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Results for “gene expression”

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bioRxiv2025

PRECOG update: An augmented resource of clinical outcome associations with gene expression for pediatric and immunotherapy cohorts

Gene expression can be used to define prognostic and predictive biomarkers across cancers and treatment modalities. PRECOG (https://precog.stanford.edu) is a compendium of datasets with gene expression and clinical outcomes that facilitates visualization of associations between genomic profiles and patient survival. Here, we augment the existing PRECOG with new datasets in previously poorly represented adult cancer types, as well as adding annotated pediatric and immunotherapy treated cohorts. Pediatric PRECOG comprises [~]4,000 patients across 12 cancers; while the immunotherapy cohort (ICI PRECOG) contains [~]4,500 patients across 20 cancer subtypes from 80 distinct datasets across 52 studies. We compute and visualize associations of gene expression with survival outcomes using Cox regression for time-to-event, or logistic regression for responder vs non-responder, across all datasets. We also estimate cell type fractions in samples via computational deconvolution using CIBERSORTx, to identify survival associations at the level of cell types. All expression data, clinical annotations, and gene and cell type survival z-scores and meta z-scores for adult, pediatric, and ICI PRECOG, are available for interactive analysis and download, along with Kaplan-Meier and boxplot visualizations. This updated resource will provide new insights into biomarkers for specific therapies, populations, and cancer types. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/671849v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@13446bdorg.highwire.dtl.DTLVardef@11025fcorg.highwire.dtl.DTLVardef@12e017corg.highwire.dtl.DTLVardef@1637dba_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Identifying a developmental transition in honey bees using gene expression data

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

systems biology↗

bioRxiv2022

Differential gene expression underlying the biosynthesis of Dufour's gland signals in Bombus impatiens

Pheromones regulating social behavior are one of the most explored phenomena in social insects. However, compound identity, biosynthesis and their genetic regulation are known in only a handful of species. Here we conducted chemical and RNA-seq analyses of the Dufours gland in the bumble bee Bombus impatiens and examined the signals and the pathways regulating signal production in queens and workers. Across Hymenopterans, the Dufours gland contains mostly long-chained hydrocarbons and esters that signal reproductive and social status in several bee species. In bumble bees, the Dufours gland contains queen- and worker-specific esters, in addition to terpenes and terpene-esters only found in gynes and queens. These compounds are assumed to be synthesized de novo in the gland, however, their genetic regulation is unknown. A whole transcriptome gene expression analysis of the gland in queens, gynes, queenless and queenright workers showed distinct transcriptomic profiles, with thousands of differentially expressed genes between the groups. Workers and queens express genes associated with key enzymes in the biosynthesis of wax esters, while queens and gynes preferentially express key genes in terpene biosynthesis. In contrast, no genes were differentially expressed in queenless and queenright workers, despite differences in their Dufours gland chemistry and reproductive state, suggesting the quantitative differences in worker secretion are not regulated at the level of production. Overall, our data demonstrate gland-specific regulation of chemical signals associated with social behavior and identifies genes and pathways regulating caste-specific chemical signals in social insects.

genomics↗

bioRxiv2024

Stabilizing selection and adaptation shape cis and trans gene expression variation in C. elegans

An outstanding question in the evolution of gene expression is the composition of the underlying regulatory architecture and the processes that shape it. Mutations affecting a genes expression may reside locally in cis or distally in trans; the accumulation of these changes, their interactions, and their modes of inheritance influence how traits are expressed and how they evolve. Here, we interrogated gene expression variation in C. elegans, including the first allele-specific expression analysis in this system, capturing effects in cis and in trans that govern gene expression differences between the reference strain N2 and seven wild strains. We observed extensive compensatory regulation, in which opposite effects in cis and trans at individual genes mitigate expression differences among strains, and that genes with expression differences exhibit strain specificity. As the genomic distance increased between N2 and each wild strain, the number of genes with expression differences also increased. We also report for the first time that expression-variable genes are lower expressed on average than genes without expression differences, a trend that may extend to humans and Drosophila melanogaster and may reflect the selection constraints that govern the universal anticorrelation between gene expression and rate of protein evolution. Together, these and other observed trends support the conclusion that many C. elegans genes are under stabilizing selection for expression level, but we also highlight outliers that may be biologically significant. To provide community access to our data, we introduce an easily accessible, interactive web application for gene-based queries: https://wildworm.biosci.gatech.edu/ase/. Author SummaryGenes are first expressed as RNAs, which act as templates in the synthesis of proteins, the workhorse molecules of the cell. Naturally-occurring mutations can influence how RNA expression occurs; to evaluate how RNA expression varies among individuals and how such patterns evolve, the authors measured expression in wild strains of the nematode C. elegans and in offspring derived from crosses between them. This study describes the architecture of RNA expression variation in this essential model organism and reports novel patterns that may extend to insects and humans.

