Yeast microarrays for genome wide parallel genetic and gene expression analysis

Design and characterization of allantoin-inducible expression systems in budding yeast

BACKGROUND

Saccharomyces cerevisiae has been extensively employed as a host for the production of various biochemicals and recombinant proteins. The expression systems employed in S. cerevisiae typically rely on constitutive or galactose-regulated promoters, and the limited repertoire of gene expression regulations imposes constraints on the productivity of microbial cell factories based on budding yeast.

RESULTS

In this study, we designed and characterized a series of allantoin-inducible expression systems based on the endogenous allantoin catabolic system (DAL-related genes) in S. cerevisiae. We first characterized the expression profile of a set of DAL promoters induced by allantoin, and further combined with the galactose-inducible (GAL) system to create a highly responsive genetic switch that efficiently amplifies the output signals. The resulting allantoin-GAL system could give a ON/OFF ratio of 68.6, with 6.8-fold higher signal output over that of direct PDAL2-controlled gene expression. Additionally, when a centromeric plasmid was used for EGFP expression, the ON/OFF ratio was increased to > 67.2, surpassing the EGFP expression levels driven by the DAL2 promoter. Subsequently, we successfully demonstrated that allantoin-GAL system can be used to effectively regulate carotenoid production and cell flocculation in S. cerevisiae.

CONCLUSIONS

In summary, we characterized several allantoin-inducible DAL promoters from budding yeast and further developed a layered allantoin-GAL system that utilizes the DAL2 promoter to regulate the galactose regulon in budding yeast. The resulting allantoin-GAL system could give an impressive ON/OFF ratio that surpassed the traditional PDAL2-controlled gene expression. It is anticipated that utilizing our allantoin-inducible system in budding yeast with allantoin as the alternative nitrogen source might favor the low-cost production of biochemicals and pharmaceuticals.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Transcriptomic changes in single yeast cells under various stress conditions

BACKGROUND

The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method.

RESULTS

We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.

CONCLUSION

Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.

Saccharomyces cerevisiae as a Model System for Eukaryotic Cell Biology, from Cell Cycle Control to DNA Damage Response

The yeast Saccharomyces cerevisiae has been used for bread making and beer brewing for thousands of years. In addition, its ease of manipulation, well-annotated genome, expansive molecular toolbox, and its strong conservation of basic eukaryotic biology also make it a prime model for eukaryotic cell biology and genetics. In this review, we discuss the characteristics that made yeast such an extensively used model organism and specifically focus on the DNA damage response pathway as a prime example of how research in S. cerevisiae helped elucidate a highly conserved biological process. In addition, we also highlight differences in the DNA damage response of S. cerevisiae and humans and discuss the challenges of using S. cerevisiae as a model system.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org.

CRISPR-Cas-Mediated Tethering Recruits the Yeast HMR Mating-Type Locus to the Nuclear Periphery but Fails to Silence Gene Expression

To investigate the relationship between genome structure and function, we have developed a programmable CRISPR-Cas system for nuclear peripheral recruitment in yeast. We benchmarked this system at the HMR and GAL2 loci, both of which are well-characterized model systems for localization to the nuclear periphery. Using microscopy and gene silencing assays, we demonstrate that CRISPR-Cas-mediated tethering can recruit the HMR locus but does not detectably silence reporter gene expression. A previously reported Gal4-mediated tethering system does silence gene expression, and we demonstrate that the silencing effect has an unexpected dependence on the properties of the protein tether. The CRISPR-Cas system was unable to recruit GAL2 to the nuclear periphery. Our results reveal potential challenges for synthetic genome structure perturbations and suggest that distinct functional effects can arise from subtle structural differences in how genes are recruited to the periphery.

Genetic interaction between RLM1 and F-box motif encoding gene SAF1 contributes to stress response in Saccharomyces cerevisiae

BACKGROUND

Stress response is mediated by the transcription of stress-responsive genes. The F-box motif protein Saf1p is involved in SCF-E3 ligase mediated degradation of the adenine deaminase, Aah1p upon nutrient stress. The four transcription regulators, BUR6, MED6, SPT10, SUA7, are listed for SAF1 in the genome database of Saccharomyces cerevisiae. Here in this study, we carried out an in-silico analysis of gene expression and transcription factor databases to understand the regulation of SAF1 expression during stress for hypothesis and experimental analysis.

