Transcriptome Organization Research Articles

Transcriptomic analysis of the human habenula in schizophrenia.

Transcriptomic analysis of the human habenula and identification of molecular changes associated with schizophrenia risk and illness state.

Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset.

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.

Genetic Signatures for Distinguishing Chemo-Sensitive from Chemo-Resistant Responders in Prostate Cancer Patients.

Prostate cancer remains a significant public health concern in sub-Saharan Africa, particularly impacting South Africa with high mortality rates. Despite many years of extensive research and significant financial expenditure, there has yet to be a definitive solution to prostate cancer. It is not just individuals who vary in their response to treatment, but even different nodules within the same tumor exhibit unique transcriptome patterns. These distinctions extend beyond mere differences in gene expression levels to encompass the control and networking of individual genes. Escalating chemotherapy resistance in prostate cancer patients has prompted increased research into its underlying mechanisms. The heterogeneous nature of transcriptomic organization among men makes the pursuit of universal biomarkers and one-size-fits-all treatments impractical. This study delves into the expression of drug resistance-associated genes, ABCB1 and CYP1B1, in cancer cells. Employing bioinformatics, we explored the molecular pathways and cascades linked to drug resistance following upregulation of these genes. Samples were obtained from archived prostate cancer patient specimens through pre-treatment biopsies of two categories: good vs. poor responders, with cDNAs synthesized from isolated RNAs subjected to qPCR analysis. The results revealed increased ABCB1 and CYP1B1 expression in tumor samples of the poor responders. Gene enrichment and network analysis associated ABCB1 with ABC transporters and LncRNA-mediated therapeutic resistance (WP3672), while CYP1B1 was linked to ovarian steroidogenesis, tryptophan metabolism, steroid hormone biosynthesis, benzo(a)pyrene metabolism, the sulindac metabolic pathway, and the estrogen receptor pathway, which are associated with drug resistance. Both ABCB1 and CYP1B1 correlated with microRNAs in cancer and the Nuclear Receptors Meta-Pathway. STRING analysis predicted protein-protein interactions of ABCB1 and CYP1B1 with Glutathione S-transferase Pi, Catechol O-methyltransferase, UDP-glucuronosyltransferase 1-6, Leucine-rich Transmembrane and O-methyltransferase (LRTOMT), and Epoxide hydrolase 1, with scores of 0.973, 0.971, 0.966, 0.966, and 0.966, respectively. Furthermore, molecular docking analysis of the chemotherapy drug, docetaxel, with CYP1B1 and ABCB1 revealed robust molecular interactions, with binding energies of -20.37 and -15.25 Kcal/mol, respectively. These findings underscore the susceptibility of cancer patients to drug resistance due to increased ABCB1 and CYP1B1 expression in tumor samples from patients in the poor-responders category that affects associated molecular pathways. The potent molecular interactions of ABCB1 and CYP1B1 with docetaxel further emphasize the potential basis for chemotherapy resistance.

Transcriptomic profiling of prefrontal cortex reveals shared mechanisms underlying Alzheimer’s and Parkinson’s Diseases

AbstractBackgroundAlzheimer’s disease (AD) and Parkinson’s disease (PD) have overlapping characteristics in terms of symptoms and brain pathology. Understanding the similarities in these diseases could lead to more effective treatments. While there is only limited evidence for genetic overlap between the two, enrichment of heritability in genetic regions that are related to microglia has been found in both diseases. However, most of the transcriptomics studies published on AD and PD employed postmortem brains, which obscures the identification of alteration of gene expression within individual cell types.MethodHere we used single‐nucleus transcriptomics to examine over eight million nuclei generated from prefrontal cortex samples of over a thousand postmortem donors with AD, PD, both diseases and controls. Then, we built gene regulatory networks (GRNs) to detect the shared gene expression alterations in all major cell types and detected key drivers within each module.ResultExamining multiple levels of transcriptomic organization, such as gene expression and GRNs for both protein‐coding and noncoding genes, provided a comprehensive understanding of the molecular pathology of AD and PD. Our analysis revealed cell‐specific modules linked to shared and distinct mechanisms of both diseases. Network‐derived signatures showed cell‐type specificity, implicating regulatory regions within AD and PD genetic risk loci that participate in a variety of biological pathways as well as changes in TF regulation.ConclusionThe integration of transcriptomic and genetic data from AD and PD provides a quantitative, genome‐wide resource for mechanistic insight and potential therapeutic development. Funding: U01NS125580, R01AG067025, AARF‐21‐722200

TRNA-derived fragments as new players in regulatory processes in yeast.

