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At least 235 records · Page 13

Unveiling a pervasive DNA adenine methylation regulatory network in the early-diverging fungus Rhizopus microsporus

Development of the DNA affinity purification and sequencing (DAP-seq) technique has allowed genome-scale studies of transcription factor (TF)-binding sites with high reproducibility. Here, we apply this technique to the human opportunistic pathogen Rhizopus microsporus, a mucoralean fungus belonging to the understudied group of early-diverging fungi. We characterize genome-wide binding sites of 58 TFs encoded by genes regulated through adenine methylation and representing major TF families. This analysis reveals their binding profiles and recognized sequences, expanding and diversifying the catalog of known fungal motifs. By integrating this data with DNA 6-methyladenine profiling, we uncover the extensive direct and indirect impact of this epigenetic modification on the regulation of gene expression. Furthermore, we use the generated data to identify TFs involved in biologically relevant processes such as zinc metabolism and light response. Our work enhances our understanding of regulatory mechanisms in R. microsporus and provides broader insights into gene regulation across the fungal kingdom.

Lax, Carlos [Universidad de Murcia (Spain)] (ORCID↗

Senseless, a Zn finger transcription factor, is necessary and sufficient for sensory organ development in Drosophila

The senseless (sens) gene is required for proper development of most cell types of the embryonic and adult peripheral nervous system (PNS) of Drosophila. Sens is a nuclear protein with four Zn fingers that is expressed and required in the sensory organ precursors (SOP) for proper proneural gene expression. Ectopic expression of Sens in many ectodermal cells causes induction of PNS external sensory organ formation and is able to recreate an ectopic proneural field. Hence, sens is both necessary and sufficient for PNS development. Our data indicate that proneural genes activate sens expression. Sens is then in turn required to further activate and maintain proneural gene expression. This feedback mechanism is essential for selective enhancement and maintenance of proneural gene expression in the SOPs.

NASA Discipline Developmental Biology↗

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Deletion of the transcriptional coactivator PGC1α in skeletal muscles is associated with reduced expression of genes related to oxidative muscle function

The expression of the transcriptional coactivator PGC1α is increased in skeletal muscles during exercise. Previously, we showed that increased PGC1α leads to prolonged exercise performance (the duration for which running can be continued) and, at the same time, increases the expression of branched-chain amino acid (BCAA) metabolism-related enzymes and genes that are involved in supplying substrates for the TCA cycle. We recently created mice with PGC1α knockout specifically in the skeletal muscles (PGC1α KO mice), which show decreased mitochondrial content. In this study, global gene expression (microarray) analysis was performed in the skeletal muscles of PGC1α KO mice compared with that of wild-type control mice. As a result, decreased expression of genes involved in the TCA cycle, oxidative phosphorylation, and BCAA metabolism were observed. Compared with previously obtained microarray data on PGC1α-overexpressing transgenic mice, each gene showed the completely opposite direction of expression change. Bioinformatic analysis of the promoter region of genes with decreased expression in PGC1α KO mice predicted the involvement of several transcription factors, including a nuclear receptor, ERR, in their regulation. As PGC1α KO microarray data in this study show opposing findings to the PGC1α transgenic data, a loss-of-function experiment, as well as a gain-of-function experiment, revealed PGC1α’s function in the oxidative energy metabolism of skeletal muscles. - Highlights: • Microarray analysis was performed in the skeletal muscle of PGC1α KO mice. • Expression of genes in the oxidative energy metabolism was decreased. • Bioinformatic analysis of promoter region of the genes predicted involvement of ERR. • PGC1α KO microarray data in this study show the mirror image of transgenic data.

60 APPLIED LIFE SCIENCES↗

Expression of dehydroshikimate dehydratase in poplar induces transcriptional and metabolic changes in the phenylpropanoid pathway

Abstract Modification of lignin in feedstocks via genetic engineering aims to reduce biomass recalcitrance to facilitate efficient conversion processes. These improvements can be achieved by expressing exogenous enzymes that interfere with native biosynthetic pathways responsible for the production of the lignin precursors. In planta expression of a bacterial 3-dehydroshikimate dehydratase in poplar trees reduced lignin content and altered the monomer composition, which enabled higher yields of sugars after cell wall polysaccharide hydrolysis. Understanding how plants respond to such genetic modifications at the transcriptional and metabolic levels is needed to facilitate further improvement and field deployment. In this work, we acquired fundamental knowledge on lignin-modified poplar expressing 3-dehydroshikimate dehydratase using RNA-seq and metabolomics. The data clearly demonstrate that changes in gene expression and metabolite abundance can occur in a strict spatiotemporal fashion, revealing tissue-specific responses in the xylem, phloem, or periderm. In the poplar line that exhibited the strongest reduction in lignin, we found that 3% of the transcripts had altered expression levels and ~19% of the detected metabolites had differential abundance in the xylem from older stems. The changes affected predominantly the shikimate and phenylpropanoid pathways as well as secondary cell wall metabolism, and resulted in significant accumulation of hydroxybenzoates derived from protocatechuate and salicylate.

