Biology subjects

Berglund, A.

Publications and source records attributed to Berglund, A..

12 recordsLinked to original sources

bioRxiv2026

Three-Year Longitudinal Analysis of Splicing Changes in Myotonic Dystrophy Type 1

Myotonic dystrophy type 1 (DM1) is caused by a CTG expansion in the 3' untranslated region of the DMPK gene that results in the expression of expanded CUG RNA which trap splicing factors resulting in widespread alternative splicing dysregulation. A panel of these splicing events, or splicing index (SI), is currently used to measure splicing dysregulation in DM1 patients. Despite significant interest in splicing, little is known about its progression over time in DM1. The objective of this study was to evaluate the changes in SI over 3 years in a well-phenotyped cohort of DM1 participants and identify factors contributing to these changes. RNA was extracted from vastus lateralis (VL) biopsies of 20 DM1 participants taken 3 years apart and the SI score was measured using targeted RNA sequencing. Baseline SI scores correlated with percent predicted muscle strength of the ankle dorsiflexors, knee extensors and hand grip. On average, SI scores worsened over 3 years, but the progression varied markedly among participants. Adult/juvenile phenotypes and lower baseline SI scores were among the factors associated with higher progression of SI scores over time. These findings emphasize the importance of integrating clinical phenotype and baseline molecular severity when interpreting longitudinal SI change, particularly in the context of clinical trials.

bioinformatics↗

bioRxiv2026

Inosine misincorporation into mRNA triggers the integrated stress response and activates an innate immune gene expression signature

The inosine triphosphate pyrophosphatase (ITPase) enzyme restricts levels of the non-canonical nucleotides (deoxy)inosine triphosphate (dITP/ITP) and prevents their aberrant misincorporation into nucleic acids. ITPase deficiency is associated with dilated cardiomyopathy and epileptic encephalopathy in humans and is usually fatal in infancy. It leads to pronounced inosine misincorporation into RNA but the cellular consequences of this misincorporation are not well understood and the pathogenic basis of ITPase deficiency remains unknown. Here we show that cellular transfection of mRNA with inosine misincorporation activates the integrated stress response (ISR) with an innate immune gene expression signature. This stress response triggers stress granule formation and is modulated by the double stranded RNA sensor protein Kinase R (PKR). Inosine nucleoside treatment of ITPase-deficient cells leads to endogenous inosine misincorporation into mRNA and activation of the ISR. Further, differentiation of human ITPase-deficient induced pluripotent stem cells into neurons results in a low-level stress response. Thus, our study establishes inosine misincorporation into mRNA as an unappreciated form of cellular stress. This is normally prevented by the ITPase enzyme, with implications for the pathogenesis of ITPase deficiency.

molecular biology↗

bioRxiv2024

Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory

BackgroundThe radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GES into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process. MethodsUsing HG-U133Plus 2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with reference laboratory were evaluated. ResultsRSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12 % of observed range). ConclusionsOverall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GES for clinical usage.

genomics↗

bioRxiv2024

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

bioinformatics↗

bioRxiv2024

Zika virus infection in a cell culture model reflects the transcriptomic signatures in patients.

Zika virus (ZIKV), a re-emerging flavivirus, is associated with devasting developmental and neurological disease outcomes particularly in infants infected in utero. Towards understanding the molecular underpinnings of the unique ZIKV disease pathologies, numerous transcriptome-wide studies have been undertaken. Notably, these studies have overlooked the assimilation of RNA-seq analysis from ZIKV-infected patients with cell culture model systems. In this study we find that ZIKV-infection of human lung adenocarcinoma A549 cells, mirrored both the transcriptional and alternative splicing profiles from previously published RNA-seq data of peripheral blood mononuclear cells collected from pediatric patients during early acute, late acute, and convalescent phases of ZIKV infection. Our analyses show that ZIKV infection in cultured cells correlates with transcriptional changes in patients, while the overlap in alternative splicing profiles was not as extensive. Overall, our data indicate that cell culture model systems support dissection of select molecular changes detected in patients and establishes the groundwork for future studies elucidating the biological implications of alternative splicing during ZIKV infection.

microbiology↗

bioRxiv2024

Mutation of two intronic nucleotide alters RNA structure and dynamics, inhibiting MBNL1 and RBFOX1 regulated splicing of the Insulin Receptor