RESULT

An analysis of the GEO profile database indicated an increase in SAF1 expression when cells were treated with stress agents such as Clioquinol, Pterostilbene, Gentamicin, Hypoxia, Genotoxic, desiccation, and heat. The increase in expression of SAF1 during stress conditions correlated positively with the expression of RLM1, encoding the Rlm1p transcription factor. The expression of AAH1 encoding Aah1p, a Saf1p substrate for ubiquitination, appeared to be negatively correlated with the expression of RLM1 as revealed by an analysis of the Yeastract expression database. Based on analysis of expression profile and regulatory association of SAF1 and RLM1, we hypothesized that inactivation of both the genes together may contribute to stress tolerance. The experimental analysis of cellular growth response of cells lacking both SAF1 and RLM1 to selected stress agents such as cell wall and osmo-stressors, by spot assay indicated stress tolerance phenotype similar to parental strain however sensitivity to genotoxic and microtubule depolymerizing stress agents.

CONCLUSIONS

Based on in-silico and experimental data we suggest that SAF1 and RLM1 both interact genetically in differential response to genotoxic and general stressors.

RNA imaging in bacteria

The spatiotemporal regulation of gene expression plays an essential role in many biological processes. Recently, several imaging-based RNA labeling and detection methods, both in fixed and live cells, were developed and now enable the study of transcript abundance, localization and dynamics. Here, we review the main single-cell techniques for RNA visualization with fluorescence microscopy and describe their applications in bacteria.

[New technologies for renal pathology: Transcriptomics on paraffin-embedded fixed tissue]

The development of new high-throughput technologies in genomics and then in transcriptomics has modified clinical approach in nephrology. At the interface between high-throughput technologies (microarray, new generation sequencing «NGS») and few mRNA analysis (reverse transcriptase quantitative PCR [RT-qPCR]), the nCounter® of NanoString® offers a new and complementary approach. Capable of analyzing formalin-fixed paraffin-embedded samples, this technology is a credible candidate for implanting transcriptomics in clinical routine.

CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation

Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

Results

We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log₂ transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.

Availability and implementation

CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.

Supplementary information

Supplementary data are available at Bioinformatics online.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift

The yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run, data-independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement, and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source-dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system readout pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation of targets of the GID E3 ligase. Moreover, this approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole-proteome-based readouts can serve as comprehensive systems-level assays in all cellular systems.

Diversity of Oligopeptide Transport in Yeast and Its Impact on Adaptation to Winemaking Conditions

Nitrogen is an essential nutrient for yeasts and its relative abundance is an important modulator of fermentation kinetics. The main sources of nitrogen in food are ammonium and free amino acids, however, secondary sources such as oligopeptides are also important contributors to the nitrogen supply. In yeast, oligopeptide uptake is driven by different families of proton–coupled transporters whose specificity depends on peptide length. Proton-dependent Oligopeptide Transporters (POT) are specific to di- and tri-peptides, whereas the Oligopeptide Transport (OPT) family members import tetra- and pentapeptides. Recently, the novel family of Fungal Oligopeptide Transporters (FOT) has been identified in Saccharomyces cerevisiae wine strains as a result of a horizontal gene transfer from Torulaspora microellipsoides. In natural grape must fermentations with S. cerevisiae, Fots have a broader range of oligopeptide utilization in comparison with non-Fot strains, leading to higher biomass production and better fermentation efficiency. In this review we present the current knowledge on the diversity of oligopeptide transporters in yeast, also discussing how the consumption of oligopeptides provides an adaptive advantage to yeasts within the wine environment.

Genome-scale transcriptional dynamics and environmental biosensing

Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisely react to their environment. The importance of complex feedback in controlling the real-time response to external stimuli has led to a need for the next generation of cell-based technologies that enable both the collection and analysis of high-throughput temporal data. Toward this end, we have developed a microfluidic platform capable of monitoring temporal gene expression from over 2,000 promoters. By coupling the "Dynomics" platform with deep neural network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how machine learning can be harnessed to assess patterns in transcriptional data on a genome scale and identify which genes contribute to these patterns. Furthermore, we demonstrate the utility of the Dynomics platform as a field-deployable real-time biosensor through prediction of the presence of heavy metals in urban water and mine spill samples, based on the the dynamic transcription profiles of 1,807 unique Escherichia coli promoters.