59 BASIC BIOLOGICAL SCIENCES↗

Too dim, too bright, and just right: Systems analysis of the Chlamydomonas diurnal program under limiting and excess light

Photosynthetic organisms coordinate their metabolism and growth with diurnal light, which can range in intensity from limiting to excessive. Little is known about how light intensity impacts the diurnal program in Chlamydomonas reinhardtii, or how diurnal rhythms in gene expression and metabolism shape photoprotective responses at different times of day. To address these questions, we performed a systems analysis of synchronized Chlamydomonas populations acclimated to low, moderate, and high diurnal light. Transcriptomic and proteomic data revealed that the Chlamydomonas rhythmic gene expression program is resilient to limiting and excess light: genome-wide, waves of transcripts, and proteins peak at the same times in populations acclimated to stressful light intensities as in populations acclimated to moderate light. Yet, diurnal photoacclimation gives rise to hundreds of gene expression changes, even at night. Time course measurements of photosynthetic efficiency and pigments responsive to excess light showed that high light-acclimated cells partially overcome photodamage in the latter half of the day prior to cell division. Although gene expression and photodamage are dynamic over the diurnal cycle, Chlamydomonas populations acclimated to low and high diurnal light maintain altered photosystem abundance, thylakoid architecture, and non-photochemical quenching capacity through the night phase. This suggests that cells remember or anticipate the light intensities that they have typically encountered during the day. The integrated data constitute an excellent resource for understanding photoacclimation in eukaryotes under environmentally relevant conditions.

Dupuis, Sunnyjoy↗

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Huckins, Laura M↗

Taxonomic Re-Evaluation and Genomic Comparison of Novel Extracellular Electron Uptake-Capable Rhodovulum visakhapatnamense and Rhodovulum sulfidophilum Isolates

Rhodovulum spp. are anoxygenic phototrophic purple bacteria with versatile metabolisms, including the ability to obtain electrons from minerals in their environment to drive photosynthesis, a relatively novel process called phototrophic extracellular electron uptake (pEEU). A total of 15 strains of Rhodovulum sulfidophilum were isolated from a marine estuary to observe these metabolisms in marine phototrophs. One representative strain, Rhodovulum sulfidophilum strain AB26, can perform phototrophic iron oxidation (photoferrotrophy) and couples carbon dioxide fixation to pEEU. Here, we reclassify two R. sulfidophilum isolates, strainAB26 and strain AB19, as Rhodovulum visakhapatnamense using taxonomic re-evaluation based on 16S and pufM phylogenetic analyses. The strain AB26 genome consists of 4,380,746 base-pairs, including two plasmids, and encodes 4296 predicted protein-coding genes. Strain AB26 contains 22 histidine kinases, 20 response regulators, and dedicates ~16% of its genome to transport. Transcriptomic data under aerobic, photoheterotrophy, photoautotrophy, and pEEU reveals how gene expression varies between metabolisms in a novel R. visakhapatnamense strain. Genome comparison led by transcriptomic data under pEEU reveals potential pEEU-relevant genes both unique to R. visakhapatnamense strains and shared within the R. sulfidophilum genomes. With these data we identify potential pEEU-important transcripts and how speciation may affect molecular mechanisms of pEEU in Rhodovulum species from the same environment.

