Biology subjects

Alvarez, M.

Publications and source records attributed to Alvarez, M..

32 records · Page 2Linked to original sources

bioRxiv2022

Hierarchical unimodal processing within the primary somatosensory cortex during a bimodal detection task

Where and how in the brain do neurons process more than one sensory modality? To answer these questions, scientists have generated a wide variety of studies at distinct space-time scales in different animal models, and often shown contradictory conclusions. Some conclude that this process occurs in early sensory cortices, but others that this occurs in areas central to sensory cortices. Here, we sought to determine whether sensory neurons process and encode physical stimulus properties of different modalities (tactile and acoustic). For this, we designed a bimodal detection task where the senses of touch and hearing compete from trial to trial. Two Rhesus monkeys performed this novel task, while neural activity was recorded in areas 3b and 1 of the primary somatosensory cortex (S1). We analyzed neurons coding properties and variability, organizing them by their receptive fields position relative to the stimulation zone. Our results indicate that neurons of areas 3b and 1 are unimodal, encoding only the tactile modality, both in the firing rate and variability, but not to the acoustic one. Moreover, we found that neurons of both subareas encode the tactile information differently, revealing a hidden processingbased hierarchy. Finally, using a powerful non-linear dimensionality reduction algorithm, we show that the activity from areas 3b and 1 can be separated, establishing a clear division in the functionality of these two subareas of S1. SIGNIFICANCE STATEMENTOur brain integrates information from all our senses to perceive the external world. But where and how in the brain this integration occurs? Here we ask if the primary somatosensory cortex (S1) encodes information from more than one sensory modality. We recorded the activity of single neurons from areas 3b and S1, while trained monkeys performed a bimodal detection task, where tactile and acoustic stimuli compete. The analysis showed that neurons from areas 3b and 1 responded only to the tactile modality both in their rate and variability. However, our results support that these two areas are different enough as to be considered functionally distinct entities.

neuroscience↗

bioRxiv2022

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

Biobanks that collect deep phenotypic and genomic data across large numbers of individuals have emerged as a key resource for human genetic research. However, phenotypes acquired as part of Biobanks are often missing across many individuals, limiting the utility of these datasets. The ability to accurately impute or "fill-in" missing phenotypes is critical to harness the power of population-scale Biobank datasets. We propose AutoComplete, a deep learning-based imputation method which can accurately impute missing phenotypes in population-scale Biobank datasets. When applied to collections of phenotypes measured across {approx} 300K individuals from the UK Biobank, AutoComplete improved imputation accuracy over existing 2 methods (average improvement in r2 of 18% for all phenotypes and 42% for binary phenotypes). We explored the utility of phenotype imputation for improving the power of genome-wide association studies (GWAS) by applying our method to a group of five clinically relevant traits with an average missigness rate of 83% (67% to 94%) leading to an an increase in effective sample size of {approx}2-fold on average (0.5 to 3.3-fold across the phenotypes). GWAS on the resulting imputed phenotypes led to an increase in the total number of loci significantly associated to the traits from four to 129. Our results demonstrate the utility of deep-learning based imputation to increase power for genetic discoveries in existing biobank data sets.

bioinformatics↗

bioRxiv2022

Fanca and p53 genes cooperate to suppress oral SCC in transgenic mice

Fanconi anemia (FA) patients frequently develop oral squamous cell carcinoma (OSCC). This cancer in FA patients is diagnosed within the first 3-4 decades of life, very often preceded by lesions that suffer a malignant transformation. In addition, they respond poorly to current treatments due to toxicity or multiple recurrences. Translational research of new chemopreventive agents and therapeutic strategies has been unsuccessful partly due to scarcity of disease models or failure to fully reproduce the disease. Here we report that Fanca gene knockout mice (Fanca-/-) frequently display pre-malignant lesions in the oral cavity. Moreover, when these animals were crossed with animals having conditional deletion of Trp53 gene in oral mucosa (K14cre;Trp53F2-10/F2-10), they spontaneously developed OSCC with a high penetrance and a median latency of less than ten months. Tumors were well differentiated and expressed markers of squamous differentiation, such as keratins K5 and K10. In conclusion, Fanca and Trp53 genes cooperate to suppress oral cancer in mice, and Fanca-/-;K14cre;Trp53F2-10/F2-10 mice constitute the first animal model of spontaneous OSCC in FA.

cancer biology↗

bioRxiv2022

The OncoLoop network-based precision oncology framework

At present, prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not feasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts using Master Regulator (MR) conservation analysis revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient-derived xenograft (PDX). OncoLoop is a highly generalizable framework that can be extended to other cancers and potentially other diseases. Significance StatementOncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround co-clinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.

