Biology subjects

Emberly, E.

Publications and source records attributed to Emberly, E..

7 recordsLinked to original sources

bioRxiv2026

Motif-based model of transcription predicts effects of sequence variants in AR enhancers and reveals distinct functions for AR-associated transcription factors

Androgen receptor (AR)-mediated transcription plays a central role in prostate cancer development and progression, yet the contributions of individual transcription factors (TFs) to AR-dependent enhancer activity remain incompletely understood. Here we use a biophysically motivated, interpretable motif-based model to dissect these contributions from STARR-seq data in LNCaP cells. By fitting the model separately to androgen inducibility and to baseline enhancer activity, we resolve TFs into three functional classes: hormone-dependent drivers, constitutive activators, and dual-role factors that contribute to both. These patterns suggest that inducibility is associated not only with the presence of AR and co-activator motifs, but also with the relative absence of constitutive activators that may saturate enhancer output. We validate the model against an independent saturation-mutagenesis dataset spanning 40 AR enhancers, predicting mutational effects at single-base resolution (AUC = 0.76), and show that direct fitting to these data independently recovers known AR regulators. Finally, we apply the model to prostate cancer GWAS risk alleles in AR binding site regions, prioritizing four candidate variants predicted to reduce the DHT/EtOH enhancer activity ratio at these loci.

genomics↗

bioRxiv2026

Inference of enhancer-specific transcription factor interactions from gene expression data using a biophysical model

Transcription factors (TFs) play a central role in gene expression and regulation. In recent years, numerous experimental techniques have generated large-scale datasets, alongside computational methods aimed at inferring the role of TF-TF interactions in gene regulation. However, these approaches typically yield global interaction patterns across datasets, which may not accurately reflect local regulatory interactions at specific enhancers. Here, we model transcription using an Ising-type biophysical framework and introduce approximations based on its mean-field representation to infer TF-TF interactions at the level of individual enhancers from expression data, such as STARR-seq or fluorescent protein measurements. We validate our approach using simulated data and evaluate the effect of the strengths of TF-TF and TF-DNA interactions on inference accuracy. We then apply the model to experimental fluorescence data of gap genes for the eve stripe-2 (eve2) enhancer in the fruit fly embryo. The model successfully infers the established roles of the gap genes and predicts the possibility of cooperative and antagonistic interactions among them, which can be experimentally investigated.

biophysics↗

bioRxiv2025

Exploring the multifactorial causes and therapeutic strategies for anabolic resistance in sarcopenia: A systems modeling study

BackgroundSarcopenia is the progressive loss of skeletal muscle mass, strength, and function with age, driven by dysregulation in the rates of muscle protein synthesis (MPS) and breakdown (MPB). Although MPB contributes to net protein balance (NB), a primary contributor of sarcopenia is anabolic resistance, defined as the blunted MPS response to anabolic stimuli such as feeding. While candidate mechanisms of anabolic resistance have been identified, none singularly accounts for the observed reduction in MPS. Instead, multiple mechanisms likely act simultaneously and interactively to suppress MPS. Studying these interactions experimentally is challenging. Mathematical modeling is well suited to analyzing complex biological phenomena such as anabolic resistance. MethodsWe analyzed a previously developed kinetic model of leucine-mediated signaling and protein metabolism in human skeletal muscle to systematically investigate potential mechanisms contributing to anabolic resistance. Using global sensitivity analysis, we identified key controllers of MPS, MPB, and NB. We then simulated amino acid feeding in older adults, classified the responses as either anabolic sensitive or resistant, and compared the resulting parameter distributions of the two groups. We next performed targeted analysis to evaluate the effects of individual and combined putative mechanisms of anabolic resistance on muscle metabolism. Finally, we simulated therapeutic interventions aimed at restoring muscle metabolism. ResultsThe sensitivity analysis revealed that MPS and MPB are primarily controlled by their proximal signaling processes, while NB is largely driven by MPS dynamics. Exploratory simulations showed that several parameters and signaling protein concentrations, particularly those controlling MPS, differed significantly between anabolic sensitive and resistant groups. The targeted simulations indicated that multiple dysregulated mechanisms were required to account for the experimentally observed reductions in MPS in older adults. Therapy simulations showed that single-target interventions could largely restore MPS when isolated mechanisms were perturbed (e.g., increasing mTORC1 sensitivity, enhancing p70S6K levels), but a multifactorial approach was required to recover muscle metabolism when all anabolic resistance mechanisms were present. ConclusionThis study highlights the multifactorial nature of anabolic resistance and the implications for therapy. Specifically, the results motivate new hypotheses regarding the mechanisms most likely to be impaired and argue for multi-target therapeutic strategies to help restore muscle protein metabolism in aging.

systems biology↗

bioRxiv2024

Neural basis of cognitive control signals in anterior cingulate cortex during delay discounting

