Integration Of Transcriptomic Data Research Articles

Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes.

Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large- and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue- and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions. In this study, we comprehensively analyzed the tumor-specific ceRNA fluctuations observed in the three highest-incidence cancers, LUAD, PRAD, and BRCA, compared to healthy lung, prostate, and breast tissues, respectively. Our observations pertaining to tumor-specific competing endogenous RNA (ceRNA) interactions revealed that, in the cases of lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), and breast invasive carcinoma (BRCA), 3,204, 1,233, and 406 ceRNAs, respectively, engage in post-transcriptional intercommunication within tumor tissues, in contrast to their absence in corresponding healthy samples. We also found that 90 ceRNAs are shared by the three cancer types and that these ceRNAs participate in ceRNA interactions in tumor tissues compared to those in normal tissues. Among the 90 ceRNAs that directly interact with miRNAs, we uncovered a core network of 165 miRNAs and 63 ceRNAs that should be considered in RNA-targeted and RNA-mediated approaches in future studies and could be used in these three aggressive cancer types. More specifically, in this core interaction network, ceRNAs such as GALNT7, KLF9, and DAB2 and miRNAs like miR-106a/b-5p, miR-20a-5p, and miR-519d-3p may have potential as common targets in the three critical cancers. In contrast to conventional methods that construct ceRNA networks using differentially expressed genes compared to normal tissues, our proposed approach identifies ceRNA players by considering their context within the ceRNA:miRNA interactions. Our results have the potential to reveal distinct and common ceRNA interactions in cancer types and to pinpoint critical RNAs, thereby paving the way for RNA-based strategies in the battle against cancer.

Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis.

Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.IMPORTANCEThis work presents a detailed reconstruction of the metabolism of Bifidobacterium longum subsp. infantis, a prominent member of the infant gut microbiome, providing a systems view of its metabolism of human milk oligosaccharides.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR’s ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/).

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis.

Cancer-Associated Fibroblast Induces Acinar-to-Ductal Cell Transdifferentiation and Pancreatic Cancer Initiation Via LAMA5/ITGA4 Axis

BACKGROUND & AIMSPancreatic ductal adenocarcinoma (PDAC) is characterized by desmoplastic stroma surrounding most tumors. Activated stromal fibroblasts, namely Cancer-associated fibroblasts (CAFs), play a major role in PDAC progression. Here we analyzed if CAFs influence acinar cells and impact PDAC initiation, namely “Acinar to Ductal Metaplasia” (ADM). ADM connection with PDAC pathophysiology is indicated but not yet established. Hence, we hypothesized that CAF secretome might play a significant role in ADM in PDAC initiation. METHODSMouse and human acinar cell organoids, acinar cells cocultured with CAFs and exposed to CAF-conditioned media (CAF-CM), acinar cell explants, and CAF cocultures, etc., were examined by qRT-PCR, RNA-seq, immunoblotting and confocal microscopy. Data from LC-MS/MS analysis of CAF CM and RNAseq data of acinar cells post-CM exposure were integrated using bioinformatics tools to identify molecular mechanism for CAF-induced ADM. Using confocal microscopy, immunoblotting, and qRT-PCR analysis, we validated the depletion of key signaling axis in cell-line, acinar explant coculture, and mCAFs. RESULTSClose association of acino-ductal (UEA1, Amylase, Ck19) markers and mCAFs (α-SMA) in LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1Cre (KPC) and LSL-KrasG12D/+; Pdx1Cre (KC) autochthonous progression tumor tissue was observed. Caerulein treatment-induced mCAFs increased Ck19 and decreased Amylase in wild-type (WT) and KC pancreas. Likewise, acinar-mCAF cocultures revealed induction of ductal transdifferentiation in cell-line, acinar-organoid, and explant coculture formats in WT and KC mice pancreas. Proteomic and transcriptomic data integration revealed a novel Laminin5/Integrinα4/Stat3 axis responsible for CAF-mediated acinar to ductal cell transdifferentiation. CONCLUSIONResults collectively suggest the first evidence for CAF-influenced acino-ductal phenotypic switchover, thus highlighting tumor micro-environment role in the pancreatic carcinogenesis inception.

TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data.

Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and high mortality rates. It remains a prevailing clinical need to distinguish whether the patient can benefit from therapy, such as chemotherapy. By integrating single-cell and global transcriptome data, we have for the first time identified TCL1A+ B cell functions that are prognostically relevant in TNBC. This finding broadens the perspective of traditional tumor-infiltrating lymphocytes in predicting survival, especially the potential value of B cells in TNBC. Single-cell RNA-seq data from five TNBC patients were collected to identify the association between immune cell populations and clinical outcomes. Functional analysis was according to gene set enrichment analysis using pathways from MsigDB. Subsequently, the gene signature of TCL1A+ B cells based on differential expression genes of TCL1A+ B cells versus other immune cells was used to explore the correlation with tumor microenvironment (TME) and construct a prognostic signature using a non-parametric and unsupervised method. We identified TCL1A+ B cells as a cluster of B cells associated with clinical outcomes in TNBC. Functional analysis demonstrated its function in B cell activation and regulation of immune response. The highly enriched TCL1A+ B cell population was found to be associated with a thermal TME with anti-tumor effects. A high abundance of TCL1A+ B cell population is positively correlated with a favorable therapeutic outcome, as indicated by longer overall survival. The present study suggests that TCL1A+ B cells play a key role in the treatment and prognostic prediction of TNBC, although further studies are needed to validate our findings. Moreover, the integration of transcriptome data at various resolutions provides a viable approach for the discovery of novel prognostic markers.

Response of leaf blade in Zanthoxylum planispinum var. dintanensis to differences altitudes through integrated metabolomics and transcriptomics analysis

To analyze the effects of altitude on Z. planispinum var. Dintanensisin, leaves of samples from different altitudes (H1:1054 m.a.s.l., H2:821 m.a.s.l., H3:645 m.a.s.l.) were collected for integrative analysis of the metabolome and transcriptome in the present study. According to transcriptome data, a total of 9,566 differentially expressed genes (DEGs) were identified from the three sample comparisons (H1 vs. H2, H1 vs. H3, H2 vs. H3). Fewer DEGs were observed in the H2 vs. H3 group than in the other two comparison groups, suggesting that differences in the transcriptome increased with elevation. Most genes were involved in “Biosynthesis of secondary metabolites”, “Plant hormone signal transduction” and “MAPK signaling pathway-plant” based on KEGG enrichment analysis. In total, 423 differential metabolites (DAMs) were determined in the three comparisons (mentioned above) by using metabolomics analysis, of which flavonoids (14.91%) were dominant. Interestingly, more accumulation of terpenoids, aldehydes and esters was observed at higher altitudinal levels, while higher percentages of flavonoids and polyphenols were obtained at lower altitudes based upon K-Means analysis. A unique pathway (p value<0.01) linked to “flavonoid biosynthesis” appeared to have consistent expression patterns in the comparison of H1 vs. H3 based on network analyses of DEGs and DAMs. Notably, regarding the direct products of catalytic reactions, naringenin chalcone, aromadendrin, and naringenin were positively regulated by genes encoding CHS, F3H and CHI, respectively. In addition, the transcription levels of nine candidate genes, including CHI, CHS, CCoAOMT, 2ODDs, ANS, ANR, F3H, DFR and HCT, were confirmed by RT‒qPCR, which were consistent with transcriptional profile data regarding RNA-seq and further demonstrated that these genes involved in flavonoid biosynthesis contribute to the environmental adaptation of the blade in Z. planispinum var. Dintanensis. In summary, our findings represent the first integration of transcriptome data with widely targeted metabolomic data from Z. planispinum var. Dintanensis grown in rocky desertification control areas, which provides novel insights into adaptation to environmental stress. This study has the potential to improve the cultivation and management of Z. planispinum var. Dintanensis through various technologies to gain maximum medicinal production in the future.

Cross-Platform Transcriptomic Data Integration, Profiling, and Mining in Vibrio cholerae.

A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. cholerae in vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules. IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae.