Alternative splicing (AS) of Exon 11 of the Insulin Receptor (INSR) is highly regulated and disrupted in several human disorders. To better understand INSR exon 11 AS regulation, splicing activity of an INSR exon 11 minigene reporter was measured across a gradient of the AS regulator muscleblind-like 1 protein (MBNL1). The RNA-binding protein Fox-1 (RBFOX1) was added to determine its impact on MBNL1-regulated splicing. The role of the RBFOX1 UGCAUG binding site within intron 11 was assessed across the MBNL1 gradient. Mutating the UGCAUG motif inhibited RBFOX1 regulation of exon 11 and had the unexpected effect of reducing MBNL1 regulation of this exon. Molecular dynamics simulations showed that exon 11 and the adjacent RNA adopts a dynamically stable conformation. Mutation of the RBFOX1 binding site altered RNA structure and dynamics, while a mutation that created an optimal MBNL1 binding site at the RBFOX1 site shifted the RNA back to wild type. An antisense oligonucleotide (ASO) was used to confirm the structure in this region of the pre-mRNA. This example of intronic mutations shifting pre-mRNA structure and dynamics to modulate splicing suggests RNA structure and dynamics should be taken into consideration for AS regulation and therapeutic interventions targeting pre-mRNA. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/574689v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@17e410org.highwire.dtl.DTLVardef@7deb00org.highwire.dtl.DTLVardef@cf69f9org.highwire.dtl.DTLVardef@13684e8_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract figure:C_FLOATNO Model for INSR exon 11 splicing regulation through the UGCAUG motif. We propose that the UGCAUG motif, MBNL1, and RBFOX1 cooperatively regulate INSR exon 11 splicing. Mutating this UGCAUG motif is sufficient to alter RNA structural dynamics to disrupt this regulation. C_FIG

biochemistry↗

bioRxiv2021

Techniques to Produce and Evaluate Realistic Multivariate Synthetic Data

BackgroundData modeling in biomedical-healthcare research requires a sufficient sample size for exploration and reproducibility purposes. A small sample size can inhibit model performance evaluations (i.e., the small sample problem). ObjectiveA synthetic data generation technique addressing the small sample size problem is evaluated. We show: (1) from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; (2) synthetic populations (SPs) of unlimited size can be generated from this class with univariate kernel density estimation (uKDE) followed by standard normal random variable generation techniques; and (3) samples drawn from these SPs are statistically like their respective samples. MethodsThree samples (n = 667), selected pseudo-randomly, were investigated each with 10 input variables (i.e., X). uKDE (optimized with differential evolution) was used to augment the sample size in X (i.e., the input variables). The enhanced sample size was used to construct maps that produced univariate normally distributed variables in Y (mapped input variables). Principal component analysis in Y produced uncorrelated variables in T, where the univariate probability density functions (pdfs) were approximated as normal with specific variances; a given SP in T was generated with normally distributed independent random variables with these specified variances. Reversing each step produced the respective SPs in Y and X. Synthetic samples of the same size were drawn from these SPs for comparisons with their respective samples. Multiple tests were deployed: to assess univariate and multivariate normality; to compare univariate and multivariate pdfs; and to compare covariance matrices. ResultsOne sample was approximately multivariate normal in X and all samples were approximately multivariate normal in Y, permitting the generation of unlimited sized SPs. Uni/multivariate pdf and covariance comparisons (in X, Y and T) showed similarity between samples and synthetic samples. ConclusionsThe work shows that a class of multivariate samples has a latent normal characteristic; for such samples, our technique is a simplifying mechanism that offers an approximate solution to the small sample problem by generating similar synthetic data. Further studies are required to understand this latent normal class, as two samples exhibited this characteristic in the study.

bioinformatics↗

bioRxiv2021

Quantification and visualization of the tumor microenvironment heterogeneity from spatial transcriptomic experiments

SummarySpatially-resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics (SThet) that can be compared against clinical information, spot-level cell deconvolution, and spatially-informed clustering (STclust), all using a new data object to store data and resulting analyses simultaneously. Availability and implementationThe R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. ContactFridley.Lab@Moffitt.org or Brooke.Fridley@Moffitt.org Supplementary informationAvailable at Bioinformatics online. O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/454023v2_figG1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@191cbe7org.highwire.dtl.DTLVardef@e1b6c7org.highwire.dtl.DTLVardef@a6f68forg.highwire.dtl.DTLVardef@1853601_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Overview of spatialGE features. A. The STList data object from spatialGE can be creared from several sources, including comma- or tab-separated files containing gene counts and spatial coordinates. The object can also be created directly from Visium outputs, Seurat objects, or GeoMx outputs. B. Users can optionally provide a metadata file, containing information associated with each sample (one row per sample, or per ROI if GeoMx data). C. Methods for quality control of data are provided by spatialGE, including visualizations of counts and genes per spot, as well as filtering of spots or genes within user-determined thresholds. D. A novel method (STclust) performs spatially informed clustering of spots and tissue domain identification. E. spatialGE provides different types of data visualization, including gene expression at each spot ("quilt plots"), as well as adaptation of spatial interpolation ("kriging") to spatial transcriptomics data (transcriptomic surface). F. spatialGE also leverages spatial statistics (Morans I, Gearys C, Getis-Ord Gi) to quantitatively describe heterogeneity within the tumor microenvironment and to explore associations between spatial heterogeneity and clinical oucomes. G. Gene expression deconvolution can also be applied to each spot to detect immune cell types (xCell) and classification of spots as tumor or stroma (ESTIMATE). C_FIG

bioinformatics↗

bioRxiv2021

CCG*CGG interruptions in high penetrance SCA8 families increase RAN translation and protein toxicity