cancer biology↗

bioRxiv2021

Single-cell dissection of obesity-exercise axis in adipose-muscle tissues

Regular physical exercise has long been recognized to reverse the effects of diet-induced obesity, but the molecular mechanisms mediating these multi-tissue beneficial effects remain uncharacterized. Here, we address this challenge by studying the opposing effects of exercise training and high-fat diet at single-cell, deconvolution and tissue-level resolutions across 3 metabolic tissues. We profile scRNA-seq in 204,883 cells, grouped into 53 distinct cell subtypes/states in 22 major cell types, from subcuta-neous and visceral white adipose tissue (WAT), and skeletal muscle (SkM) in mice with diet and exercise training interventions. With a great number of mesenchymal stem cells (MSCs) profiled, we compared depot-specific adipose stem cell (ASC) states, and defined 7 distinct fibro-adipogenic progenitor (FAP) states in SkM including discovering and validating a novel CD140+/CD34+/SCA1-FAP population. Exercise- and obesity-regulated proportion, transcriptional and cell-cell interaction changes were most strongly pronounced in and centered around ASCs, FAPs, macrophages and T-cells. These changes reflected thermogenesis-vs-lipogenesis and hyperplasia-vs-hypertrophy shifts, clustered in pathways including extracellular matrix remodeling and circadian rhythm, and implicated complex single- and multi-tissue communication including training-associated shift of a cytokine from binding to its decoy receptor on ASCs to true receptor on M2 macrophages in vWAT. Overall, our work provides new insights on the metabolic protective effects of exercise training, uncovers a previously-underappreciated role of MSCs in mediating tissue-specific and multi-tissue effects, and serves as a model for multitissue single-cell analyses in physiologically complex and multifactorial traits exemplified by obesity and exercise training.

physiology↗

bioRxiv2021

Allogeneic CAR-invariant Natural Killer T Cells Exert Potent Antitumor Effects Through Host CD8 T cell Cross-Priming

The development of allogeneic chimeric antigen receptor (CAR) T cell therapies for off-the-shelf use is a major goal yet faces two main immunological challenges, namely the risk of graft-versus-host-disease (GvHD) induction by the transferred cells and the rejection by the host immune system limiting their persistence. We demonstrate that allogeneic CAR-engineered invariant natural killer T (iNKT) cells, a cell population without GvHD-induction potential that displays immunomodulatory properties, exerted potent direct and indirect antitumor activity in murine models of B-cell lymphoma when administered across major MHC-barriers. In addition to their known direct cytotoxic effect, allogeneic CAR iNKT cells induced tumor-specific antitumor immunity through host CD8 T cell cross-priming, resulting in a potent antitumor effect lasting longer than the physical persistence of the allogeneic cells. The utilization of off-the-shelf allogeneic CAR iNKT cells could meet significant unmet needs in the clinic.

immunology↗

bioRxiv2020

Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation

Polyploid genomes pose several inherent challenges to population genetic analyses. While alignment-based methods are fundamentally limited in their applicability to polyploids, alignment-free methods bypass most of these limits. We investigated the use of Mash, a k-mer analysis tool that uses the MinHash method to reduce complexity in large genomic datasets, for basic population genetic analyses of polyploid sequences. We measured the degree to which Mash correctly estimated pairwise genetic distance in simulated diploid and polyploid short-read sequences with various levels of missing data. Mash-based estimates of genetic distance were comparable to alignment-based estimates, and were less impacted by missing data. We also used Mash to analyze publicly available short-read data for three polyploid and one diploid species, then compared Mash results to published results. For both simulated and real data, Mash accurately estimated pairwise genetic differences for polyploids as well as diploids as much as 476 times faster than alignment-based methods, though we found that Mash genetic distance estimates could be biased by per-sample read depth. Mash may be a particularly useful addition to the toolkit of polyploid geneticists for rapid confirmation of alignment-based results and for basic population genetics in reference-free systems with poor quality DNA.

genetics↗

bioRxiv2020

Patterns of population genomic diversity in the invasive Japanese knotweed species complex