Integrated gene expression profiles reveal a transcriptomic network underlying the thermogenic response in adipose tissue

Obesity and type 2 diabetes are two closely related diseases representing a serious threat worldwide. An increase in metabolic rate through enhancement of non-shivering thermogenesis in adipose tissue may represent a potential therapeutic strategy. Nevertheless, a better understanding of thermogenesis transcriptional regulation is needed to allow the development of new effective treatments. Here, we aimed to characterize the specific transcriptomic response of white and brown adipose tissues after thermogenic induction. Using cold exposure to induce thermogenesis in mice, we identified mRNAs and miRNAs that were differentially expressed in several adipose depots. In addition, integration of transcriptomic data in regulatory networks of miRNAs and transcription factors allowed the identification of key nodes likely controlling metabolism and immune response. Moreover, we identified the putative role of the transcription factor PU.1 in the regulation of PPARγ-mediated thermogenic response of subcutaneous white adipose tissue. Therefore, the present study provides new insights into the molecular mechanisms that regulate non-shivering thermogenesis.

Review: Overview of the transcriptomic landscape in bovine blastocysts and elongated conceptuses driving developmental competence.

In cattle, pregnancy loss due to early embryonic mortality is a major concern that significantly impacts reproductive efficiency. Given the economic importance of cattle in livestock productivity, much research has been carried out to comprehend the regulatory mechanisms underlying this early embryo loss. Thus, understanding the molecular principles behind the reciprocal communication between the maternal uterus and the developing conceptus is paramount. Measurement of mRNA expression through a variety of techniques is widely used to unravel the complex and dynamic interaction between these two players. Development of high-throughput technologies, such as microarrays and RNA sequencing, have allowed global quantification of the full range of expressed mRNA, or transcriptome, of a biological sample. Therefore, numerous investigators have applied one or the other method to study the bovine embryo transcriptome at different developmental checkpoints and under different conditions. The goal of this article was to review studies involving the use of high-throughput techniques to study the transcriptome of the bovine embryo from the blastocyst (∼day 7) to the elongating conceptus stage (∼days 13-16) in terms of developmental capacity and the impact of procedures for in vitro embryo production. Furthermore, the differentially expressed genes reported by each study and enriched pathways were compared to determine common terms. The studies described here highlight differences in the transcriptome (i) between blastocysts with divergent ability to sustain a pregnancy, (ii) between age-matched elongated conceptuses with divergent developmental fates, and (iii) between blastocysts and elongated conceptuses produced in vitro or in vivo. Comparison between these works, supported by other studies involving transcriptomic data integration presented at the end of this review, highlights the involvement of pathways related to energy metabolism in embryonic competence, which may be altered because of the procedures involved in the in vitro production of embryos.

Transcriptome analysis stratifies second-generation non-WNT/non-SHH medulloblastoma subgroups into clinically tractable subtypes.

Medulloblastoma (MB), one of the most common malignant pediatric brain tumor, is a heterogenous disease comprised of four distinct molecular groups (WNT, SHH, Group 3, Group 4). Each of these groups can be further subdivided into second-generation MB (SGS MB) molecular subgroups, each with distinct genetic and clinical characteristics. For instance, non-WNT/non-SHH MB (Group 3/4) can be subdivided molecularly into eight distinct and clinically relevant tumor subgroups. A further molecular stratification/summarization of these SGS MB would allow for the assignment of patients to risk-associated treatment protocols. Here, we performed DNA- and RNA-based analysis of 574 non-WNT/non-SHH MB and analyzed the clinical significance of various molecular patterns within the entire cohort and the eight SGS MB, with the aim to develop an optimal risk stratification of these tumors. Multigene analysis disclosed several survival-associated genes highly specific for each molecular subgroup within this non-WNT/non-SHH MBcohort with minimal inter-subgroup overlap. These subgroup-specific and prognostically relevant genes were associated with pathways that could underlie SGS MB clinical-molecular diversity and tumor-driving mechanisms. By combining survival-associated genes within each SGS MB, distinct metagene sets being appropriate for their optimal risk stratification were identified. Defined subgroup-specific metagene sets were independent variables in the multivariate models generated for each SGS MB and their prognostic value was confirmed in a completely non-overlapping validation cohort of non-WNT/non-SHH MB (n = 377). In summary, the current results indicate that the integration of transcriptome data in risk stratification models may improve outcome prediction for each non-WNT/non-SHH SGS MB. Identified subgroup-specific gene expression signatures could be relevant for clinical implementation and survival-associated metagene sets could be adopted for further SGS MB risk stratification. Future studies should aim at validating the prognostic role of these transcriptome-based SGS MB subtypes in prospective clinical trials.