For a very long time, RNA molecules were treated as transistory molecules, by which the genetic information flows from DNA to proteins; the model proposed in the 1960s accepted that proteins are boththe products and the regulators of gene expression. Since then, thousands of reports proved that RNAs should be thought about as the factors that do control gene expression. The pervasive transcription has been reported in many eukaryotic organisms, illustrating a highly interwoven transcriptome organization that includes hundreds of previously unknown noncoding RNAs. The key roles of noncoding RNAs (microRNAs and small interferingRNAs) in gene expression regulation are no longer surprising, as are new classes of noncoding RNAs constantly being discovered. TransferRNAs (tRNAs) are the second most abundant type of RNAs in the cell. Advances in high-throughput sequencing technologies exposed the existence of functional, regulatory tRNA-derived RNA fragments (tRFs), generated from precursor and mature tRNAs. These tRF molecules have been found to play central roles during stress and different pathological conditions. Herein, we present the critical assessment of the discoveries made in the field of tRNA-derived fragments in the past 15 years in various pathogenic and nonpathogenic yeast species.

Abstract GS4-07: The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts

Abstract Background. DCIS consists of a molecularly heterogeneous group of premalignant lesions, with variable risk of invasive progression. Understanding biomarkers for invasive progression could help individualize treatment recommendations based upon tumor biology. As part of the NCI Human Tumor Atlas Network (HTAN), we conducted comprehensive genomic analyses on two large DCIS case-control cohorts. Methods. We performed smart3-seq and low-pass whole genome sequencing on two independent, retrospective, longitudinally sampled DCIS case-control cohorts. TBCRC 038 was a multicenter cohort diagnosed with DCIS between 1998 and 2016 at one of the Translational Breast Cancer Research sites; the RAHBT (Resource of Archival Human Breast Tissue) cohort included women identified through the St. Louis Breast Tissue Repository, and the Women’s Health Repository diagnosed between 1997 and 2001. We studied the spectrum of molecular changes present and sought genomic predictors of subsequent ipsilateral breast events (iBEs: DCIS recurrence or invasive progression) in both DCIS epithelium and stroma in formalin fixed paraffin embedded tissue. We generated de novo tumor and stroma-centric subtypes for DCIS that represents fundamental transcriptomic organization. Copy number analysis was performed using low-pass DNA sequencing. Non-negative matrix factorization (NMF) was applied to the RNA expression of all coding genes to identify clusters. A negative-binomial regression model was used to identify differentially expressed genes. Results. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up. In TBCRC samples, we identified three clusters via NMF in TBCRC referred to as ER low, quiescent, and ER high. The ER-low cluster had significantly higher levels of ERBB2 and lower levels of ESR1 compared to quiescent and ER-high clusters. Quiescent cluster lesions were less proliferative and less metabolically active than ER high and ER low subtypes. These findings were replicated in the RAHBT cohort. Focusing on the stromal component of DCIS from laser capture microdissection in RAHBT samples, we identified four distinct DCIS-associated stromal clusters. A “normal-like” stromal cluster with ECM organization and PI3K-AKT signaling; a “collagen-rich” stromal cluster; a “desmoplastic” stromal cluster with high fibroblast and total myeloid abundance, mostly associated with macrophages and myeloid dendritic cells (mDC); and an “immune-dense” stromal cluster. Further, we compared differentially expressed genes in patients with or without subsequent iBEs within 5 years of diagnosis. Hypothesizing that the resulting 812 DE genes (DESeq2) represent multiple routes to subsequent iBEs, we leveraged NMF to identify paths to progression. In both TBCRC and RAHBT cohorts, poor outcome groups exhibited increased ER, MYC signaling, and oxidative phosphorylation, supporting that these pathways are important for DCIS recurrence and progression. Conclusion. Comprehensive genomic profiling in two independent DCIS cohorts with longitudinal outcomes shows distinct DCIS stromal expression patterns and immune cell composition. RNA expression profiles reveal underlying tumor biology that is associated with later iBEs in both cohorts. These studies provide new insight into DCIS biology and will guide the design of diagnostic strategies to prevent invasive progression. Citation Format: Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, Lorraine M King, Tyler Risom, Lunden Simpson, Sujay Vennam, Aziz Khan, Timothy Hardman, Bryan E Harmon, Fergus J Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-07.