59 BASIC BIOLOGICAL SCIENCES↗

Analysis of metastasis associated signal regulatory network in colorectal cancer

Metastasis is a key factor that affects the survival and prognosis of colorectal cancer patients. To elucidate molecular mechanism associated with the metastasis of colorectal cancer, genes related to the metastasis time of colorectal cancer were screened. Then, a network was constructed with this genes. Data was obtained from colorectal cancer expression profile. Molecular mechanism elucidated the time of tumor metastasis and the expression of genes related to colorectal cancer. We found that metastasis-promoting and metastasis-inhibiting networks included protein hubs of high connectivity. These protein hubs were components of organelles. Some ribosomal proteins promoted the metastasis of colorectal cancer. In some components of organelles, such as proteasomes, mitochondrial ribosome, ATP synthase, and splicing factors, the metastasis of colorectal cancer was inhibited by some sections of these organelles. After performing survival analysis of proteins in organelles, joint survival curve of proteins was constructed in ribosomal network. This joint survival curve showed metastasis was promoted in patients with colorectal cancer (P = 0.0022939). Joint survival curve of proteins was plotted against proteasomes (P = 7 e–07), mitochondrial ribosome (P = 0.0001157), ATP synthase (P = 0.0001936), and splicing factors (P = 1.35e–05). These curves indicate that metastasis of colorectal cancer can be inhibited. After analyzing proteins that bind with organelle components, we also found that some proteins were associated with the time of colorectal cancer metastasis. Hence, different cellular components play different roles in the metastasis of colorectal cancer.

60 APPLIED LIFE SCIENCES↗

Data for Discovery, Characterization, and Application of Chromosomal Integration Sites for Stable Heterologous Gene Expression in Rhodotorula toruloides

Rhodotorula toruloides is a non-model, oleaginous yeast uniquely suited to produce acetyl-CoA-derived chemicals. However, the lack of well-characterized genomic integration sites has impeded the metabolic engineering of this organism. Here we report a set of computationally predicted and experimentally validated chromosomal integration sites in R. toruloides . We first implemented an in silico platform by integrating essential gene information and transcriptomic data to identify candidate sites that meet stringent criteria. We then conducted a full experimental characterization of these sites, assessing integration efficiency, gene expression levels, impact on cell growth, and long-term expression stability. Among the identified sites, 12 exhibited integration efficiencies of 50% or higher, making them sufficient for most metabolic engineering applications. Using selected high-efficiency sites, we achieved simultaneous double and triple integrations and efficiently integrated long functional pathways (up to 14.7 kb). Additionally, we developed a new inducible marker recycling system that allows multiple rounds of integration at our characterized sites. We validated this system by performing five sequential rounds of GFP integration and three sequential rounds of MaFAR integration for fatty alcohol production, demonstrating, for the first time, precise gene copy number tuning in R. toruloides . These characterized integration sites should significantly advance metabolic engineering efforts and future genetic tool development in R. toruloides .

Conversion↗

Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach

Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.

Jonathan Oribello↗

Comparison of the Transcriptome Response within the Swine Tracheobronchial Lymphnode Following Infection with PRRSV, PCV-2 or IAV-S.

Porcine reproductive and respiratory syndrome virus (PRRSV) is a major respiratory pathogen of swine that has become extremely costly to the swine industry worldwide, often causing losses in production and animal life due to their ease of spread. However, the intracellular changes that occur in pigs following viral respiratory infections are still scantily understood for PRRSV, as well as other viral respiratory infections. The aim of this study was to acquire a better understanding of the PRRS disease by comparing gene expression changes that occur in tracheobronchial lymph nodes (TBLN) of pigs infected with either porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus type 2 (PCV-2), or swine influenza A virus (IAV-S) infections. The study identified and compared gene expression changes in the TBLN of 80 pigs following infection by PRRSV, PCV-2, IAV-S, or sham inoculation. Total RNA was pooled for each group and time-point (1, 3, 6, and 14 dpi) to make 16 libraries—analyses are by Digital Gene Expression Tag Profiling (DGETP). The data underwent standard filtering to generate a list of sequence tag raw counts that were then analyzed using multidimensional and differential expression statistical tests. The results showed that PRRSV, IAV-S and PCV-2 viral infections followed a clinical course in the pigs typical of experimental infection of young pigs with these viruses. Gene expression results echoed this course, as well as uncovered genes related to intersecting and unique host immune responses to the three viruses. By testing and observing the host response to other respiratory viruses, our study has elucidated similarities and differences that can assist in the development of vaccines and therapeutics that shorten or prevent a chronic PRRSV infection.

Miller↗

Spaceflight Environmental-Telemetry Data for Biological Science

There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.