Big data in yeast systems biology

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Gene Expression of Haloferax volcanii on Intermediate and Abundant Sources of Fixed Nitrogen

Haloferax volcanii, a well-developed model archaeon for genomic, transcriptomic, and proteomic analyses, can grow on a defined medium of abundant and intermediate levels of fixed nitrogen. Here we report a global profiling of gene expression of H. volcanii grown on ammonium as an abundant source of fixed nitrogen compared to l-alanine, the latter of which exemplifies an intermediate source of nitrogen that can be obtained from dead cells in natural habitats. By comparing the two growth conditions, 30 genes were found to be differentially expressed, including 16 genes associated with amino acid metabolism and transport. The gene expression profiles contributed to mapping ammonium and l-alanine usage with respect to transporters and metabolic pathways. In addition, conserved DNA motifs were identified in the putative promoter regions and transcription factors were found to be in synteny with the differentially expressed genes, leading us to propose regulons of transcriptionally co-regulated operons. This study provides insight to how H. volcanii responds to and utilizes intermediate vs. abundant sources of fixed nitrogen for growth, with implications for conserved functions in related halophilic archaea.

Temperature preference can bias parental genome retention during hybrid evolution

Interspecific hybridization can introduce genetic variation that aids in adaptation to new or changing environments. Here, we investigate how hybrid adaptation to temperature and nutrient limitation may alter parental genome representation over time. We evolved Saccharomyces cerevisiae x Saccharomyces uvarum hybrids in nutrient-limited continuous culture at 15°C for 200 generations. In comparison to previous evolution experiments at 30°C, we identified a number of responses only observed in the colder temperature regime, including the loss of the S. cerevisiae allele in favor of the cryotolerant S. uvarum allele for several portions of the hybrid genome. In particular, we discovered a genotype by environment interaction in the form of a loss of heterozygosity event on chromosome XIII; which species' haplotype is lost or maintained is dependent on the parental species' temperature preference and the temperature at which the hybrid was evolved. We show that a large contribution to this directionality is due to a temperature dependent fitness benefit at a single locus, the high affinity phosphate transporter gene PHO84. This work helps shape our understanding of what forces impact genome evolution after hybridization, and how environmental conditions may promote or disfavor the persistence of hybrids over time.

Analysis of the Teashirt Target Genes in Ureteric Bud Development

Microarrays and RNA-seq (RNA sequencing) are powerful techniques to assess transcript abundance in biological samples and to improve our understanding of the relationship between genotype and phenotype. Tshz3^+/- heterozygous mouse is a model for a human 19q12 syndrome characterized by autistic traits and renal tract defects (Caubit et al., Nat Genet 48:1359-1369, 2016). To unravel renal tract pathological mechanisms, we took advantage of Tshz3 mouse and performed comparative genome-wide expression profiling on embryonic ureter and/or kidney.

Physiological and transcriptomic response of Saccharomyces pastorianus to cold storage

Removal of yeast biomass at the end of fermentation, followed by a period of storage before re-inoculation into a subsequent fermentation, is common in the brewing industry. Storage is typically conducted at cold temperatures to preserve yeast quality, a practice which has unfavourable cost and environmental implications. To determine the potential for alleviating these effects, the transcriptomic and physiological response of Saccharomyces pastorianus strain W34/70 to standard (4°C) and elevated (10°C) storage temperatures was explored. Higher temperatures resulted in increased expression of genes associated with the production and mobilisation of intracellular glycogen, trehalose, glycerol and fatty acids, although these observations were limited to early stages of storage. Intracellular trehalose and glycerol concentrations were higher at 4°C than at 10°C, as a consequence of the cellular response to cold stress. However, significant changes in glycogen degradation or cellular fatty acid composition did not occur between the two sets of populations, ensuring that cell viability remained consistent. It is anticipated that this data may lead to changes in standard practice for handling yeast cultures, without compromising yeast quality. This work has significance not only for the brewing industry, but also for food and biofuel sectors requiring short-term storage of liquid yeast.