Cognitive control involves allocating cognitive effort according to internal needs and task demands. The anterior cingulate cortex (ACC) is hypothesized to play a central role in this process. We investigated the neural basis of cognitive control in the ACC of rats performing an adjusting-amount delay discounting task, with a 4s or 8s delay between lever choice and reward. A reinforcement learning model indicated that decision making on this task can be guided by either a value tracking strategy, requiring a resource-based form of cognitive control or a delay-lever biased strategy requiring a resistance-based form of cognitive control. This was then tested in vivo by multiple single unit recordings and local field potentials acquired from male rats performing the task. On this task, the behavioral manifestation of resistance-based control is an excessive focus on delayed lever choices which was observed during a substantial portion of 4s but not 8s delay sessions. On a neural level, this was associated with an increase in Theta (6-12Hz) oscillations prior to delay choices which was present exclusively on 4s delay sessions. By contrast, evidence of a resource-based control signal was found in spike trains that closely tracked lever value prior to choice, and was far more prevalent on 8s delay sessions. These data provide candidate neural signatures of resource-based versus resistance-based forms of cognitive control. While mediated by distinct neural mechanisms, either form could be engaged by individual subjects under different task conditions.

neuroscience↗

bioRxiv2024

Inference of Transcriptional Regulation From STARR-seq Data

One of the primary regulatory processes in cells is transcription, during which RNA polymerase II (Pol-II) transcribes DNA into RNA. The binding of Pol-II to its site is regulated through interactions with transcription factors (TFs) that bind to DNA at enhancer cis-regulatory elements. Measuring the enhancer activity of large libraries of distinct DNA sequences is now possible using Massively Parallel Reporter Assays (MPRAs), and computational methods have been developed to identify the dominant statistical patterns of TF binding within these large datasets. Such methods are global in their approach and may overlook important regulatory sites which function only within the local context. Here we introduce a method for inferring functional regulatory sites (their number, location and width) within an enhancer sequence based on measurements of its transcriptional activity from an MPRA method such as STARR-seq. The model is based on a mean-field thermodynamic description of Pol-II binding that includes interactions with bound TFs. Our method applied to simulated STARR-seq data for a variety of enhancer architectures shows how data quality impacts the inference and also how it can find local regulatory sites that may be missed in a global approach. We also apply the method to recently measured STARR-seq data on androgen receptor (AR) bound sequences, a TF that plays an important role in the regulation of prostate cancer. The method identifies key regulatory sites within these sequences which are found to overlap with binding sites of known co-regulators of AR. 1 Author SummaryWe present an inference method for identifying regulatory sites within a putative DNA enhancer sequence, given only the measured transcriptional output of a set of overlapping sequences using an assay like STARR-seq. It is based on a mean-field thermodynamic model that calculates the binding probability of Pol-II to its promoter and includes interactions with sites in the DNA sequence of interest. By maximizing the likelihood of the data given the model, we can infer the number of regulatory sites, their locations, and their widths. Since it is a local model, it can in principle find regulatory sites that are important within a local context that may get missed in a global fit. We test our method on simulated data of simple enhancer architectures and show that it is able to find only the functional sites. We also apply our method to experimental STARR-seq data from 36 androgen receptor bound DNA sequences from a prostate cancer cell line. The inferred regulatory sites overlap known important regulatory motifs and their ChIP-seq data in these regions. Our method shows potential at identifying locally important functional regulatory sites within an enhancer given only its measured transcriptional output.

genomics↗

bioRxiv2020

Functional mapping of androgen receptor enhancer activity

Androgen receptor (AR) is critical to the initiation, growth and progression of almost all prostate cancers. Once activated, the AR binds to cis-regulatory enhancer elements on DNA that drive gene expression. Yet, there are 10-100x more binding sites than differentially expressed genes. It still remains unclear how individual sites contribute to AR-mediated transcription. While descriptive functional genomic approaches broadly correlate with enhancer activity, they do not provide the locus-specific resolution needed to delineate the underlying regulatory logic of AR-mediated transcription. Therefore, we functionally tested all commonly occuring clinical AR binding sites with Self-Transcribing Active Regulatory Regions sequencing (STARRseq) to generate the first map of intrinsic AR enhancer activity. This approach is not significantly affected by endogenous chromatin modifications and measures the potential enhancer activity at each cis-regulatory element. Interestingly we found that only 7% of AR binding sites displayed increased enhancer activity upon hormonal stimulation. Instead, the vast majority of AR binding sites were either inactive (81%) or constitutively active enhancers (11%). These annotations strongly correlated with enhancer-associated features in both cell line and clinical prostate cancer. With these validated annotations we next investigated the effect of each enhancer class on transcription and found that AR-driven inducible enhancers frequently interacted with promoters, forming central chromosomal loops critical for gene transcription. We demonstrated that these inducible enhancers act as regulatory hubs that increase contacts with both other AR binding sites and gene promoters. This functional map was used to identify a somatic mutation that significantly reduces the expression of a commonly mutated AR-regulated tumour suppressor. Together, our data reveal a complex interplay between different AR binding sites that work in a highly coordinated manner to drive gene transcription.

cancer biology↗

bioRxiv2019

Dynamic changes in Anterior Cingulate Cortex ensembles mark the transition from exploration to exploitation

The ability to acquire knowledge about the value of stimuli or actions factors into simple foraging behaviors as well as complex forms of decision making. The anterior cingulate cortex (ACC) is thought to be involved in these processes, although the manner in which neural representations acquire value is unclear. Here we recorded from ensembles of ACC neurons as rats learned which of 3 levers was rewarded each day through a trial and error process. Action representations remained largely stable during exploration, but there was an abrupt, coordinated and differential change in the representation of rewarded and nonrewarded levers by ACC neurons at the point where the rat realized which lever was rewarded and began to exploit it. Thus, rather than a gradual, incremental process, value learning in ACC can occur in an all-or-none manner and help to initiate strategic shifts in forging behavior.

neuroscience↗