Danielle K. Lopez↗

Multitrait genome–wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits

Genome–wide association studies (GWAS) have great promise for identifying the loci that contribute to adaptive variation, but the complex genetic architecture of many quantitative traits presents a substantial challenge.We measured 14 morphological and physiological traits and identified single nucleotide polymorphism (SNP)–phenotype associations in a Populus trichocarpa population distributed from California, USA to British Columbia, Canada. We used whole–genome resequencing data of 882 trees with more than 6.78 million SNPs, coupled with multitrait association to detect polymorphisms with potentially pleiotropic effects. Candidate genes were validated with functional data.Broad–sense heritability (H2) ranged from 0.30 to 0.56 for morphological traits and 0.08 to 0.36 for physiological traits. In total, 4 and 20 gene models were detected using the single–trait and multitrait association methods, respectively. Several of these associations were corroborated by additional lines of evidence, including co–expression networks, metabolite analyses, and direct confirmation of gene function through RNAi.Multitrait association identified many more significant associations than single–trait association, potentially revealing pleiotropic effects of individual genes. Furthermore, this approach can be particularly useful for challenging physiological traits such as water–use efficiency or complex traits such as leaf morphology, for which we were able to identify credible candidate genes by combining multitrait association with gene co–expression and co–methylation data.

59 BASIC BIOLOGICAL SCIENCES↗

A maize semi-dwarf mutant reveals a GRAS transcription factor involved in brassinosteroid signaling

Brassinosteroids (BR) and gibberellins (GA) regulate plant height and leaf angle in maize (Zea mays). Mutants with defects in BR or GA biosynthesis or signaling identify components of these pathways and enhance our knowledge about plant growth and development. In this study, we characterized three recessive mutant alleles of GRAS transcription factor 42 (gras42) in maize, a GRAS transcription factor gene orthologous to the DWARF AND LOW TILLERING (DLT) gene of rice (Oryza sativa). These maize mutants exhibited semi-dwarf stature, shorter and wider leaves, and more upright leaf angle. Transcriptome analysis revealed a role for GRAS42 as a determinant of BR signaling. Analysis of the expression consequences from loss of GRAS42 in the gras42-mu1021149 mutant indicated a weak loss of BR signaling in the mutant, consistent with its previously demonstrated role in BR signaling in rice. Loss of BR signaling was also evident by the enhancement of weak BR biosynthetic mutant alleles in double mutants of nana plant1-1 and gras42-mu1021149. The gras42-mu1021149 mutant had little effect on GA-regulated gene expression, suggesting that GRAS42 is not a regulator of core GA signaling genes in maize. Single-cell expression data identified gras42 expressed among cells in the G2/M phase of the cell cycle consistent with its previously demonstrated role in cell cycle gene expression in Arabidopsis (Arabidopsis thaliana). Cis-acting natural variation controlling GRAS42 transcript accumulation was identified by expression genome-wide association study (eGWAS) in maize. Our results demonstrate a conserved role for GRAS42/SCARECROW-LIKE 28 (SCL28)/DLT in BR signaling, clarify the role of this gene in GA signaling, and suggest mechanisms of tillering and leaf angle control by BR.

54 ENVIRONMENTAL SCIENCES↗

Primary transcripts: From the discovery of RNA processing to current concepts of gene expression ‐ Review

