Biology subjects

Raj, A.

Publications and source records attributed to Raj, A..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Regulators of health and lifespan extension in genetically diverse mice on dietary restriction

Caloric restriction (CR) delays aging and extends healthy lifespan in multiple species. Alternative forms of dietary restriction (DR) such as intermittent fasting (IF) have drawn significant interest as a more sustainable regimen, but the landscape of longevity-promoting dietary interventions remains largely unexplored. Identifying the most robust, efficacious, and experimentally tractable modes of DR is key to better understanding and implementing effective longevity interventions for human healthspan. To that end, we have performed an extensive assessment of DR interventions, investigating the effects of graded levels of CR (20% and 40%) and IF (1 day and 2 days of fasting per week) on the health and survival of 960 genetically diverse female mice. All interventions extended lifespan, although only CR significantly reduced the mortality doubling time. Notably, IF did not extend lifespan in mice with high pre-intervention bodyweight. We carried out extensive phenotyping to determine the health effects of long-term DR and to better understand the mechanisms driving within-diet heterogeneity in lifespan. The top within-diet predictor of lifespan was the ability of mice to maintain bodyweight through periods of handling, an indicator of stress resilience. Additional predictors of long lifespan include specific changes in immune cells, red blood cell distribution width (RDW), and retention of adiposity in late life. We found that lifespan is heritable (h2 = 0.24), and that genetic background has a larger influence on lifespan than dietary interventions. We identified a significant association for lifespan and RDW on chromosome 18 that explained 4.3% of the diet-adjusted variation in lifespan. Diet-induced changes on metabolic traits, although beneficial, were relatively poor predictors of lifespan, arguing against the long-standing notion that DR works by counteracting the negative effects of obesity. These findings indicate that improving health and extending lifespan are not synonymous and that metabolic parameters may be inappropriate endpoints for evaluating aging interventions in preclinical models and clinical trials.

genetics↗

bioRxiv2023

Nuclear speckles regulate HIF-2α programs and correlate with patient survival in kidney cancer

Nuclear speckles are membrane-less bodies within the cell nucleus enriched in RNA biogenesis, processing, and export factors. In this study we investigated speckle phenotype variation in human cancer, finding a reproducible speckle signature, based on RNA expression of speckle-resident proteins, across >20 cancer types. Of these, clear cell renal cell carcinoma (ccRCC) exhibited a clear correlation between the presence of this speckle expression signature, imaging-based speckle phenotype, and clinical outcomes. ccRCC is typified by hyperactivation of the HIF-2 transcription factor, and we demonstrate here that HIF-2 drives physical association of a select subset of its target genes with nuclear speckles. Disruption of HIF-2-driven speckle association via deletion of its speckle targeting motifs (STMs)--defined in this study--led to defective induction of speckle-associating HIF-2 target genes without impacting non-speckle-associating HIF-2 target genes. We further identify the RNA export complex, TREX, as being specifically altered in speckle signature, and knockdown of key TREX component, ALYREF, also compromises speckle-associated gene expression. By integrating tissue culture functional studies with tumor genomic and imaging analysis, we show that HIF-2 gene regulatory programs are impacted by specific manipulation of speckle phenotype and by abrogation of speckle targeting abilities of HIF-2. These findings suggest that, in ccRCC, a key biological function of nuclear speckles is to modulate expression of a specific subset of HIF-2-regulated target genes that, in turn, influence patient outcomes. We also identify STMs in other transcription factors, suggesting that DNA-speckle targeting may be a general mechanism of gene regulation. HIGHLIGHTS- Nuclear speckles shown to reproducibly vary in cancer, predicting patient survival in ccRCC - HIF-2 drives DNA/gene-speckle contacts dependent on identified speckle targeting motifs within HIF-2 - Putative speckle targeting motifs are highly enriched among regulators of gene expression - Partitioning of transcription factor functional programs may be a major biological function of nuclear speckles

cancer biology↗

bioRxiv2023

Probing the Mechanisms of Global Brain Reconfiguration after Local Manipulations

Understanding how localized brain interventions translate into whole-brain dynamics is crucial for deciphering neural function and tailoring therapeutic strategies. Combining mouse experimental datasets of focal interventions (thalamic lesion and chemogenetic silencing of cortical hubs), we demonstrate both local and global effects. Using whole-brain simulations of experimental data, we not only confirm the distributed nature of local manipulations but also offer mechanistic insights into these processes. Our simulations predict specific alterations in firing rates and spectral characteristics across specific brain networks, leading to structured changes in functional connectivity patterns. Some of these predictions have been empirically validated. Notably, the affected brain subnetworks--and their resultant signatures of change--are contingent on the original intervention site, suggesting a method to accurately localize the source of alteration. Our results provide a general framework for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

neuroscience↗

bioRxiv2023

Single cell susceptibility to SARS-CoV-2 infection is driven by variable cell states