Personalized 3-Gene Panel for Prostate Cancer Target Therapy.

Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.

Hybrid Sequencing Reveals Novel Features in the Transcriptomic Organization of Equid Alphaherpesvirus

Hybrid Sequencing Reveals Novel Features in the Transcriptomic Organization of Equid Alphaherpesvirus

Targeting the Transcriptome Through Globally Acting Components.

Transcription is a step in gene expression that defines the identity of cells and its dysregulation is associated with diseases. With advancing technologies revealing molecular underpinnings of the cell with ever-higher precision, our ability to view the transcriptomes may have surpassed our knowledge of the principles behind their organization. The human RNA polymerase II (Pol II) machinery comprises thousands of components that, in conjunction with epigenetic and other mechanisms, drive specialized programs of development, differentiation, and responses to the environment. Parts of these programs are repurposed in oncogenic transformation. Targeting of cancers is commonly done by inhibiting general or broadly acting components of the cellular machinery. The critical unanswered question is how globally acting or general factors exert cell type specific effects on transcription. One solution, which is discussed here, may be among the events that take place at genes during early Pol II transcription elongation. This essay turns the spotlight on the well-known phenomenon of promoter-proximal Pol II pausing as a step that separates signals that establish pausing genome-wide from those that release the paused Pol II into the gene. Concepts generated in this rapidly developing field will enhance our understanding of basic principles behind transcriptome organization and hopefully translate into better therapies at the bedside.

The Human Tumor Atlas Network (HTAN) Breast Precancer Atlas: A Multi-Omic Integrative Analysis of Ductal Carcinoma in situ With Clinical Outcomes

Ductal carcinoma in situ (DCIS) is the most commonly diagnosed precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes to identify predictors of subsequent ipsilateral breast events. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up from two independent cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents the fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct DCIS stromal expression patterns and immune cell compositions were identified. We also found RNA expression patterns that correlate with later events in both cohorts. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Using TIF-Seq2 to investigate association between 5´ and 3´mRNA ends.

The development of high-throughput technologies has revealed pervasive transcription in all genomes that have been investigated so far. This has uncovered a highly interleaved transcriptome organization involving thousands of overlapping coding and non-coding RNA isoforms that challenge our traditional definitions of genes and functional regions of the genome. In this chapter, we discuss the application of an improved Transcript Isoform Sequencing approach (TIF-Seq2) able to concurrently determine the start and end sites of individual RNA molecules. We exemplify its use for the investigation of the human transcriptome and show how it is especially well suited to discriminate between overlapping molecules and accurately define their boundaries.

Transcriptomic organization of the human brain in post-traumatic stress disorder.

Despite extensive study of the neurobiological correlates of post-traumatic stress disorder (PTSD), little is known about its molecular determinants. Here, differential gene expression and network analyses of four prefrontal cortex subregions from postmortem tissue of people with PTSD demonstrate extensive remodeling of the transcriptomic landscape. A highly connected downregulated set of interneuron transcripts is present in the most significant gene network associated with PTSD. Integration of this dataset with genotype data from the largest PTSD genome-wide association study identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked transcriptomic sexual dimorphism that could contribute to higher rates of PTSD in women. Comparison with a matched major depressive disorder cohort revealed significant divergence between the molecular profiles of individuals with PTSD and major depressive disorder despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the prefrontal cortex that contribute to the pathophysiology of PTSD in humans.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach.