genetics↗

bioRxiv2024

Termination sequence between an inducible promoter and ubiquitous chromatin opening element (UCOE) reduces gene expression leakage and silencing

AbstractInducible gene expression circuits offer precise control over target gene activation, making them essential tools for direct reprogramming, where cells are guided to differentiate into specific cell types. However, stable circuit function and consistent expression of key inducible proteins are crucial for effective reprogramming, and DNA methylation-induced silencing hinders this stability. To address this issue, A2-ubiquitous chromatin opening elements (A2UCOE) have gained attention for their ability to maintain open chromatin and prevent methylation, thereby stabilizing gene expression. In this study, we aimed to forward program iPSCs into thymic epithelial cells (TECs) using a compact, all-in-one gene circuit composed of a doxycycline-inducible Tet-On system, 863-bp A2UCOE (0.9 UCOE), and FOXN1, a master transcription factor for TEC differentiation. This compact construct enables site-specific genome integration and stable inducible expression within iPSCs for renewably generating TECs. While the 0.9 UCOE promoted stable expression of constitutively expressed genes, it also caused unintended FOXN1 gene leakage, leading to unprogrammed gradual differentiation of the iPSCs. We generated A2UCOE fragments of varying lengths and found that gene leakage persisted regardless of fragment size. We tested spacer sequences between the A2UCOE and the Tet-On promoter consisting of varying AT-nucleotide content (35, 50, and 65%) and the 65% AT-rich SV40 poly-A terminator sequence and found that only the SV40 poly-A mitigated this leakage, and surprisingly, enhanced desired anti-silencing effects. These findings highlight the benefits and potential risks of using A2UCOE in iPSCs, and provides insights into a regulatory DNA architecture optimal for compact forward programming circuits for controlled iPSC differentiation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/629244v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@8398d7org.highwire.dtl.DTLVardef@1a75beeorg.highwire.dtl.DTLVardef@84767eorg.highwire.dtl.DTLVardef@7f5cec_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/524406v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@86d135org.highwire.dtl.DTLVardef@1110670org.highwire.dtl.DTLVardef@bdaddeorg.highwire.dtl.DTLVardef@e1d268_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISpatial transcriptomics using the Visium platform reveals the transcriptional signature across the brain, recapitulating the anatomy of the mouse brain C_LIO_LISleep deprivation induces transcriptomic changes unique to each brain region C_LIO_LIThe hippocampus is the brain region impacted the most by acute sleep deprivation, with most differentially regulated genes significantly downregulated C_LIO_LIThe neocortex exhibits layer-specific changes in gene expression, with most differentially regulated genes significantly upregulated C_LIO_LIRegistration of spatial transcriptomic data to a common anatomical reference space (Allen Common Coordinate Framework) allows statistical analysis of gene expression across regions of the brain and for multi-sample analysis C_LI

neuroscience↗

bioRxiv2018

Cold Inducible RNA-binding protein (CIRBP) adjusts clock gene expression and REM sleep recovery after sleep deprivation

