among the strain backgrounds. We also showed that changes in yeast cell morphology induced by the targeted mutations also differed depending on the strain backgrounds. The number of commonly changed morphological parameters was limited. Thus, divergent characteristics were produced by the targeted mutations in pedigreed sake yeast strains, suggesting a breeding strategy to generate a variety of sake yeasts with excellent brewing characteristics.

Assignment of unimodal probability distribution models for quantitative morphological phenotyping.

BACKGROUND: Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. RESULTS: Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box-Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. CONCLUSIONS: Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets.

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

Defining Functions of Mannoproteins in Saccharomyces cerevisiae by High-Dimensional Morphological Phenotyping.

Mannoproteins are non-filamentous glycoproteins localized to the outermost layer of the yeast cell wall. The physiological roles of these structural components have not been completely elucidated due to the limited availability of appropriate tools. As the perturbation of mannoproteins may affect cell morphology, we investigated mannoprotein mutants in Saccharomyces cerevisiae via high-dimensional morphological phenotyping. The mannoprotein mutants were morphologically classified into seven groups using clustering analysis with Gaussian mixture modeling. The pleiotropic phenotypes of cluster I mutant cells (ccw12Δ) indicated that CCW12 plays major roles in cell wall organization. Cluster II (ccw14Δ, flo11Δ, srl1Δ, and tir3Δ) mutants exhibited altered mother cell size and shape. Mutants of cluster III and IV exhibited no or very small morphological defects. Cluster V (dse2Δ, egt2Δ, and sun4Δ) consisted of endoglucanase mutants with cell separation defects due to incomplete septum digestion. The cluster VI mutant cells (ecm33Δ) exhibited perturbation of apical bud growth. Cluster VII mutant cells (sag1Δ) exhibited differences in cell size and actin organization. Biochemical assays further confirmed the observed morphological defects. Further investigations based on various omics data indicated that morphological phenotyping is a complementary tool that can help with gaining a deeper understanding of the functions of mannoproteins.

Genome Editing to Generate Sake Yeast Strains with Eight Mutations That Confer Excellent Brewing Characteristics.

Sake yeast is mostly diploid, so the introduction of recessive mutations to improve brewing characteristics requires considerable effort. To construct sake yeast with multiple excellent brewing characteristics, we used an evidence-based approach that exploits genome editing technology. Our breeding targeted the AWA1, CAR1, MDE1, and FAS2 genes. We introduced eight mutations into standard sake yeast to construct a non-foam-forming strain that makes sake without producing carcinogens or an unpleasant odor, while producing a sweet ginjo aroma. Small-scale fermentation tests showed that the desired sake could be brewed with our genome-edited strains. The existence of a few unexpected genetic perturbations introduced during breeding proved that genome editing technology is extremely effective for the serial breeding of sake yeast.

Genome editing to generate nonfoam-forming sake yeast strains.

Mutations frequently occur during breeding of sake yeasts and result in unexpected phenotypes. Here, genome editing tools were applied to develop an ideal nonfoam-forming sake yeast strain, K7GE01, which had homozygous awa1∆/awa1∆ deletion alleles that were responsible for nonfoam formation and few off-target mutations. High-dimensional morphological phenotyping revealed no detectable morphological differences between the genome-edited strain and its parent, while the canonical nonfoam-forming strain, K701, showed obvious morphological changes. Small-scale fermentation tests also showed differences between components of sake produced by K7GE01 and K701. The K7GE01 strain produced sake with significant differences in the concentrations of ethyl acetate, malic acid, lactic acid, and acetic acid, while K701 produced sake with more differences. Our results indicated genuine phenotypes of awa1∆/awa1∆ in sake yeast isolates and showed the usefulness of genome editing tools for sake yeast breeding.

Genetic dissection of the signaling pathway required for the cell wall integrity checkpoint.

The cell wall integrity checkpoint monitors synthesis of cell wall materials during the Saccharomyces cerevisiae cell cycle. Upon perturbation of cell wall synthesis, the cell wall integrity checkpoint is activated, downregulating Clb2 transcription. Here, we identified genes involved in this checkpoint by genetic screening of deletion mutants. In addition to the previously identified dynactin complex, the Las17 complex, in particular the Bzz1 and Vrp1 components, plays a role in this checkpoint. We also revealed that the high osmolarity glycerol (HOG) and cell wall integrity mitogen-activated protein kinase (MAPK) signaling pathways are essential for checkpoint function. The defective checkpoint caused by the deficient dynactin and Las17 complexes was rescued by hyperactivation of the cell wall integrity MAPK pathway, but not by the activated form of Hog1, suggesting an order to these signaling pathways. Mutation of Fkh2, a transcription factor important for Clb2 expression, suppressed the checkpoint-defective phenotype of Las17, HOG MAPK and cell wall integrity MAPK mutations. These results provide genetic evidence that signaling from the cell surface regulates the downstream transcriptional machinery to activate the cell wall integrity checkpoint.

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling.

BACKGROUND: The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth. OBJECTIVES: In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies. MATERIALS AND METHODS: Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,'c' and 'σ') of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE). RESULTS: Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method. CONCLUSION: Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of 'c' and 'σ' parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.

Systematic analysis of Ca2+ homeostasis in Saccharomyces cerevisiae based on chemical-genetic interaction profiles.

We investigated the global landscape of Ca2+ homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+ After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+-cls interactions. We found that high-dimensional, morphological Ca2+-cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+-cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+-cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+ homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+ homeostasis.

Plant dehydrins: shedding light on structure and expression patterns of dehydrin gene family in barley.

Dehydrins, an important group of late embryogenesis abundant proteins, accumulate in response to dehydration stresses and play protective roles under stress conditions. Herein, phylogenetic analysis of the dehydrin family was performed using the protein sequences of 108 dehydrins obtained from 14 plant species based on plant taxonomy and protein subclasses. Sub-cellular localization and phosphorylation sites of these proteins were also predicted. The protein features distinguishing these dehydrins categories were identified using various attribute weighting and decision tree analyses. The results revealed that the presence of the S motif preceding the K motif (YnSKn, SKn, and SnKS) was more evident and the YnSKn subclass was more frequent in monocots. In barley, as one of the most drought-tolerant crops, there are ten members of YnSKn out of 13 HvDhns. In promoter regions, six types of abiotic stress-responsive elements were identified. Regulatory elements in UTR sequences of HvDhns were infrequent while only four miRNA targets were found. Furthermore, physiological parameters and gene expression levels of HvDhns were studied in tolerant (HV1) and susceptible (HV2) cultivars, and in an Iranian tolerant wild barley genotype (Spontaneum; HS) subjected to gradual water stress and after recovery duration at the vegetative stage. The results showed the significant impact of dehydration on dry matter, relative leaf water, chlorophyll contents, and oxidative damages in HV2 compared with the other studied genotypes, suggesting a poor dehydration tolerance, and incapability of recovering after re-watering in HV2. Under severe drought stress, among the 13 HvDhns genes, 5 and 10 were exclusively induced in HV1 and HS, respectively. The gene and protein structures and the expression patterns of HvDhns as well as the physiological data consistently support the role of dehydrins in survival and recovery of barley plants from drought particularly in HS. Overall, this information would be helpful for functional characterization of the Dhn family in plants.