Biology subjects

Goldenberg, A.

Publications and source records attributed to Goldenberg, A..

10 recordsLinked to original sources

bioRxiv2023

Age, sex, and cell type-resolved hypothalamic gene expression across the pubertal transition in mice

Although the hypothalamus plays a critical role in the regulation of puberty, more research is needed to identify the gene regulatory networks that control pubertal timing. Here, we investigate the age-, sex- and cell-type-specific gene regulation in the hypothalamus across the pubertal transition. We used RNA-seq to profile hypothalamic gene expression in male and female mice at five time points spanning the onset of puberty (postnatal days (PD) 12, 22, 27, 32, and 37). By combining this data with hypothalamic scRNA-seq data of pre- and post-pubertal mice, we were able to assign gene expression changes to their cell types of origin. In our colony, pubertal onset occurs earlier in male mice allowing us to focus on genes whose expression is dynamic across ages and offset between sexes and to explore bases of sex effects. Our age-by- sex pattern of expression enriched for biological pathways involved hormone production, neuronal activation, and glial maturation. Additionally, we found a dramatic expansion of oligodendrocytes precursor cells into mature oligodendrocytes spanning the pre-pubertal (PD12) to peri-pubertal (PD27) timepoints, and that genes driving this expansion enrich for genes involved in pubertal regulation. Together, by incorporating multiple biological timepoints with male and female mice simultaneously, our work furthers the understanding of gene and cell-type changes that accompany the development of secondary sex characteristics in both sexes.

genomics↗

bioRxiv2022

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the users gene lists, aiding in hypothesis generation and data-driven literature review. HighlightsBy curating metadata from uniformly processed human RNA-seq studies, we created a database of 3162 differential expression analyses. These analyses include TCGA, GTEx, and 142 unique studies in SRA, involving 985 distinct experimental conditions. The Differential Expression Enrichment Tool (DEET) allows users to systematically compare their gene lists to this database.

bioinformatics↗

bioRxiv2022

Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland

The pituitary gland regulates essential physiological processes such as growth, pubertal onset, stress response, metabolism, reproduction, and lactation. While sex biases in these functions and hormone production have been described, the underlying identity, temporal deployment, and cell-type specificity of sex-biased pituitary gene regulatory networks are not fully understood. To capture sex differences in pituitary gene regulation dynamics during postnatal development, we performed 3 untranslated region sequencing and small RNA sequencing to ascertain gene and microRNA expression respectively across five postnatal ages (postnatal days 12, 22, 27, 32, 37) that span the pubertal transition in female and male C57BL/6J mouse pituitaries (n=5-6 biological replicates for each sex at each age). We observed over 900 instances of sex-biased gene expression and 17 sex-biased microRNAs, with the majority of sex differences occurring with puberty. Using miRNA-gene target interaction databases, we identified 18 sex-biased genes that were putative targets of 5 sex-biased microRNAs. In addition, by combining our bulk RNA-seq with publicly available male and female mouse pituitary single-nuclei RNA-seq data, we obtained evidence that cell-type proportion sex differences exist prior to puberty and persist post-puberty for three major hormone-producing cell types: somatotropes, lactotropes, and gonadotropes. Finally, we predicted sex-biased genes in these three pituitary cell types after accounting for cell-type proportion differences between sexes. Our study reveals the identity and postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary. This work also highlights the importance of considering sex biases in cell-type composition when understanding sex differences in the processes regulated by the pituitary gland. HighlightsO_LIMale and female mouse pituitary gland gene and miRNA expression was profiled across five postnatal ages spanning pubertal development C_LIO_LIAbundant sex differences in pituitary gene expression exist prior to puberty and become more prominent upon puberty C_LIO_LICombining expression data from genes and miRNAs revealed 18 putative sex-biased gene targets of 5 sex-biased miRNAs C_LIO_LISex differences in the proportions of somatotropes, lactotropes, and gonadotropes are predicted to occur prior to puberty C_LI

genomics↗

bioRxiv2020

De novo coding variants in the AGO1 gene cause a neurodevelopmental disorder with intellectual disability