The ability of a virus to infect a cell type is at least in part determined by the presence of host factors required for the viral life cycle. However, even within cell types that express known factors needed for infection, not every cell is equally susceptible, suggesting that our knowledge of the full spectrum of factors that promote infection is incomplete. Profiling the most susceptible subsets of cells within a population may reveal additional factors that promote infection. However, because viral infection dramatically alters the state of the cell, new approaches are needed to reveal the state of these cells prior to infection with virus. Here, we used single-cell clone tracing to retrospectively identify and characterize lung epithelial cells that are highly susceptible to infection with SARS-CoV-2. The transcriptional state of these highly susceptible cells includes markers of retinoic acid signaling and epithelial differentiation. Loss of candidate factors identified by our approach revealed that many of these factors play roles in viral entry. Moreover, a subset of these factors exert control over the infectable cell state itself, regulating the expression of key factors associated with viral infection and entry. Analysis of patient samples revealed the heterogeneous expression of these factors across both cells and patients in vivo. Further, the expression of these factors is upregulated in particular inflammatory pathologies. Altogether, our results show that the variable expression of intrinsic cell states is a major determinant of whether a cell can be infected by SARS-CoV-2.

bioengineering↗

bioRxiv2023

A meta-analysis of mouse models of tauopathy reveals the cellular underpinnings of selective regional vulnerability

Neurodegenerative diseases such as Alzheimers disease (AD) exhibit pathological changes in the brain that proceed in a stereotyped and regionally specific fashion, but the cellular and molecular underpinnings of regional vulnerability are currently poorly understood. Recent work has identified certain subpopulations of neurons in a few focal regions of interest, such as the entorhinal cortex, that are selectively vulnerable to tau pathology in AD. However, the cellular underpinnings of regional susceptibility to tau pathology are currently unknown, primarily because whole-brain maps of a comprehensive collection of cell types have been inaccessible. Here, we deployed a recent cell-type mapping pipeline, Matrix Inversion and Subset Selection (MISS), to determine the brain-wide distributions of pan-hippocampal and neocortical neuronal and non-neuronal cells in the mouse using recently available single-cell RNA sequencing (scRNAseq) data. We then performed a robust set of analyses to identify general principles of cell-type-based selective vulnerability using these cell-type distributions, utilizing 5 transgenic mouse studies that quantified regional tau in 12 distinct PS19 mouse models. Using our approach, which constitutes the broadest exploration of whole-brain selective vulnerability to date, we were able to discover cell types and cell-type classes that conferred vulnerability and resilience to tau pathology. Hippocampal glutamatergic neurons as a whole were strongly positively associated with regional tau deposition, suggesting vulnerability, while cortical glutamatergic and GABAergic neurons were negatively associated. Among glia, we identified oligodendrocytes as the single-most strongly negatively associated cell type, whereas microglia were consistently positively correlated. Strikingly, we found that there was no association between the gene expression relationships between cell types and their vulnerability or resilience to tau pathology. When we looked at the explanatory power of cell types versus GWAS-identified AD risk genes, cell type distributions were consistently more predictive of end-timepoint tau pathology than regional gene expression. To understand the functional enrichment patterns of the genes that were markers of the identified vulnerable or resilient cell types, we performed gene ontology analysis. We found that the genes that are directly correlated to tau pathology are functionally distinct from those that constitutively embody the vulnerable cells. In short, we have demonstrated that regional cell-type composition is a compelling explanation for the selective vulnerability observed in tauopathic diseases at a whole-brain level and is distinct from that conferred by risk genes. These findings may have implications in identifying cell-type-based therapeutic targets.

neuroscience↗

bioRxiv2023

Quality control and annotation of variant peptides identified through proteogenomics