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach.

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

Short term starvation potentiates the efficacy of chemotherapy in triple negative breast cancer via metabolic reprogramming

BackgroundChemotherapy (CT) is central to the treatment of triple negative breast cancer (TNBC), but drug toxicity and resistance place strong restrictions on treatment regimes. Fasting sensitizes cancer cells to a range of chemotherapeutic agents and also ameliorates CT-associated adverse effects. However, the molecular mechanism(s) by which fasting, or short-term starvation (STS), improves the efficacy of CT is poorly characterized.MethodsThe differential responses of breast cancer or near normal cell lines to combined STS and CT were assessed by cellular viability and integrity assays (Hoechst and PI staining, MTT or H2DCFDA staining, immunofluorescence), metabolic profiling (Seahorse analysis, metabolomics), gene expression (quantitative real-time PCR) and iRNA-mediated silencing. The clinical significance of the in vitro data was evaluated by bioinformatical integration of transcriptomic data from patient data bases: The Cancer Genome Atlas (TCGA), European Genome-phenome Archive (EGA), Gene Expression Omnibus (GEO) and a TNBC cohort. We further examined the translatability of our findings in vivo by establishing a murine syngeneic orthotopic mammary tumor-bearing model.ResultsWe provide mechanistic insights into how preconditioning with STS enhances the susceptibility of breast cancer cells to CT. We showed that combined STS and CT enhanced cell death and increased reactive oxygen species (ROS) levels, in association with higher levels of DNA damage and decreased mRNA levels for the NRF2 targets genes NQO1 and TXNRD1 in TNBC cells compared to near normal cells. ROS enhancement was associated with compromised mitochondrial respiration and changes in the metabolic profile, which have a significant clinical prognostic and predictive value. Furthermore, we validate the safety and efficacy of combined periodic hypocaloric diet and CT in a TNBC mouse model.ConclusionsOur in vitro, in vivo and clinical findings provide a robust rationale for clinical trials on the therapeutic benefit of short-term caloric restriction as an adjuvant to CT in triple breast cancer treatment.

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

Analyzing Prokaryotic Transcriptomics in the Light of Genome Data with the MicroScope Platform.

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species occurring in our environment. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. To address this challenge, the MicroScope platform has been developed. It is an integrated Web platform for management, annotation, comparative analysis, and visualization of microbial genomes ( https://mage.genoscope.cns.fr/microscope ). Launched in 2005, the platform has been under continuous development and provides analyzes for complete and ongoing genome projects together with metabolic network reconstruction and transcriptomic experiments allowing users to improve the understanding of gene functions. MicroScope platform is widely used by microbiologists from academia and industry all around the world for collaborative studies and expert annotation. It enables collaborative work in a rich comparative genomic context and improves community-based curation efforts. Here, we describe the protocol to follow for the integration and analysis of transcriptomics data in the Microscope platform. The chapter reviews each key step from the experimental design to the analysis and interpretation of the experiment data and results. The integration of transcriptomics data gives a dynamic view of the genome by allowing the users to improve the understanding of gene functions by interpreting them in the light of regulatory cell processes. Moreover, they can also contribute to the refinement of genome annotation through the discovery of new genes and help to fill metabolic gaps.

Human placental microRNAs dysregulated by cadmium exposure predict neurobehavioral outcomes at birth.