The main purpose of this review is to recall for investigators - and in particular students -, some of the early data and concepts in molecular genetics and biology that are rarely cited in the current literature and are thus invariably overlooked. There is a growing tendency among editors and reviewers to consider that only data produced in the last 10–20 years or so are pertinent. However this is not the case. In exact science, sound data and lucid interpretation never become obsolete, and even if forgotten, will resurface sooner or later. In the field of gene expression, covered in the present review, recent post-genomic data have indeed confirmed many of the earlier results and concepts developed in the mid-seventies, well before the start of the recombinant DNA revolution. Human brains and even the most powerful computers, have difficulty in handling and making sense of the overwhelming flow of data generated by recent high-throughput technologies. This was easier when low throughput, more integrative methods based on biochemistry and microscopy dominated biological research. Nowadays, the need for organising concepts is ever more important, otherwise the mass of available data can generate only {sup b}uilding ruins{sup }- the bricks without an architect. Concepts such as pervasive transcription of genomes, large genomic domains, full domain transcripts (FDTs) up to 100 kb long, the prevalence of post-transcriptional events in regulating eukaryotic gene expression, and the 3D-genome architecture, were all developed and discussed before 1990, and are only now coming back into vogue. Thus, to review the impact of earlier concepts on later developments in the field, I will confront former and current data and ideas, including a discussion of old and new methods. Whenever useful, I shall first briefly report post-genomic developments before addressing former results and interpretations. Equally important, some of the terms often used sloppily in scientific discussions will be clearly defined. As a basis for the ensuing discussion, some of the issues and facts related to eukaryotic gene expression will first be introduced. In chapter 2 the evolution in perception of biology over the last 60 years and the impact of the recombinant DNA revolution will be considered. Then, in chapter 3 data and theory concerning the genome, gene expression and genetics will be reviewed. The experimental and theoretical definition of the gene will be discussed before considering the 3 different types of genetic information - the {sup T}riad{sup }- and the importance of post-transcriptional regulation of gene expression in the light of the recent finding that 90% of genomic DNA seems to be transcribed. Some previous attempts to provide a conceptual framework for these observations will be recalled, in particular the {sup C}ascade Regulation Hypothesis{sup }(CRH) developed in 1967–85, and the {sup G}ene and Genon{sup }concept proposed in 2007. A knowledge of the size of primary transcripts is of prime importance, both for experimental and theoretical reasons, since these molecules represent the primary units of the {sup R}NA genome{sup }on which most of the post-transcriptional regulation of gene expression occurs. In chapter 4, I will first discuss some current post-genomic topics before summarising the discovery of the high Mr-RNA transcripts, and the investigation of their processing spanning the last 50 years. Since even today, a consensus concerning the real form of primary transcripts in eukaryotic cells has not yet been reached, I will refer to the viral and specialized cellular models which helped early on to understand the mechanisms of RNA processing and differential splicing which operate in cells and tissues. As a well-studied example of expression and regulation of a specific cellular gene in relation to differentiation and pathology, I will discuss the early and recent work on expression of the globin genes in nucleated avian erythroblasts. An important concept is that the primary transcript not only embodies protein-coding information and regulation of its expression, but also the 3D-structure of the genomic DNA from which it was derived. The wealth of recent post-genomic data published in this field emphasises the importance of a fundamental principle of genome organisation and expression that has been overlooked for years even though it was already discussed in the 1970–80ties. These issues are addressed in chapter 5 which focuses on the involvement of the nuclear matrix and nuclear architecture in DNA and RNA biology. This section will make reference to the Unified Matrix Hypothesis (UMH), which was the first molecular model of the 3D organisation of DNA and RNA. The chapter on the {sup R}NA-genome and peripheral memories{sup }discusses experimental data on the ribonucleoprotein complexes containing pre-mRNA (pre-mRNPs) and mRNA (mRNPs) which are organised in nuclear and cytoplasmic spaces respectively. Finally, {sup O}utlook {sup }will enumerate currently unresolved questions in the field, and will propose some ideas that may encourage further investigation, and comprehension of available experimental data still in need of interpretation. In chapter 8, some propositions and paradigms basic to the authors own analysis are discussed. {sup I}n conclusion{sup }the raison d'être of this review is recalled and positioned within the overall framework of scientific endeavour.

60 APPLIED LIFE SCIENCES↗

Toxicological responses of environmental mixtures: Environmental metal mixtures display synergistic induction of metal-responsive and oxidative stress genes in placental cells

Exposure to elevated levels of the toxic metals inorganic arsenic (iAs) and cadmium (Cd) represents a major global health problem. These metals often occur as mixtures in the environment, creating the potential for interactive or synergistic biological effects different from those observed in single exposure conditions. In the present study, environmental mixtures collected from two waste sites in China and comparable mixtures prepared in the laboratory were tested for toxicogenomic response in placental JEG-3 cells. These cells serve as a model for evaluating cellular responses to exposures during pregnancy. One of the mixtures was predominated by iAs and one by Cd. Six gene biomarkers were measured in order to evaluate the effects from the metal mixtures using dose and time-course experiments including: heme oxygenase 1 (HO-1) and metallothionein isoforms (MT1A, MT1F and MT1G) previously shown to be preferentially induced by exposure to either iAs or Cd, and metal transporter genes aquaporin-9 (AQP9) and ATPase, Cu{sup 2+} transporting, beta polypeptide (ATP7B). There was a significant increase in the mRNA expression levels of ATP7B, HO-1, MT1A, MT1F, and MT1G in mixture-treated cells compared to the iAs or Cd only-treated cells. Notably, the genomic responses were observed at concentrations significantly lower than levels found at the environmental collection sites. These data demonstrate that metal mixtures increase the expression of gene biomarkers in placental JEG-3 cells in a synergistic manner. Taken together, the data suggest that toxic metals that co-occur may induce detrimental health effects that are currently underestimated when analyzed as single metals. - Highlights: • Toxicogenomic responses of environmental metal mixtures assessed • Induction of ATP7B, HO-1, MT1A, MT1F and MT1G by metal mixtures observed in placental cells • Higher gene induction in response to metal mixtures versus single metal treatments.

60 APPLIED LIFE SCIENCES↗

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