Variant peptides resulting from translation of single nucleotide polymorphisms (SNPs) can lead to aberrant or altered protein functions and thus hold translational potential for disease diagnosis, therapeutics and personalized medicine. Variant peptides detected by proteogenomics are fraught with high number of false positives. Class-specific FDR along with ad-hoc post-search filters have been employed to tackle this issue, but there is no uniform and comprehensive approach to assess variant quality. These protocols are mostly manual or tedious, and not accessible across labs. We present a software tool, PgxSAVy, for the quality control of variant peptides. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy was able to segregate true and false variants with 98.43% accuracy on simulated data. We then used [~]2.8 million spectra (PXD004010 and PXD001468) and identified 12,705 variant PSMs, of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. In these datasets, it identified previously found variants as well some novel variants not seen in original studies. The confident variants identified the importance of mutations in glycolysis and gluconeogenesis pathways in Alzheimers disease. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. AvailabilityPgxSAVy is freely available at https://github.com/anuragraj/PgxSAVy Key PointsO_LIVariant peptide in proteogenomics have high rates of false positives C_LIO_LIclass-specific FDR is not sufficiently effective, and tedious manual filtering is not scalable C_LIO_LIWe developed PgxSAVy for automated quality control and disease annotation of variant peptides from proteogenomics search results C_LIO_LIPgxSAVy was validated using simulation data and manually annotated variant PSMs C_LIO_LIIndependent application on large datasets on Alzheimers and HEK cell lines demonstrated that PgxSAVy discovered known and novel mutations with important biological roles. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=161 HEIGHT=200 SRC="FIGDIR/small/542998v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@187a515org.highwire.dtl.DTLVardef@67471corg.highwire.dtl.DTLVardef@6d90e9org.highwire.dtl.DTLVardef@144c6c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise

The inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.

bioengineering↗

bioRxiv2023

Bayesian Inference of a Spectral Graph Model for Brain Oscillations

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

neuroscience↗

bioRxiv2023

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimers disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimers disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

neuroscience↗

bioRxiv2023

Retrospective identification of intrinsic factors that mark pluripotency potential in rare somatic cells

Pluripotency can be induced in somatic cells by the expression of the four "Yamanaka" factors OCT4, KLF4, SOX2, and MYC. However, even in homogeneous conditions, usually only a rare subset of cells admit reprogramming, and the molecular characteristics of this subset remain unknown. Here, we apply retrospective clone tracing to identify and characterize the individual human fibroblast cells that are primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis led to increased reprogramming efficiency, identifying it as a barrier to reprogramming. Changing the frequency of reprogramming by inhibiting the activity of LSD1 led to an enlarging of the pool of cells that were primed for reprogramming. Our results show that even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

systems biology↗

bioRxiv2022

Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants

The UK Biobank Pharma Proteomics Project (UKB-PPP) is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,306 UKB participants. Here, we describe results from the first phase of UKB-PPP, including protein quantitative trait loci (pQTL) mapping of 1,463 proteins that identifies 10,248 primary genetic associations, of which 85% are newly discovered. We also identify independent secondary associations in 92% of cis and 29% of trans loci, expanding the catalogue of genetic instruments for downstream analyses. The study provides an updated characterisation of the genetic architecture of the plasma proteome, leveraging population-scale proteomics to provide novel, extensive insights into trans pQTLs across multiple biological domains. We highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement proteins, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug target discovery by extending the genetic proxied effect of PCSK9 levels on lipid concentrations, cardio- and cerebro-vascular diseases, and additionally disentangle specific genes and proteins perturbed at COVID-19 susceptibility loci. This public-private partnership provides the scientific community with an open-access proteomics resource of unprecedented breadth and depth to help elucidate biological mechanisms underlying genetic discoveries and accelerate the development of novel biomarkers and therapeutics.

genetics↗

bioRxiv2022

Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation

Metastasis occurs when tumor cells leave the primary tumor site and disseminate to distal organs. Even though most cells remain in the primary tumor, the circumstances by which a small fraction of them disseminate remain unclear. Here, we show that a rare, highly invasive subpopulation of melanoma cells can be detected within clonal cell lines due to non-genetic fluctuations in gene expression. The highly invasive phenotype was intrinsic to the cells, independent of their environment, and was marked by transiently high levels of SEMA3C expression, as revealed by RNA-sequencing analysis. Furthermore, the invasive subpopulation drove the bulk dissemination of tumor cells to distal locations in a mouse model of melanoma. The transcription factor NKX2.2 regulated the proportion of invasive cells in the melanoma 1205Lu cell line. Furthermore, an overall tradeoff between proliferation and invasion in single cells was observed. Our results suggest that phenotypes like metastasis may arise from intrinsic differences stemming from non-genetic fluctuations between single cells.