Progress in the genomics and genome-wide study of sake yeast

Completion of the whole genome sequence of a laboratory yeast strain Saccharomyces cerevisiae in 1996 ushered in the development of genome-wide experimental tools and accelerated subsequent genetic study of S. cerevisiae. The study of sake yeast also shared the benefit of such tools as DNA microarrays, gene disruption-mutant collections, and others. Moreover, whole genome analysis of representative sake yeast strain Kyokai no. 7 was performed in the late 2000s, and enabled comparative genomics between sake yeast and laboratory yeast, resulting in some notable finding for of sake yeast genetics. Development of next-generation DNA sequencing and bioinformatics also drastically changed the field of the genetics, including for sake yeast. Genomics and the genome-wide study of sake yeast have progressed under these circumstances during the last two decades, and are summarized in this article. Abbreviations: AFLP: amplified fragment length polymorphism; CGH: comparative genomic hybridization; CNV: copy number variation; DMS: dimethyl succinate; DSW: deep sea water; LOH: loss of heterozygosity; NGS: next generation sequencer; QTL: quantitative trait loci; QTN: quantitative trait nucleotide; SAM: S-adenosyl methionine; SNV: single nucleotide variation.

GENETICS OF LARGE PIGMENT EPITHELIAL DETACHMENTS IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION

PURPOSE

We hypothesized that severe forms of neovascular age-related macular degeneration (AMD) such as large pigment epithelial detachments poorly responding to anti-vascular endothelial growth factor therapy might present a distinct genotype compared with overall series of neovascular AMD.

METHODS

This is a multicenter genetic association study. Sixty-eight patients presenting pigment epithelial detachments resistant to ranibizumab (issued from ARI2 study, register number NCT02157077 on clinicaltrials.gov) were compared with two series of patients derived from previously published clinical studies, presenting neovascular AMD (NAT2 study n = 300 and PHRC study n = 1,127), and with healthy controls (n = 441). The phenotype of neovascular AMD groups was based on visual acuity measurement, fundus examination, spectral-domain optical coherence tomography, and angiographic data. All samples were genotyped for three single-nucleotide polymorphisms: CFH (rs1061170), ARMS2 (rs10490924), and C3 (rs2230199). Significant difference in allele frequency between participants with neovascular AMD and control was the main outcome measurement.

RESULTS

The GG genotype of the C3 rs2230199 was significantly more frequent in the ARI2 group (55.9%) than the PHRC group (6.0%, P < 0.0001; odds ratio = 24.0 [95% confidence interval 10.4-55.0]) and the NAT2 group (5.1%, P < 0.0001; odds ratio = 16.1 [95% confidence interval 5.0-51.9]). The repartition of patients carrying a T allele of the ARMS2 (rs10490924) or patients carrying a C allele of the CFH (rs1061170) was similar in the ARI2 group when compared with the NAT2 and PHRC groups.

CONCLUSION

In our series, the genotype GG of C3 rs2230199 was more significantly associated with the phenotype of large vascularized pigment epithelial detachment poorly responding to anti-vascular endothelial growth factor therapy than in global AMD series.

Microarray Technologies in Fungal Diagnostics

Microarray technologies have been a major research tool in the last decades. In addition they have been introduced into several fields of diagnostics including diagnostics of infectious diseases. Microarrays are highly parallelized assay systems that initially were developed for multiparametric nucleic acid detection. From there on they rapidly developed towards a tool for the detection of all kind of biological compounds (DNA, RNA, proteins, cells, nucleic acids, carbohydrates, etc.) or their modifications (methylation, phosphorylation, etc.). The combination of closed-tube systems and lab on chip devices with microarrays further enabled a higher automation degree with a reduced contamination risk. Microarray-based diagnostic applications currently complement and may in the future replace classical methods in clinical microbiology like blood cultures, resistance determination, microscopic and metabolic analyses as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel nucleic acid based biomarkers. Here I focus an microarray technologies in diagnostics and as research tools, based on nucleic acid-based arrays.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

Gene co-expression analyses: an overview from microarray collections in Arabidopsis thaliana

Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell's inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes' decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.