Prenatal cadmium (Cd) exposure has been implicated in both placental toxicity and adverse neurobehavioral outcomes. Placental microRNAs (miRNAs) may function to developmentally program adverse pregnancy and newborn health outcomes in response to gestational Cd exposure. In a subset of the Rhode Island Child Health Study (RICHS, n = 115) and the New Hampshire Birth Cohort Study (NHBCS, = 281), we used small RNA sequencing and trace metal analysis to identify Cd-associated expression of placental miRNAs using negative binomial generalized linear models. We predicted mRNAs targeted by Cd-associated miRNAs and relate them to neurobehavioral outcomes at birth through the integration of transcriptomic data and summary scores from the NICU Network Neurobehavioral Scale (NNNS). Placental Cd concentrations are significantly associated with the expression level of five placental miRNAs in NHBCS, with similar effect sizes in RICHS. These miRNA target genes overrepresented in nervous system development, and their expression is correlated with NNNS metrics suggestive of atypical neurobehavioral outcomes at birth. Gestational Cd exposure is associated with the expression of placental miRNAs. Predicted targets of these miRNAs are involved in nervous system development and may also regulate placental physiology, allowing their dysregulation to modify developmental programming of early life health outcomes. This research aims to address the poor understanding of the molecular mechanisms governing adverse pregnancy and newborn health outcomes in response to Gestational cadmium (Cd) exposure. Our results outline a robust relationship between Cd-associated placental microRNA expression and NICU Network Neurobehavioral Scales (NNNS) at birth indicative of atypical neurobehavior. This study utilized healthy mother-infant cohorts to describe the role of Cd-associated dysregulation of placental microRNAs as a potential mechanism by which adverse neurobehavioral outcomes are developmentally programmed.

Amino acids profiling and transcriptomic data integration demonstrates the dynamic regulation of amino acids synthesis in the leaves of Cyclocarya paliurus.

BackgroundCyclocarya paliurus is a tree well known for its edible and medicinal leaves. Amino acids are essential nutritional components that are present in foods and closely related to the flavor and quality of tea. However, the abundance of amino acids and the regulation of amino acid biosynthesis in the leaves of C. paliurus have not been investigated across different developmental stages.MethodsA combined metabolomic and transcriptomic analysis was employed to investigate the changes in the amino acid profile over several developmental stages (S1, the smallest fully expanded leaf; S3, full leaf enlargement and full leaf thickness; and S2, an intermediate developmental stage between S1 and S3) and the molecular mechanism was elucidated.ResultsThe results showed that leaves at the S1 stage had the highest content, while those at the S3 stage had the lowest content of amino acids; fourteen differentially expressed genes were involved in the glycolysis pathway, the tricarboxylic acid cycle and the pentose phosphate pathway, which indicated that the reduced abundance of amino acids in the leaves of C. paliurus (mature leaves) may be attributable to reduced gene expression related to carbohydrate metabolism. Four basic leucine zipper transcription factors might play important roles in the regulation of the biosynthesis of amino acids in the leaves of C. paliurus.ConclusionsLeaves at the S1 stage are recommended for high quality tea production because of their high content of amino acids, while leaves at the S2 stage are recommended for generous tea production because of their high levels of sweet flavor amino acids (alanine) and essential amino acids (methionine, phenylalanine, threonine, and tryptophan).

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis.

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression

BackgroundRecent studies have shown that choroid plexuses (CP) may be involved in the neuro-immune axes, playing a role in the interaction between the central and peripheral inflammation. Here we aimed to investigate CP volume alterations in depression and their associations with inflammation. Methods51 depressed participants (HDRS score > 13) and 25 age- and sex-matched healthy controls (HCs) from the Wellcome Trust NIMA consortium were re-analysed for the study. All the participants underwent full peripheral cytokine profiling and simultaneous [11C]PK11195 PET/structural MRI imaging for measuring neuroinflammation and CP volume respectively. ResultsWe found a significantly greater CP volume in depressed subjects compared to HCs (t(76) = +2.17) that was positively correlated with [11C]PK11195 PET binding in the anterior cingulate cortex (r = 0.28, p = 0.02), prefrontal cortex (r = 0.24, p = 0.04), and insular cortex (r = 0.24, p = 0.04), but not with the peripheral inflammatory markers: CRP levels (r = 0.07, p = 0.53), IL-6 (r = -0.08, p = 0.61), and TNF-α (r = -0.06, p = 0.70). The CP volume correlated with the [11C]PK11195 PET binding in CP (r = 0.34, p = 0.005). Integration of transcriptomic data from the Allen Human Brain Atlas with the brain map depicting the correlations between CP volume and PET imaging found significant gene enrichment for several pathways involved in neuroinflammatory response. ConclusionThis result supports the hypothesis that changes in brain barriers may cause reduction in solute exchanges between blood and CSF, disturbing the brain homeostasis and ultimately contributing to inflammation in depression. Given that CP anomalies have been recently detected in other brain disorders, these results may not be specific to depression and might extend to other conditions with a peripheral inflammatory component.