Sleep depriving mice affects clock gene expression, suggesting that these genes partake in sleep homeostasis. The mechanisms linking wakefulness to clock gene expression are, however, not well understood. We propose CIRBP because its rhythmic expression is i) sleep-wake driven and ii) necessary for high-amplitude clock gene expression in vitro. We therefore expect Cirbp knock-out (KO) mice to exhibit attenuated sleep-deprivation (SD) induced changes in clock gene expression, and consequently to differ in their sleep homeostatic regulation. Lack of CIRBP indeed blunted the SD-incurred changes in cortical expression of the clock gene Rev-erb whereas it amplified the changes in Per2 and Clock. Concerning sleep homeostasis, KO mice accrued only half the extra REM sleep wild-type (WT) littermates obtained during recovery. Unexpectedly, KO mice were more active during lights-off which was accompanied by an acceleration of theta oscillations. Thus, CIRBP adjusts cortical clock gene expression after SD and expedites REM sleep recovery.

neuroscience↗

bioRxiv2019

PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

BackgroundAlthough the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.\n\nMethodsWe performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples.\n\nResultsThis microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94.\n\nConclusionThe gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.

cancer biology↗

bioRxiv2019

Longitudinal transcriptome-wide gene expression analysis of sleep deprivation treatment showsinvolvement of circadian genes and immune pathways

BackgroundTherapeutic sleep deprivation (SD) rapidly induces robust, transient antidepressant effects in a large proportion of major mood disorder patients suffering from a depressive episode, but underlying biological factors remain poorly understood. Research suggests that these patients may have altered circadian molecular genetic clocks and that SD functions through resetting dysregulated genes; additional factors may be involved, warranting further investigation. Leveraging advances in microarray technology enabling the transcriptome-wide assessment of gene expression, this study aimed to examine gene expression changes accompanying SD and recovery sleep in patients suffering from an episode of depression. MethodsPatients (N=78) and controls (N=15) underwent SD, with blood taken at the same time of day before, after one night of SD and after recovery sleep. A transcriptome-wide gene-by-gene approach was used, with a targeted look also taken at circadian genes. Furthermore, gene set enrichment, and longitudinal gene set analyses including the time point after recovery sleep, were conducted. ResultsCircadian genes were significantly affected by SD, with patterns suggesting that molecular clocks of responders and non-responders, as well as patients and controls respond differently to chronobiologic stimuli. Notably, gene set analyses revealed a strong widespread effect of SD on pathways involved in immune function and inflammatory response, such as those involved in cytokine and especially in interleukin signalling. Longitudinal gene set analyses showed that in responders these pathways were upregulated after SD; in non-responders, little response was observed. ConclusionsOur findings emphasize the close relationship between circadian, immune and sleep systems and their link to etiology of depression at the transcriptomic level.

epidemiology↗

bioRxiv2019

STAT3 and GR cooperate to drive basal-like triple negative breast cancer gene expression and proliferation.

Breast cancers can be divided into subtypes with different prognoses and treatment responses based on global gene expression differences. Luminal breast cancer gene expression and proliferation are driven by the transcription factors Estrogen Receptor (ER), FOXA1 and GATA3. Targeting ER is the most effective therapy for treating luminal breast cancer because ER is the master regulator of the luminal gene expression program. In contrast, it is unclear which transcription factors are responsible for driving the gene expression signature that defines basal-like triple negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype of the disease. This study utilized integrated analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. The results of this study indicate that glucocorticoid receptor (GR) and signal transducer and activator of transcription 3 (STAT3) bind to the same genomic regulatory regions that are specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperate to regulate expression of hundreds of genes in the basal-like gene expression signature and these downstream genes are associated with poor prognosis in patients. Furthermore, combination treatment with small molecule drugs that inhibit both transcription factors leads to synergistic decreases in cell proliferation in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple negative breast cancer through rational combination of STAT3 and GR inhibitors.

cancer biology↗

bioRxiv2025

Epigenetic Regulation During Flatfish Metamorphosis: Integrative Omics Analysis of DNA Methylation and Gene Expression in Turbot Brain