Interrogating the topological robustness of gene regulatory circuits by randomization

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

Cells are able to robustly carry out their essential biological functions, possibly because of multiple layers of tight regulation via complex, yet well-designed, gene regulatory networks involving a substantial number of genes. State-of-the-art genomics technology has enabled the mapping of these large gene networks, yet it remains a tremendous challenge to elucidate their design principles and the regulatory mechanisms underlying their biological functions such as signal processing and decision-making. One of the key barriers is the absence of accurate kinetics for the regulatory interactions, especially from in vivo experiments. To this end, we have developed a new computational modeling method, Random Circuit Perturbation (RACIPE), to explore the dynamic behaviors of gene regulatory circuits without the requirement of detailed kinetic parameters. RACIPE takes a network topology as the input, and generates an unbiased ensemble of models with varying kinetic parameters. Each model is subjected to simulation, followed by statistical analysis for the ensemble. We tested RACIPE on several gene circuits, and found that the predicted gene expression patterns from all of the models converge to experimentally observed gene state clusters. We expect RACIPE to be a powerful method to identify the role of network topology in determining network operating principles.

Global Fecal and Plasma Metabolic Dynamics Related to Helicobacter pylori Eradication

Background:Helicobacter pylori colonizes the gastric mucosa of more than half of the world's population. There is increasing evidence H. pylori protects against the development of obesity and childhood asthma/allergies in which the development of these diseases coincide with transient dysbiosis. However, the mechanism underlying the association of H. pylori eradication with human metabolic and immunological disorders is not well-established. In this study, we aimed to investigate the local and systemic effects of H. pylori eradication through untargeted fecal lipidomics and plasma metabolomics approaches by liquid chromatography mass spectrometry (LC-MS).

Results: Our study revealed that eradication of H. pylori eradication (i.e., loss of H. pylori and/or H. pylori eradication therapy) changed many global metabolite/lipid features, with the majority being down-regulated. Our findings primarily show that H. pylori eradication affects the host energy and lipid metabolism which may eventually lead to the development of metabolic disorders.

Conclusion: These predictive metabolic signatures of metabolic and immunological disorders following H. pylori eradication can provide insights into dynamic local and systemic metabolism related to H. pylori eradication in modulating human health.

SAC3B, a central component of the mRNA export complex TREX-2, is required for prevention of epigenetic gene silencing in Arabidopsis

Epigenetic regulation is important for organismal development and response to the environment. Alteration in epigenetic status has been known mostly from the perspective of enzymatic actions of DNA methylation and/or histone modifications. In a genetic screen for cellular factors involved in preventing epigenetic silencing, we isolated an Arabidopsis mutant defective in SAC3B, a component of the conserved TREX-2 complex that couples mRNA transcription with nuleo-cytoplasmic export. Arabidopsis SAC3B dysfunction causes gene silencing at transgenic and endogenous loci, accompanied by elevation in the repressive histone mark H3K9me2 and by reduction in RNA polymerase Pol II occupancy. SAC3B dysfunction does not alter promoter DNA methylation level of the transgene d35S::LUC, although the DNA demethylase ROS1 is also required for d35S::LUC anti-silencing. THP1 and NUA were identified as SAC3B-associated proteins whose mutations also caused d35S::LUC silencing. RNA-DNA hybrid exists at the repressed loci but is unrelated to gene suppression by the sac3b mutation. Genome-wide analyses demonstrated minor but clear involvement of SAC3B in regulating siRNAs and DNA methylation, particularly at a group of TAS and TAS-like loci. Together our results revealed not only a critical role of mRNA-export factors in transcriptional anti-silencing but also the contribution of SAC3B in shaping plant epigenetic landscapes.

Cell type-specific transcriptome profiling in mammalian brains

A mammalian brain contains numerous types of cells. Advances in neuroscience in the past decade allow us to identify and isolate neural cells of interest from mammalian brains. Recent developments in high-throughput technologies, such as microarrays and next-generation sequencing (NGS), provide detailed information on gene expression in pooled cells on a genomic scale. As a result, many novel genes have been found critical in cell type-specific transcriptional regulation. These differentially expressed genes can be used as molecular signatures, unique to a particular class of neural cells. Use of this gene expression-based approach can further differentiate neural cell types into subtypes, potentially linking some of them with neurological diseases. In this article, experimental techniques used to purify neural cells are described, followed by a review on recent microarray- or NGS-based transcriptomic studies of common neural cell types. The future prospects of cell type-specific research are also discussed.

Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the 'liquid biopsy' was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ∼100% sensitivity, ∼91% specificity and ∼96% accuracy. In the blinded test, the signals were classified with ∼91% sensitivity, ∼82% specificity and ∼86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ∼1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays.

Periodic, Quasi-periodic and Chaotic Dynamics in Simple Gene Elements with Time Delays

Regulatory gene circuit motifs play crucial roles in performing and maintaining vital cellular functions. Frequently, theoretical studies of gene circuits focus on steady-state behaviors and do not include time delays. In this study, the inclusion of time delays is shown to entirely change the time-dependent dynamics for even the simplest possible circuits with one and two gene elements with self and cross regulations. These elements can give rise to rich behaviors including periodic, quasi-periodic, weak chaotic, strong chaotic and intermittent dynamics. We introduce a special power-spectrum-based method to characterize and discriminate these dynamical modes quantitatively. Our simulation results suggest that, while a single negative feedback loop of either one- or two-gene element can only have periodic dynamics, the elements with two positive/negative feedback loops are the minimalist elements to have chaotic dynamics. These elements typically have one negative feedback loop that generates oscillations, and another unit that allows frequent switches among multiple steady states or between oscillatory and non-oscillatory dynamics. Possible dynamical features of several simple one- and two-gene elements are presented in details. Discussion is presented for possible roles of the chaotic behavior in the robustness of cellular functions and diseases, for example, in the context of cancer.

Applications of molecular diagnostics for personalized treatment of head and neck cancer: state of the art

Squamous cell carcinomas of the head and neck are the sixth most frequent tumors worldwide. Risk factors are carcinogenic exposure, infection with the human papillomavirus (HPV) and genetic predisposition. Lymph node metastasis in the neck and HPV status are major prognostic factors. There are several important clinical challenges that determine the research agenda in head and neck cancer. The first is more accurate staging, particularly of occult metastatic lymph nodes in the neck. A second challenge is the lack of biomarkers for personalized therapy. There are a number of treatment modalities that can be employed both single and in combination, but at present only site and stage of the tumor are used for treatment planning. Provided here is an overview of the successes and failures of molecular diagnostic approaches that have been and are being evaluated to address these clinical challenges.

Scope and limitations of yeast as a model organism for studying human tissue-specific pathways

Background

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue.

Results

We present a novel framework to systematically quantify the suitability of yeast as a model organism for different human tissues. To this end, we develop a computational method for dissecting the global human interactome into tissue-specific cellular networks. By individually aligning these networks with the yeast interactome, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. Finally, we couple our framework with a novel statistical model to assess the conservation of tissue-specific pathways and infer the overall similarity of each tissue with yeast. We further study each of these subspaces in detail, and shed light on their unique biological roles in the human tissues.

Conclusions

Our framework provides a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. While tissue-selective genes are significantly associated with the onset and development of a number of tissue-specific pathologies, we show that the human-specific subset has even higher association. Consequently, they provide excellent candidates as drug targets for therapeutic interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0253-0) contains supplementary material, which is available to authorized users.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Use of quantum dots as mass and fluorescence labels in microarray biosensing

In this work, we demonstrate the efficacy of a Quantum Dot (QD) mass label strategy to enhance sensitivity in an interferometric technique called interferometric reflectance imaging sensor (IRIS). This biomass detection platform confers the advantage of absolute mass quantification and lower cost, easily implementable equipment. We discuss the advantages of this label when used in parallel with fluorescence detection. QDs represent a unique opportunity to improve sensitivity in both mass-label detection methods due to their large detectable mass, as well as in fluorescence detection, as they fluoresce without quenching. Streptavidin-conjugated QDs (SA-QDs) have been investigated as such a dual-role probe because of their large shape and mass, their 655nm emission peak for fluorescent detection platforms, and their robust insensitivity to photobleaching and quenching. In particular we explored their dual role in a microarrays immunoassay designed to detect antibodies against β-lactoglobulin, a common milk allergen. The SA-QDs formed a large detectable monolayer of 6.2ng/mm(2) in the saturation conditions, a mass signal corroborated by previous studies by Platt et al..