cancer biology↗

bioRxiv2022

Estimating Brain Functional Connectivity from Common Subspace Mapping Between Structural and Functional Connectomes

Understanding the connection between the brains structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subjects functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.

neuroscience↗

bioRxiv2022

clampFISH 2.0 enables rapid, scalable amplified RNA detection in situ

RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect and exponentially amplify signals from many RNA species at once, while also reducing time and cost compared to the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection by demonstrating the ability to detect 10 different RNA species in over 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.

bioengineering↗

bioRxiv2022

Whole brain network analysis of neural synchrony and information flow during transition from wakefulness to light non-rapid eye movement sleep

Sleep is a highly stereotyped phenomenon, requiring robust spatial and temporal coordination of neural activity. How the brain coordinates neural activity with sleep onset can provide insight into the physiological functions subserved by sleep and pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wake to non-rapid eye movement (NREM) sleep using magnetoencephalography imaging in healthy subjects. In addition, we performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The sleep transition was identified to be encoded in spatially and temporally specific patterns of local and long-range neural synchrony. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset. Finally, biophysical neural mass modeling demonstrated spatially heterogeneous properties of cortical excitation-to-inhibition from wake to NREM. Together, these findings reveal whole-brain corticocortical structure in the sleep-wake transition and demonstrate the orchestration of local and long-range, frequency-specific cortical interactions that are fundamental to sleep onset.

bioengineering↗

bioRxiv2022

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease

BackgroundNeuronal and circuit level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (A{beta}) in preclinical models of Alzheimers disease (AD). These relationships remain poorly understood in patients with AD. MethodsUsing empirical spectra from magnetoencephalography (MEG) and computational modeling (neural mass model; NMM) we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and A{beta}, measured by positron emission tomography (PET), in patients with AD. ResultsPatients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher A{beta} depositions. ConclusionsOur results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and A{beta} in patients with AD. FundingThis study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM & GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimers Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL and (KGR); 2019-A-013-SUP (KGR); a grant from the Alzheimers Association: (PCTRB-13-288476) (KAV), and made possible by Part the CloudTM, (ETAC-09-133596); a grant from Tau Consortium (GDR & WJJ), and a gift from the S. D. Bechtel Jr. Foundation.

neuroscience↗

bioRxiv2022

ClonoCluster: a method for using clonal origin to inform transcriptome clustering

Clustering cells based on their high dimensional profiles is an important data reduction process by which researchers infer distinct categories of cellular state. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity.

bioinformatics↗

bioRxiv2022

Changes in chromatin accessibility are not concordant with transcriptional changes for single-factor perturbations

A major goal in the field of transcriptional regulation is the mapping of changes in the binding of transcription factors to the resultant changes in gene expression. Recently, methods for measuring chromatin accessibility have enabled us to measure changes in accessibility across the genome, which are thought to correspond to transcription factor binding events. In concert with RNA-sequencing, these data in principle enable such mappings; however, few studies have looked at their concordance over short duration treatments with specific perturbations. Here, we used tandem, bulk ATAC-seq and RNA-seq measurements from MCF-7 breast carcinoma cells to systematically evaluate the concordance between changes in accessibility and changes in expression in response to retinoic acid and TGF-{beta}. We found two classes of genes whose expression showed a significant change: those that showed some change in accessibility of nearby chromatin, and those that showed virtually no change despite strong changes in expression. The peaks associated with genes in the former group had a lower baseline accessibility prior to exposure to signal. Focusing the analysis specifically on peaks with motifs for transcription factors associated with retinoic acid and TGF-{beta} signaling did not reduce the lack of correspondence. Analysis of paired chromatin accessibility and gene expression data from distinct paths along the hematopoietic differentiation trajectory showed a much stronger correspondence, suggesting that the multifactorial biological processes associated with differentiation may lead to changes in chromatin accessibility that reflect rather than drive altered transcriptional status. Together, these results show many gene expression changes can happen independent of changes in accessibility of local chromatin in the context of a single-factor perturbation and suggest that some changes to accessibility changes may occur after changes to expression, rather than before.

bioinformatics↗