The regulation of gene expression plays a pivotal role in the complex metamorphic process of flatfishes, which oversees the dynamic alterations that occur during their transition from a pelagic to a benthic way of life. Flatfish metamorphosis is characterized by substantial modifications in both form and function, which are initiated by thyroid hormones. Epigenetic mechanisms play a vital role in the regulation of gene expression during this transformative process. This study examines the molecular mechanisms underlying flatfish metamorphosis by integrating multi-omics data that provide information on chromatin status, DNA methylation profile, and gene expression at three key stages (pre-metamorphosis, climax and post-metamorphosis) in the turbot (Scophthalmus maximus) brain, a complex organ that plays a critical role in the regulation of this intricate process. The analysis of DNA methylation patterns revealed major epigenetic dynamic changes during the different metamorphosis stages. Specifically, the methylation levels exhibited a typical bimodal distribution in the pre-metamorphic stage, transitioned to intermediate levels of methylation (around 50%) during the climax stage, and reverted to a bimodal distribution in the post-metamorphic stage. Notably, DMRs were identified in regions of open chromatin that colocalized with CpG islands. Moreover, our results indicate an inverse relationship between DNA methylation and transcriptional activity in regions near the transcription start sites (TSSs), where high levels of methylation correspond to low expression and low levels of methylation corresponds to high gene transcription. This study thereby elucidates the regulatory impact of methylation on gene expression during the metamorphosis process in the flatfish brain.

molecular biology↗

bioRxiv2022

Deep model predictive control of gene expression in thousands of single cells

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the frameworks ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput.

systems biology↗

bioRxiv2022

A gene expression analysis and immune infiltration between lesioned and preserved subchondral bone in osteoarthritis

BackgroundOsteoarthritis (OA) is a degenerative disease which need more research. The purpose of this study was to performed gene expression analysis and immune infiltration between lesioned and preserved subchondral bone, and validation with datasets and experiment of multiple tissues. MethodsThe different expressed genes(DEGs) of GSE51588 datasets between lesioned and preserved tibial plateaus of OA patients were conducted. Moreover, functional annotation and protein-protein interaction (PPI) network were applied for exploring the potential therapeutic targets in OA subchondral bones between lesioned and preserved sides. In addition, multiple tissues were used to screen out co-expressed genes and the expression levels of identified candidate DEGs was detected by quantitative real-time polymerase chain reaction (qRT-PCR) in OA. Finally, immune infiltration analysis was conducted. ResultsA total of 1010 DEGs were identified, including upregulated 423 genes, 587 downregulated genes. Upregulated genes showed that BP terms were enriched in "skeletal system development", "sister chromatid cohesion","ossification" etc. Pathways were enriched in "Wnt signaling pathway," "Proteoglycans in cancer," Downregulated genes showed that BP terms were enriched in "inflammatory response," "xenobiotic metabolic process", "positive regulation of inflammatory response". Pathways were enriched in "Neuroactive ligand-receptor interaction", "AMPK signaling pathway" etc. JUN, TNF, IL1B,were the hub genes in the PPI network. Col1A1 and LRRC15 were screened out by multiple datasets and validated by experiments. Immune infiltration showed adipocytes and endothelial cells infiltrated less in lesioned samples. ConclusionOur research might provide valuable information for exploring the pathogenesis mechanism of OA and identifying the potential therapy targets for OA diagnosis.

molecular biology↗

bioRxiv2022

Evolution of the regulation of developmental gene expression in blind Mexican cavefish

Changes in gene expression regulation during development are considered the main drivers of morphological evolution and diversification. Here, we analysed the embryonic transcriptomes of surface-dwelling and blind cave-adapted morphs of the species Astyanax mexicanus and their reciprocal F1 hybrids at tailbud stage. Comparing gene expression in parents and allelic expression ratios in hybrids, we found that [~]20% of the transcriptome is differentially expressed and that cis-regulatory changes are the main contributors to variations in early developmental gene expression in the two morphs. We provide a list of 108 cis-regulated genes that could contribute to cavefish developmental evolution, and further explore the regulatory mechanisms controlling the cellular and regional expression of rx3, a "master eye gene". Using quantitative embryology approaches after fluorescent in situ hybridisation, cell transplantations and interference with signalling pathways, we show that rx3 cellular levels -controlling optic cell fates-are regulated in cis and in a cell-autonomous manner, whereas the size of rx3 domain -controlling eye size-depends on non-autonomous Wnt signalling. Altogether, we reveal how distinct mechanisms and regulatory modules can regulate developmental gene expression and shape developmental evolution, with negligible contribution of coding mutations.