Reverse phase protein arrays in signaling pathways: a data integration perspective

The reverse phase protein array (RPPA) data platform provides expression data for a prespecified set of proteins, across a set of tissue or cell line samples. Being able to measure either total proteins or posttranslationally modified proteins, even ones present at lower abundances, RPPA represents an excellent way to capture the state of key signaling transduction pathways in normal or diseased cells. RPPA data can be combined with those of other molecular profiling platforms, in order to obtain a more complete molecular picture of the cell. This review offers perspective on the use of RPPA as a component of integrative molecular analysis, using recent case examples from The Cancer Genome Altas consortium, showing how RPPA may provide additional insight into cancer besides what other data platforms may provide. There also exists a clear need for effective visualization approaches to RPPA-based proteomic results; this was highlighted by the recent challenge, put forth by the HPN-DREAM consortium, to develop visualization methods for a highly complex RPPA dataset involving many cancer cell lines, stimuli, and inhibitors applied over time course. In this review, we put forth a number of general guidelines for effective visualization of complex molecular datasets, namely, showing the data, ordering data elements deliberately, enabling generalization, focusing on relevant specifics, and putting things into context. We give examples of how these principles can be utilized in visualizing the intrinsic subtypes of breast cancer and in meaningfully displaying the entire HPN-DREAM RPPA dataset within a single page.

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease

Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.

Genetic Approaches to Facilitate Antibacterial Drug Development

Very few chemically novel agents have been approved for antibacterial chemotherapies during the last 50 yr. Yet new antibacterial drugs are needed to reduce the impact on global health of an increasing number of drug-resistant infections, including highly drug-resistant forms of tuberculosis. This review discusses how genetic approaches can be used to study the mechanism of action of whole-cell screening hits and facilitate target-driven strategies for antimicrobial drug development.

Integrated RNA- and protein profiling of fermentation and respiration in diploid budding yeast provides insight into nutrient control of cell growth and development

Diploid budding yeast undergoes rapid mitosis when it ferments glucose, and in the presence of a non-fermentable carbon source and the absence of a nitrogen source it triggers sporulation. Rich medium with acetate is a commonly used pre-sporulation medium, but our understanding of the molecular events underlying the acetate-driven transition from mitosis to meiosis is still incomplete. We identified 263 proteins for which mRNA and protein synthesis are linked or uncoupled in fermenting and respiring cells. Using motif predictions, interaction data and RNA profiling we find among them 28 likely targets for Ume6, a subunit of the conserved Rpd3/Sin3 histone deacetylase-complex regulating genes involved in metabolism, stress response and meiosis. Finally, we identify 14 genes for which both RNA and proteins are detected exclusively in respiring cells but not in fermenting cells in our sample set, including CSM4, SPR1, SPS4 and RIM4, which were thought to be meiosis-specific. Our work reveals intertwined transcriptional and post-transcriptional control mechanisms acting when a MATa/α strain responds to nutritional signals, and provides molecular clues how the carbon source primes yeast cells for entering meiosis.

BIOLOGICAL SIGNIFICANCE

Our integrated genomics study provides insight into the interplay between the transcriptome and the proteome in diploid yeast cells undergoing vegetative growth in the presence of glucose (fermentation) or acetate (respiration). Furthermore, it reveals novel target genes involved in these processes for Ume6, the DNA binding subunit of the conserved histone deacetylase Rpd3 and the co-repressor Sin3. We have combined data from an RNA profiling experiment using tiling arrays that cover the entire yeast genome, and a large-scale protein detection analysis based on mass spectrometry in diploid MATa/α cells. This distinguishes our study from most others in the field-which investigate haploid yeast strains-because only diploid cells can undergo meiotic development in the simultaneous absence of a non-fermentable carbon source and nitrogen. Indeed, we report molecular clues how respiration of acetate might prime diploid cells for efficient spore formation, a phenomenon that is well known but poorly understood.