Simple SummaryWe applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells.Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3, TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.

Genomic and Transcriptomic Survey Provides New Insight into the Organization and Transposition Activity of Highly Expressed LTR Retrotransposons of Sunflower (Helianthus annuus L.).

LTR retrotransposons (RTEs) play a crucial role in plant genome evolution and adaptation. Although RTEs are generally silenced in somatic plant tissues under non-stressed conditions, some expressed RTEs (exRTEs) escape genome defense mechanisms. As our understanding of exRTE organization in plants is rudimentary, we systematically surveyed the genomic and transcriptomic organization and mobilome (transposition) activity of sunflower (Helianthus annuus L.) exRTEs. We identified 44 transcribed RTEs in the sunflower genome and demonstrated their distinct genomic features: more recent insertion time, longer open reading frame (ORF) length, and smaller distance to neighboring genes. We showed that GAG-encoding ORFs are present at significantly higher frequencies in exRTEs, compared with non-expressed RTEs. Most exRTEs exhibit variation in copy number among sunflower cultivars and one exRTE Gagarin produces extrachromosomal circular DNA in seedling, demonstrating recent and ongoing transposition activity. Nanopore direct RNA sequencing of full-length RTE RNA revealed complex patterns of alternative splicing in RTE RNAs, resulting in isoforms that carry ORFs for distinct RTE proteins. Together, our study demonstrates that tens of expressed sunflower RTEs with specific genomic organization shape the hidden layer of the transcriptome, pointing to the evolution of specific strategies that circumvent existing genome defense mechanisms.

Systematic analysis to identify transcriptome-wide dysregulation of Alzheimer's disease in genes and isoforms.

Alzheimer's disease (AD) is one of the most common neurodegeneration diseases caused by multiple factors. The mechanistic insight of AD remains limited. To disclose molecular mechanisms of AD, many studies have been proposed from transcriptome analyses. However, no analysis across multiple levels of transcription has been conducted to discover co-expression networks of AD. We performed gene-level and isoform-level analyses of RNA sequencing (RNA-seq) data from 544 brain tissues of AD patients, mild cognitive impaired (MCI) patients, and healthy controls. Gene and isoform levels of co-expression modules were constructed by RNA-seq data. The associations of modules with AD were evaluated by integrating cognitive scores of patients, Genome-wide association studies (GWAS), alternative splicing analysis, and dementia-related genes expressed in brain tissues. Totally, 29 co-expression modules were found with expressions significantly correlated with the cognitive scores. Among them, two isoform modules were enriched with AD-associated SNPs and genes whose mRNA splicing displayed significant alteration in relation to AD disease. These two modules were further found enriched with dementia-related genes expressed in four brain regions of 125 AD patients. Analyzing expressions of these two modules revealed expressions of 39 isoforms (corresponding to 35 genes) significantly correlated with cognitive scores of AD patients, in which 38 isoforms were significantly up-regulated in AD patients comparing to controls, and 33 isoforms (corresponding to 29 genes) were not reported as AD-related previously. Employing the co-expression modules and the drug-induced gene expression data from Connectivity Map (CMAP), 12 drugs were predicted as significant in restoring the gene expression of AD patients towards health, which include nine drugs reported for relieving AD. In comparison, four of the top 12 significant drugs were known for relieving AD if the drug prediction was performed by the genes expressed significantly different in AD and healthy controls. Analysis of multiple levels of the transcriptomic organization is useful in suggesting AD-related co-expression networks and discovering drugs.

The multiscale and multiphase organization of the transcriptome.