developmental biology↗

bioRxiv2023

Barley shows reduced Fusarium Head Blight under drought and modular expression of differential expressed genes under combined stress

Plants often face simultaneous abiotic and biotic stress conditions. However, physiological and transcriptional responses of plants under combined stress situations are little understood. Spring barley is susceptible to Fusarium Head Blight (FHB), which is strongly affected by weather conditions. We therefore studied the potential influence of drought on FHB severity and responses in three differently susceptible spring barley varieties and found strongly reduced FHB severity in susceptible varieties under drought. Quantity of differentially expressed genes (DEGs) and strength of transcriptomic regulation reflected the concentration of physiological stress markers such as abscisic acid or fungal DNA contents. Infection-related gene expression associated rather with susceptibility than resistance. Weighted gene correlation network analysis uncovered 18 modules of co-expressed genes, which reflect the pathogen or drought response in the used varieties. A generally infection-related module contained co-expressed genes for defence, programmed cell death and mycotoxin-detoxification indicating that diverse genotypes use a similar defence strategy towards FHB albeit with different success. Further DEGs showed co-expression in drought or genotype-associated modules correlating with measured phytohormones or the osmolyte proline. The combination of drought stress with infection lead to highest numbers of DEGs and provoked a modular composition of single stress responses rather than a specific transcriptional readout. HighlightCo-expression network analysis reveals association of physiological stress markers and gene expression modules under biotic and abiotic stress

plant biology↗

bioRxiv2024

Reduced signatures of gene duplication and non-random gene organization in shaping stage-specific patterns of gene expression across a relatively simple life cycle

Complex eukaryotes generally experience some degree of ecological transition as they develop, which is usually accompanied by the expression of different traits across their ontogeny. Theory suggests the evolution of differentiation between life stages is facilitated by the expression of different genes at different points throughout ontogeny, which alleviates pleiotropic evolutionary constraints. Therefore, ascertaining what contributes to specialized patterns of gene expression across ontogeny is fundamental to understanding the evolution of ontogenetic complexity. Expression divergence between duplicate genes and gene organization on chromosomes have been identified as important features of relatively complex ontogenies. Therefore, one could predict that their link to transcriptional specialization across ontogeny may be weaker in relatively simpler life cycles. Here, we investigated the links between gene duplication, gene organization, and stage-specific patterns of expression across the relatively simple Caenorhabditis elegans life cycle. We found that signatures of stage-biased chromosomal regions were weaker in C. elegans than what has been previously described in organisms with more complex ontogenies. Furthermore, we found the extent that duplicate genes varied in ontogenetic expression pattern was more constrained in C. elegans. Overall, our findings add to a body of evidence that suggests a link between gene duplication and gene organization and the evolution of complex ontogenies.

evolutionary biology↗

bioRxiv2017

Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression

To gain insight into how mammalian gene expression is controlled by rapidly evolving regulatory elements, we jointly analysed promoter and enhancer activity with downstream transcription levels in liver samples from twenty species. Genes associated with complex regulatory landscapes generally exhibit high expression levels that remain evolutionarily stable. While the number of regulatory elements is the key driver of transcriptional output and resilience, regulatory conservation matters: elements active across mammals most effectively stabilise gene expression. In contrast, recently-evolved enhancers typically contribute weakly, consistent with their high evolutionary plasticity. These effects are observed across the entire mammalian clade and robust to potential confounders, such as gene expression level. Overall, our results illuminate how the evolutionary stability of gene expression is profoundly entwined with both the number and conservation of surrounding promoters and enhancers.\n\nHighlightsO_LIGene expression levels and stability are linked to the number of elements in the regulatory landscape.\nC_LIO_LIConserved regulatory elements associate with tightly controlled, highly expressed genes.\nC_LIO_LIRecently evolved enhancers weakly influence gene expression, but promoters are similarly active regardless of conservation.\nC_LIO_LIThe interplay between complexity of the regulatory landscape and conservation of individual promoters and enhancers shapes gene expression in mammals.\nC_LI

genomics↗

bioRxiv2024

CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria

Foundational techniques in molecular biology--such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins--rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

synthetic biology↗