High-impact pathogenic variants in more than 1,000 protein-coding genes cause Mendelian forms of neurodevelopmental disorders (NDD), including the newly reported AGO2 gene. This study describes the molecular and clinical characterization of 28 probands with NDD harboring heterozygous AGO1 coding variants. De novo status was always confirmed when parents were available (26/28). A total of 15 unique variants leading to amino acid changes or deletions were identified: 12 missense variants, two in-frame deletions of one codon, and one canonical splice variant leading to a deletion of two amino acid residues. Some variants were recurrently identified in several unrelated individuals: p.(Phe180del), p.(Leu190Pro), p.(Leu190Arg), p.(Gly199Ser), p.(Val254Ile) and p.(Glu376del). AGO1 encodes the Argonaute 1 protein, which functions in gene-silencing pathways mediated by small non-coding RNAs. Three-dimensional protein structure predictions suggest that these variants might alter the flexibility of the AGO1 linkers domains, which likely would impair its function in mRNA processing. Affected individuals present with intellectual disability of varying severity, as well as speech and motor delay, autistic behavior and additional behavioral manifestations. Our study establishes that de novo coding variants in AGO1 are involved in a novel monogenic form of NDD, highly similar to AGO2 phenotype.

genetics↗

bioRxiv2020

KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

bioinformatics↗

bioRxiv2020

Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for [~]100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN. HighlightsO_LIscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs). C_LIO_LIscMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration. C_LIO_LIscMappR is deployed as a user-friendly R package available at CRAN. C_LI

bioinformatics↗

bioRxiv2020

Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

Most two-group statistical tests are implicitly looking for a broad pattern such as an overall shift in mean, median or variance between the two groups. Therefore, they operate best in settings where the effect of interest is uniformly affecting everyone in one group versus the other. In real-world applications, there are many scenarios where the effect of interest is heterogeneous. For example, a drug that works very well on only a proportion of patients and is equivalent to a placebo on the remaining patients, or a disease associated gene expression dysregulation that only occurs in a proportion of cases whereas the remaining cases have expression levels indistinguishable from the controls for the considered gene. In these examples with heterogeneous effect, we believe that using classical two-group statistical tests may not be the most powerful way to detect the signal. In this paper, we developed a statistical test targeting heterogeneous effects and demonstrated its power in a controlled simulation setting compared to existing methods. We focused on the problem of finding meaningful associations in complex genetic diseases using omics data such as gene expression, miRNA expression, and DNA methylation. In simulated and real data, we showed that our test is complementary to the traditionally used statistical tests and is able to detect disease-relevant genes with heterogeneous effects which would not be detectable with previous approaches.

bioinformatics↗

bioRxiv2020

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments was used. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information (NMI) was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Such new groups can be evaluated longitudinally for prognostic utility and could be stratified for clinical trials targeted toward each groups unique brain and behavioral profiles.

neuroscience↗

bioRxiv2018

Integrative Pharmacogenomics Analysis of Patient Derived Xenografts

One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] <5%). We then leveraged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. We identified 87 pathways significantly associated with response to 51 drugs (FDR < 5%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and binimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.

bioinformatics↗

bioRxiv2018

DNA Methylation Network Estimation with Sparse Latent Gaussian Graphical Model

Inferring molecular interaction networks from genomics data is important for advancing our understanding of biological processes. Whereas considerable research effort has been placed on inferring such networks from gene expression data, network estimation from DNA methylation data has received very little attention due to the substantially higher dimensionality and complications with result interpretation for non-genic regions. To combat these challenges, we propose here an approach based on sparse latent Gaussian graphical model (SLGGM). The core idea is to perform network estimation on q latent variables as opposed to d CpG sites, with q<<d. To impose a correspondence between the latent variables and genes, we use the distance between CpG sites and transcription starting sites of the genes to generate a prior on the CpG sites latent class membership. We evaluate this approach on synthetic data, and show on real data that the gene network estimated from DNA methylation data significantly explains gene expression patterns in unseen datasets.

genomics↗