Gene expression must be co-ordinated to cellular activity. From transcription to decay, the expression of millions of RNA molecules is highly synchronized. RNAs are covered by proteins that regulate every aspect of their cellular life: expression, storage, translational status, localization, and decay. Many RNAs and their associated regulatory proteins can coassemble to condense into liquid droplets, viscoelastic hydrogels, freeze into disorganized glass-like aggregates, or harden into quasi-crystalline solids. Phase separations provide a framework for transcriptome organization where the single functional unit is no longer a transcript but instead an RNA regulon. Here, we will analyze the interaction networks that underlie RNA super-assemblies, assess the complex multiscale, multiphase architecture of the transcriptome, and explore how the biophysical state of an RNA molecule can define its fate. Phase separations are emerging as critical routes for the epitranscriptomic control of gene expression.

Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease.

We have applied a novel and integrative analysis framework for next-generation sequencing (NGS) data to 503 human subjects provided by the Religious Orders Study and Memory and Aging Project (ROSMAP) to examine changes in transcriptomic organization and common variants in association with late-onset Alzheimer’s disease (LOAD). Our framework identified seven reproducible, co-regulated modules after quality control (QC), clinical segregation, preservation filtering, and functional ontology analysis. These modules were specifically enriched in several innate and adaptive immune system processes, the synaptic vesicle cycle, and Hippo signaling. Topological and functional erosion of these modules due to shedding of genes and loss of in-module connectivity was diagnostic of disease progression. Perturbation analysis revealed that only 1% of eQTLs overlapped genes participating in these co-regulated modules. Common variants nevertheless identified components of the immune systems like human leukocyte antigen (HLA) complex and microtubule-associated protein tau (MAPT) regions in association with LOAD. Our results implicate microglial function, adaptive immune response, and the structural degeneration of neurons as contributors to the transcriptional deregulation observed along with common genetic variants in the progression of LOAD.

Proximity RNA-seq: A Sequencing Method to Identify Co-localization of RNA.

RNA localization is an important regulatory layer of gene expression and cell functioning. The protocol guides through the Proximity RNA-seq method, in which RNA molecules are sequenced in their spatial, cellular context to derive RNA co-localization and transcriptome organization. Transcripts in individual subcellular particles from chemically crosslinked cells are tagged with the same, unique DNA barcode in water-in-oil emulsion droplets. First, single DNA barcodes are PCR amplified and immobilized on single, small magnetic beads in droplets. Subsequently, 3' ends of bead-bound barcode copies are tailed with random pentadecamers. Then beads are encapsulated again into droplets together with crosslinked subcellular particles containing RNA. Reverse transcription using random pentadecamers as primers is performed in droplets, which optimally contain one bead and one particle, in order to tag RNAs co-localized to the same particle. Sequencing such cDNA molecules identifies the RNA molecule and the barcode. Subsequent analysis of transcripts that share the same barcode, i.e., co-barcoding, reveals RNA co-localization and interactions. The technique is not restricted to pairs of RNAs but can as well detect groups of transcripts and estimates local RNA density or connectivity for individual transcripts. We provide here a detailed protocol to perform and analyze Proximity RNA-seq on cell nuclei to study spatial, nuclear RNA organization.

Spatiotemporal Organization of the E.coli Transcriptome: Translation Independence and Engagement in Regulation.

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E.coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E.coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.

Persistent homology analysis of brain transcriptome data in autism.

Persistent homology methods have found applications in the analysis of multiple types of biological data, particularly imaging data or data with a spatial and/or temporal component. However, few studies have assessed the use of persistent homology for the analysis of gene expression data. Here we apply persistent homology methods to investigate the global properties of gene expression in post-mortem brain tissue (cerebral cortex) of individuals with autism spectrum disorders (ASD) and matched controls. We observe a significant difference in the geometry of inter-sample relationships between autism and healthy controls as measured by the sum of the death times of zero-dimensional components and the Euler characteristic. This observation is replicated across two distinct datasets, and we interpret it as evidence for an increased heterogeneity of gene expression in autism. We also assessed the topology of gene-level point clouds and did not observe significant differences between ASD and control transcriptomes, suggesting that the overall transcriptome organization is similar in ASD and healthy cerebral cortex. Overall, our study provides a novel framework for persistent homology analyses of gene expression data for genetically complex disorders.