Biology subjects

Pimentel, H.

Publications and source records attributed to Pimentel, H..

17 recordsLinked to original sources

bioRxiv2026

Distinct activation mechanisms underlie ligand efficacy at a GPCR

Ligands that bind to the same G protein-coupled receptor (GPCR) can produce widely different levels of signaling. These differences in efficacy are central to pharmacology, yet how a receptor sequence determines signaling level remains incompletely understood. Here we develop a deep mutational pharmacology approach to measure how each residue in the -opioid receptor (OR) shapes responses to 15 opioids, yielding ~400,000 measurements of variant effects on signaling and expression, including full concentration-response series for three agonists. Mutational effects on potency are strongest near the orthosteric pocket, whereas effects on efficacy extend through the transmembrane core and intracellular face. Ligands share many functional determinants, but sensitivity to mutation differs with efficacy. As efficacy decreases, the residues required for signaling shift from the binding pocket towards the intracellular face; partial agonists therefore depend on residues that are dispensable for full agonists. Mutations can broadly increase receptor responses, allowing antagonists such as naloxone to produce signaling comparable to a full agonist and increasing the efficacy of partial agonist ligands. Combined mutations can also reorder the relative efficacies of ligands, showing that sequence changes reshape pharmacological differences rather than simply increasing or decreasing all responses together. These findings support a model in which ligands of different efficacy activate the receptor through distinct allosteric mechanisms which are determined by residues distributed across the receptor. Mapping these contributions across diverse ligands with deep mutational pharmacology provides a way to understand how one receptor converts molecular recognition into a range of signaling outputs.

biophysics↗

bioRxiv2026

keju: powerful and accurate inference in Massively Parallel Reporter Assays

Massively Parallel Reporter Assays (MPRAs) interrogate the regulatory function of thousands of designed genetic elements in parallel through linked DNA and RNA readouts using an engineered construct and attached minimal reporter. Given the complexity of MPRA experimental designs, several different sources of uncertainty complicate inference. We show that previous methods do not account for substantial differences in uncertainty levels between the DNA and RNA counts and between batches. Accordingly, we present keju, a hierarchical statistical model that estimates candidate transcription rate, differential activity between conditions, and effects from promoter composition for MPRA data. To maximize statistical power and improve false positive rate control, keju conditions on the DNA counts to model batch-specific and modality-specific uncertainty in the RNA counts. keju shows vastly improved sensitivity (59%) in simulations compared to previous methods (31% for MPRAnalyze and 9% for BCalm), and also has lower, more robust false positive rates, calling only 6.8% of unlabeled negative controls significant in real data (compared to 34% for MPRAnalyze and 12% for BCalm).

bioinformatics↗

bioRxiv2025

Cell villages and Dirichlet modeling map human cell fitness genetics

The capacity of cells to proliferate and survive is central to development and disease. Assays that measure cell fitness are therefore a cornerstone of biology, but traditional techniques lack donor diversity and have high technical variability that impedes scale and reproducibility. To overcome these barriers, we designed and validated a "cell village"-based fitness screening approach using pooled cultures of 12-39 genetically distinct human neural progenitor cell (NPC) lines. We also developed Townlet to establish a foundational statistical framework based on Dirichlet regression for analyzing proportional data from cell villages. Applying these systems, we identified hyperproliferation in NPCs harboring the autism risk factor chromosome 16p11.2 deletion, mapped common genetic variants near ZFHX3 associated with NPC proliferation rate, and discovered genetic modifiers of lead (Pb) sensitivity implicating ARNT2. Together, these experimental and analytical tools advance a scalable, genetically diverse in vitro platform for dissecting human variation in cell fitness and gene-environment interactions.

genetics↗

bioRxiv2025

Multiplexed Perturbation Enables Scalable Pooled Screens

CRISPR-based genetic perturbation screens have revolutionized the ability to link genes to cellular phenotypes with unprecedented precision and scale. However, conventional pooled CRISPR screens require large cell numbers to achieve adequate sgRNA representation, posing technical and financial challenges. Here, we investigate the impact of co-delivery of multiple guide RNAs via high multiplicity of infection (MOI) in pooled CRISPR interference (CRISPRi) screens as a strategy to enhance screening efficiency while reducing cell numbers. We systematically evaluate screen performance across varying MOIs, assessing the effects of multiplexing on knockdown efficiency, sgRNA representation, and potential interference of multiple sgRNA phenotypes. Our data demonstrate that sgRNA multiplexing (MOI 2.5-10) can maintain screen performance while enabling significant reductions in cell number requirements. We further apply these optimized conditions to conduct a genome-wide CRISPR screen for regulators of the intracellular adhesion molecule ICAM-1, successfully identifying novel candidates using as few as half a million cells. This study provides a framework for adopting multiplexed sgRNA strategies to streamline CRISPR screening applications in resource-limited settings.