Spinocerebellar ataxia type 8 (SCA8), a dominantly inherited neurodegenerative disorder caused by a CTG*CAG expansion, is unusual because most individuals that carry the mutation do not develop ataxia. To understand the variable penetrance of SCA8 we studied the molecular differences between highly penetrant families and more common sporadic cases (82%) using a large cohort of SCA8 families (N=77). We show that repeat expansion mutations from individuals with two or more affected family members have CCG*CGG interruptions at a higher frequency than sporadic SCA8 cases and that the number of CCG*CGG interruptions correlates with age at onset. At the molecular level, CCG*CGG interruptions increase RNA hairpin stability and steady state levels of SCA8 RAN polyAla and polySer proteins. Additionally, the CCG*CGG interruptions, which encode arginine interruptions in the polyGln frame increase the toxicity of the resulting proteins. In summary, CCG*CGG interruptions increase polyAla and polySer RAN protein levels, polyGln protein toxicity and disease penetrance and provide novel insight into the molecular differences between SCA8 families with high vs. low disease penetrance.

genetics↗

bioRxiv2020

Repeat length increases disease penetrance and severity in C9orf72 ALS/FTD BAC transgenic mice

C9orf72 ALS/FTD patients show remarkable clinical heterogeneity, but the complex biology of the repeat expansion mutation has limited our understanding of the disease. BAC transgenic mice were used to better understand the molecular mechanisms and repeat length effects of C9orf72 ALS/FTD. Genetic analyses of these mice demonstrate that the BAC transgene and not integration site effects cause ALS/FTD phenotypes. Transcriptomic changes in cell proliferation, inflammation and neuronal pathways are found late in disease and alternative splicing changes provide early molecular markers that worsen with disease progression. Isogenic sublines of mice with 800, 500 or 50 G4C2 repeats generated from the single-copy C9-500 line show longer repeats result in earlier onset, increased disease penetrance, and increased levels of RNA foci and RAN aggregates. These data demonstrate G4C2 repeat length is an important driver of disease and identify alternative splicing changes as early biomarkers of C9orf72 ALS/FTD.

neuroscience↗

bioRxiv2018

Acidity promotes tumor progression by altering macrophage phenotype in prostate cancer

Tumors rapidly ferment glucose to lactic acid even in the presence of oxygen, and coupling high glycolysis with poor perfusion leads to extracellular acidification. Here we demonstrate that acidity, independent from lactate, augments the pro-tumor phenotype of macrophages. We used zwitterionic buffers to show that activating macrophages at pH 6.8 in vitro enhanced an IL-4-driven phenotype as measured by gene expression, cytokine profiling, and functional assays. These results were recapitulated in vivo wherein neutralizing intratumoral acidity reduced the pro-tumor phenotype of macrophages, while also decreasing tumor incidence and invasion in the TRAMP model of prostate cancer. These results were recapitulated using an in silico mathematical model that simulate macrophage responses to environmental signals. By turning off acid-induced cellular responses, our in silico mathematical modeling shows that acid-resistant macrophages can limit tumor progression. In summary, this study suggests that tumor acidity contributes to prostate carcinogenesis by altering the state of macrophage activation.

immunology↗

bioRxiv2018

Empirically-Derived Synthetic Populations to Overcome Small Sample Sizes

Limited sample sizes can hinder biomedical research and lead to spurious findings. The objective of this work is to present a new method to generate synthetic populations (SPs) from sparse data samples to aid in modeling developments. Matched case-control data (n=180 pairs) defined the limited samples. Cases and controls were considered as two separate limited samples. Synthetic populations were generated for these observed samples using multivariate unconstrained bandwidth kernel density estimations. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization driven by covariance comparisons. Four synthetic samples (n=180) were constructed from their respective SP for comparison purposes. Similarity between the observed samples with equally sized synthetic-samples was compared under the hypothesis that their sample distributions were the same. Distributions were compared with the maximum mean discrepancy (MMD) test statistic based on a Kernel Two-Sample Test. To evaluate similarity within a modeling context, Principal Component Analysis (PCA) score distributions and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons.\n\nFour SPs were generated with the optimization procedure. The probability of selecting a replicate when randomly constructing synthetic samples with n=180 was infinitesimally small. The MMD tests indicated that the observed sample distributions were similar to the respective synthetic distributions. For both case and control samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples.\n\nThe reasonableness of this SP generation approach was demonstrated. This approach produced synthetic data at the patient level statistically similar to the observed samples, and thus could be used to generate larger-sized simulated data. The methodology coupled kernel density estimation with DE optimization and deployed novel similarity metrics derived from PCA. The use of large-sized synthetic samples may be a way to overcome sparse datasets. To further develop this approach into a research tool for model building purposes, additional evaluation with increased dimensionality is required; moreover, comparisons with other techniques such as bootstrapping and cross-validation will be required for a complete evaluation.

bioinformatics↗