PremiseInvasive species are expected to experience a reduction in genetic diversity due to founder effects, which should limit their ability to adapt to new habitats. Still, many invasive species achieve widespread distributions and dense populations. This paradox of invasions could potentially be overcome through multiple introductions or hybridization, both of which increase genetic diversity. We conducted a population genomics study of Japanese knotweed (Reynoutria japonica), which is a polyploid, clonally reproducing invasive species that has been notoriously successful worldwide despite supposedly low genetic diversity. MethodsWe used Genotyping-by-Sequencing to collect 12,912 SNP markers from 88 samples collected at 38 locations across North America for the species complex. We used non-alignment based k-mer hashing analysis in addition to traditional population genetic analyses to account for the challenges of genotyping polyploids. ResultsGenotypes conformed to three genetic clusters, likely representing Japanese knotweed, Giant knotweed, and hybrid Bohemian knotweed. We found that, contrary to previous findings, the Japanese knotweed cluster had substantial genetic diversity, though it had no apparent genetic structure across the landscape. In contrast, Giant knotweed and hybrids showed distinct population groups. We did not find evidence of Isolation-by-Distance in the species complex, likely reflecting the stochastic introduction history of this species complex. Among species, we found no correlations between SNPs and several temperature- and precipitation-based climatic variables. ConclusionsThe results indicate that clonal invasive species can show substantial genetic diversity and can be successful at colonizing a variety of habitats without showing evidence of local adaptation or genetic structure.

evolutionary biology↗

bioRxiv2020

From an invariant sensory code to a perceptual categorical code in second somatosensory cortex

A crucial role of cortical networks is the conversion of sensory inputs into perception. In the cortical somatosensory network, neurons of the primary somatosensory cortex (S1) show invariant sensory responses, while frontal lobe neuron responses correlate with the animals perceptual behavior. But, where in the cortical somatosensory network are the sensory inputs transformed into perceptual behavior? Here, we report that in the secondary somatosensory cortex (S2), neurons with invariant sensory responses coexist with neurons whose responses correlate with the animals perceptual behavior. These distinct neural responses exhibit analogous timescales of intrinsic fluctuations, suggesting that they belong to the same hierarchical processing stage. Furthermore, during a non-demanding control task, the sensory responses remained unaltered while perceptual responses vanished. Conclusively, the S2 population responses exhibit intermediate dynamics between S1 and frontal lobe neurons. These results suggest that the conversion of touch into perception crucially depends on S2.

neuroscience↗

bioRxiv2019

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

bioinformatics↗

bioRxiv2019

A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionally-distinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highly-specific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

bioinformatics↗

bioRxiv2019

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and single-nucleus RNA-seq (snRNA-seq) data, Bisque was able to replicate previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. Bisque requires a single-cell reference dataset that reflects physiological cell type composition and can further leverage datasets that includes both bulk and single cell measurements over the same samples for improved accuracy. We further propose an additional mode of operation that merely requires a set of known marker genes. Bisque is available as an R package at: https://github.com/cozygene/bisque.

bioinformatics↗

bioRxiv2018

Reduced-representation bisulfite sequencing finds novel epigenome-wide response to oil pollution in the foundation plant Spartina alterniflora

Theory predicts that environmental challenges can shape the composition of populations, which is manifest at the molecular level. Previously, we demonstrated that oil pollution affected gene expression patterns and altered genetic variation in natural populations of the foundation salt marsh grass, Spartina alterniflora. Here, we used a reduced representation bisulfite sequencing approach, epigenotyping by sequencing (epiGBS), to examine relationships among DNA sequence, DNA methylation, gene expression, and exposure to oil pollution. We documented genetic and methylation differentiation between oil-exposed and unexposed populations, suggesting that the Deepwater Horizon oil spill may have selected on genetic variation, and either selected on epigenetic variation or induced particular epigenotypes and expression patterns in exposed compared to unexposed populations. In support of the potential for differential response to the Deepwater Horizon oil spill, we demonstrate genotypic differences in response to oil under controlled conditions. Overall, these findings demonstrate genetic variation, epigenetic variation and gene expression are correlated to exposure to oil pollution, which may all contribute to the response to environmental stress.

ecology↗

bioRxiv2018

Single-neuron interactions between the somatosensory thalamo-cortical circuits during perception

Sensory thalamo-cortical interactions are key components of the neuronal chains associated with stimulus perception, but surprisingly, they are poorly understood. We addressed this problem by evaluating a directional measure between simultaneously recorded neurons from somatosensory thalamus (VPL) and somatosensory cortex (S1) sharing the same cutaneous receptive field, while monkeys judged the presence or absence of a tactile stimulus. During the stimulus-presence, feedforward (VPL[->]S1) interactions increased, while pure feedback (S1[->]VPL) interactions were unaffected. Remarkably, bidirectional interactions (VPL{leftrightarrow}S1) emerged with high stimulus amplitude, establishing a functional thalamo-cortical loop. Furthermore, feedforward interactions were modulated by task context and error trials. Additionally, significant stimulus modulations were found on intra-cortical (S1[->]S1) interactions, but not on intra-thalamic (VPL[->]VPL) interactions. Thus, these results show the directionality of the information flow between the thalamo-cortical circuits during tactile perception. We suggest that these interactions may contribute to stimulus perception during the detection task used here.

neuroscience↗