molecular biology↗

bioRxiv2025

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions

Multi-phenotype deep mutational scanning (DMS) experiments provide a powerful means to dissect how protein variants affect different layers of molecular function, such as abundance, surface expression, and ligand binding. When these phenotypes are connected through a molecular pathway, interpreting variant effects becomes challenging because downstream phenotypes often reflect both direct and indirect consequences of mutation. We introduce Cosmos, a Bayesian framework for residue-level causal inference in multi-phenotype DMS data. Cosmos addresses three key questions: (1) whether a causal relationship exists between two phenotypes; (2) the strength of that relationship; and (3) the expected downstream phenotype if the upstream phenotype were normalized, enabling counterfactual interpretation. The framework uses position-level aggregation and Bayesian model selection to infer interpretable causal structures, without requiring phenotype-specific biophysical assumptions. We apply Cosmos to three datasets--Kir2.1 (abundance and surface expression), PSD95-PDZ3 (abundance and CRIPT binding), and KRAS (abundance and RAF1-RBD binding) and show that it effectively distinguishes direct from indirect functional effects. Across these applications, Cosmos provides a generalizable and interpretable approach to disentangle causal relationships in high-throughput protein functional screens.

bioinformatics↗

bioRxiv2025

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular phenotypes, such as abundance or activity. Analysis of DMS experiments with a FACS readout is challenging due to measurement variance and the unique multidimensional nature of the phenotype. However, no statistical method has yet been developed to address the challenges of FACS-based DMS. Here we present Lilace, a Bayesian statistical model to estimate variant effects with uncertainty quantification from FACS-based DMS experiments. We validate Lilaces performance and robustness using simulated data and apply it to OCT1 and Kir2.1 DMS experiments, demonstrating an improved false discovery rate (FDR) while largely maintaining sensitivity.

bioinformatics↗

bioRxiv2025

Rosace-AA: Enhancing Interpretation of Deep Mutational Scanning Data with Amino Acid Substitution and Position-Specific Insights

Proteins are dynamic systems whose function and behavior are sensitive to environmental conditions and often involve multiple cellular roles. Deep mutational scanning (DMS) experiments generate extensive datasets to capture the functional consequences of mutations. However, the sheer volume of data presents challenges in visualization and interpretation. Current approaches often rely on heatmaps, but these methods fail to capture the nuanced effects of amino acid (AA) substitutions, which are essential for understanding mutational impact. To address this, we extend the Rosace framework with Rosace-AA, a model that incorporates both position-specific information and AA substitution trends. Using substitution matrices like BLOSUM90, Rosace-AA offers a flexible and interpretable approach to summarize DMS data oil both protein-level and position-level. We demonstrate its utility across datasets, including OCTI and MET kinase, showing that Rosace-AA highlights key positions where mutations deviate from expected substitution patterns and captures functionally relevant variation in protein behavior across multiple DMS screens. These results suggest that Rosace-AA enables more robust and interpretable analysis of complex DMS datasets.

bioinformatics↗

bioRxiv2024

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of [~]5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I [1/2] inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

cancer biology↗

bioRxiv2024

A model for accurate quantification of CRISPR effects in pooled FACS screens

CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of marker genes, followed by guide RNA (gRNA) sequencing. Analysis of these data presents several statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our experimental design model findings that, when using Waterbear for analysis, high power is maintained even at low cell coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens.

genomics↗

bioRxiv2023

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

bioinformatics↗

bioRxiv2023

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette, a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.

bioinformatics↗

bioRxiv2023

Accounting for Isoform Expression in eQTL Mapping Substantially Increases Power

A core problem in genetics is eQTL mapping, in which genetic variants associated with changes in expression of genes are identified. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. In this study, we provide a systematic evaluation of methods for accounting for individual isoform expression levels based on generative isoform expression heritability models and real data. Over a range of conditions, we show that these approaches substantially increase the power to map eQTLs in both simulations and commonly analyzed large data sets. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.

genetics↗

bioRxiv2021

Effect sizes of causal variants for gene expression and complex traits differ between populations

Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

genetics↗

bioRxiv2019

Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data

*Seq techniques (e.g. RNA-Seq) generate compositional datasets, i.e. the number of fragments sequenced is not proportional to the samples total RNA content. Thus, datasets carry only relative information, even though absolute RNA copy numbers are of interest. Current normalization methods assume most features do not change, which can lead to misleading conclusions when there are many changes. Furthermore, there are few real datasets and no simulation protocols currently available that can directly benchmark methods when many changes occur. We present absSimSeq, an R package that simulates compositional data in the form of RNA-Seq reads. We compared absSimSeq with several existing tools used for RNA-Seq differential analysis: sleuth, DESeq2, edgeR, limma, sleuth and ALDEx2 (which explicitly takes a compositional approach). We compared the standard normalization of these tools to either "compositional normalization", which uses log-ratios to anchor the data on a set of negative control features, or RUVSeq, another tool that directly uses negative control features. Our analysis shows that common normalizations result in reduced performance with current methods when there is a large change in the total RNA per cell. Performance improves when spike-ins are included and used with a compositional approach, even if the spike-ins have substantial variation. In contrast, RUVSeq, which normalizes count data rather than compositional data, has poor performance. Further, we show that previous criticisms of spike-ins did not take into consideration the compositional nature of the data. We demonstrate that absSimSeq can generate more representative datasets for testing performance, and that spike-ins should be more frequently used in a compositional manner to minimize misleading conclusions in differential analyses. Author SummaryA critical question in biomedical research is "Is there any change in the RNA transcript abundance when cellular conditions change?" RNA Sequencing (RNA-Seq) is a powerful tool that can help answer this question, but two critical parts of obtaining accurate measurements are (A) understanding the kind of data that RNA-Seq produces, and (B) "normalizing" the data between samples to allow for a fair comparison. Most tools assume that RNA-Seq data is count data, but in reality it is "compositional" data, meaning only percentages/proportions are available, which cannot directly answer the critical question. This leads to distorted results when attempting to simulate or analyze data that has a large global change. To address this problem, we designed a new simulation protocol called absSimSeq that can more accurately represent RNA-Seq data when there are large changes. We also proposed a "compositional normalization" method that can utilize "negative control" features that are known to not change between conditions to anchor the data. When there are many features changing, this approach improves performance over commonly used normalization methods across multiple tools. This work highlights the importance of having negative controls features available and of treating RNA-Seq data as compositional.

bioinformatics↗

bioRxiv2018

Intron retention as a novel source of cancer neoantigens

Personalized cancer vaccine strategies directed at tumor neoantigens derived from somatic mutations in the DNA are currently under prospective evaluation1, 2. Alterations in tumor RNA, rather than DNA, may also represent a previously-unexplored source of neoantigens. Here, we show that intron retention, a widespread feature of cancer transcriptomes3, 4, represents a novel source of tumor neoantigens. We developed an in silico approach to identify retained intron neoantigens from RNA sequencing data and applied this methodology to tumor samples from patients with melanoma treated with immune checkpoint blockade5, 6, discovering that the retained intron neoantigen burden in these samples augments the DNA-derived, somatic neoantigen burden. We validated the existence of retained intron derived neoantigens by implementing this technique on cancer cell lines with mass spectrometry-derived immunopeptidome data7, 8, revealing that retained intron neoantigens were complexed with MHC I experimentally. Unexpectedly, we observed a trend toward lack of clinical benefit from immune checkpoint blockade in high retained intron load-tumors, which harbored transcriptional signatures consistent with cell cycle dysregulation and DNA damage repair. Our results demonstrate the contribution of transcriptional dysregulation to the overall burden of tumor neoantigens, provide a foundation for augmenting personalized cancer vaccine development with a new class of tumor neoantigens, and demonstrate how global transcriptional dysregulation may impact selective response to immune checkpoint blockade.\n\nStatement of significanceWe developed and experimentally validated a computational pipeline to identify a novel class of tumor neoantigens derived from RNA-based intron retention, which is prevalent throughout cancer transcriptomes. The discovery of transcriptionally-derived tumor neoantigens expands the tumor immunopeptidome and contributes potential substrates for personalized cancer vaccine development.

cancer biology↗

bioRxiv2017

Gene-level differential analysis at transcript-level resolution

Gene-level differential expression analysis based on RNA-Seq is more robust, powerful and biologically actionable than transcript-level differential analysis. However aggregation of transcript counts prior to analysis results can mask transcript-level dynamics. We demonstrate that aggregating the results of transcript-level analysis allow for gene-level analysis with transcript-level resolution. We also show that p-value aggregation methods, typically used for meta-analyses, greatly increase the sensitivity of gene-level differential analyses. Furthermore, such aggregation can be applied directly to transcript compatibility counts obtained during pseudoalignment, thereby allowing for rapid and accurate model-free differential testing. The methods are general, allowing for testing not only of genes but also of any groups of transcripts, and we showcase an example where we apply them to perturbation analysis of gene ontologies.

bioinformatics↗

bioRxiv2017

Fusion detection and quantification by pseudoalignment

RNA sequencing in cancer cells is a powerful technique to detect chromosomal rearrangements, allowing for de novo discovery of actively expressed fusion genes. Here we focus on the problem of detecting gene fusions from raw sequencing data, assembling the reads to define fusion transcripts and their associated breakpoints, and quantifying their abundances. Building on the pseudoalignment idea that simplifies and accelerates transcript quantification, we introduce a novel approach to fusion detection based on inspecting paired reads that cannot be pseudoaligned due to conflicting matches. The method and software, called pizzly, filters false positives, assembles new transcripts from the fusion reads, and reports candidate fusions. With pizzly, fusion detection from raw RNA-Seq reads can be performed in a matter of minutes, making the program suitable for the analysis of large cancer gene expression databases and for clinical use. pizzly is available at https://github.com/pmelsted/pizzly

bioinformatics↗