Biology subjects

Oliva, M.

Publications and source records attributed to Oliva, M..

At least 19 recordsLinked to original sources

bioRxiv2025

Multicellular Calcium Waves in Cancer-Associated Fibroblasts Regulate Neuronal Mimicry and Anisotropy Leading to Immune Exclusion

Stromal barriers exclude CD8+ T cells from accessing cancer cells and hamper immune-mediated tumour control. Through multi-pronged analysis of tumours that transition from immune inflamed to immune excluded, we reveal that the formation of stromal barriers is associated with the acquisition of neuronal gene expression programmes in cancer-associated fibroblasts (CAFs), including TUBB3 expression. This leads to neuronal mimicry, with stromal barrier formation underpinned by coordinated transient bursts of intracellular calcium release, similar to those observed in neuronal tissue. Blockade of calcium release through either pharmacological or molecular interventions, such as STC2 depletion, prevents CAF alignment and the build-up of CD8+ T cells at stromal boundaries. Nintedanib treatment prevents neuronal mimicry and restores immune-mediated tumour control. Thus, we uncover unexpected mimicry of neuronal behaviour in CAFs, document the mechanism by which it leads to immune exclusion, and identify ways to prevent the induction of neuronal mimicry and restore immune-mediated tumour control. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/684281v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@938ee5org.highwire.dtl.DTLVardef@18a9c48org.highwire.dtl.DTLVardef@11a5982org.highwire.dtl.DTLVardef@ea6a79_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Structural basis for selective remdesivir incorporation by SARS-CoV-2 RNA polymerase, and S759A resistance

Nucleoside analogs (NAs) have been successfully used to treat viral infections. dNTP analogs are primarily DNA chain terminators, while NTP analogs, such as remdesivir, can inhibit as delayed chain terminators or when in the template strand. Determining the frequency of remdesivir triphosphate (RTP) incorporation in the presence of the competing ATP can help in understanding different modes of viral RNA-dependent RNA polymerase (RdRp) inhibition by NTP analogs. We employed enzymatic assays, mass spectrometry, and cryo-EM to show that SARS-CoV-2 RdRp preferentially incorporates RTP, outcompeting 10-fold higher ATP concentration; however, successive RTP incorporations are less favoured when ATP is present. Structures of SARS-CoV-2 RdRp in this and previous studies demonstrate resilience of remdesivir:UMP base pair to translocation, explaining the reduced preference for conjugate incorporations. Together, the RTP versus ATP incorporation is driven by their relative concentration and structural rigidity of remdesivir:UMP, ultimately limiting the number of incorporated remdesivir in a fully synthesized RNA strand. The S759A mutant confers RTP resistance, and the structures of S759A RdRp catalytic complexes reveal that altered ribose-ring conformation and repositioning of the primer 3'-end nucleotide contribute to RTP resistance. These findings enhance our understanding of non-obligate NTP analogs and provide insight into S759A resistance mechanism.

biochemistry↗

bioRxiv2025

Parabricks: GPU Accelerated Universal Pan-Instrument Genomics Analysis Software Suite

Next-generation sequencing (NGS) has transformed genomics, enabling breakthroughs in biotechnology, healthcare, and pharmaceuticals. However, exponential data growth-outpacing Moores Law-presents critical computational challenges for secondary analysis. We introduce Parabricks, a freely accessible, GPU-accelerated software suite supporting diverse workflows, including wholegenome, exome, transcriptome, and methylation analysis. Compatible across major short read and long read sequencing platforms, it reduces processing time from hours to minutes while maintaining concordance with established standards, this acceleration translates to significant cost savings. Deployable on-premises or through cloud platforms, Parabricks ensures scalable, genomic processing. Integrated AI-driven workflows and deep learning models further enhance precision, offering a universal solution for high-throughput genomic research and clinical diagnostics.

bioinformatics↗

bioRxiv2025

Random mutagenesis of influenza hemagglutinin identifies new sites which modulate its acid-stability and cleavability

The structural instability of influenza hemagglutinin (HA) is related to its function in low pH-mediated membrane fusion, which requires prior cleavage of the premature HA0 by a host protease. The precise determinants underlying the stability and cleavability of HA remain to be fully understood and have implications for risk assessment of zoonotic influenza A viruses (IAV), viral transmissibility and vaccine production. To address this, we conducted random mutagenesis on early 2009 pandemic H1 HA, followed by selection of acid-stable viruses and detailed profiling of the mutant HAs. This resulted in identification of four mutations, which increase the acid-stability and decrease the fusion-promoting activity of H1 HA, without compromising viral entry and replication in cells. The newly recognized mutations are situated in the globular head, vestigial esterase and membrane-proximal part of H1 HA, in regions involved in the refolding of HA at low pH. A fifth mutation, D346N, is located in the cleavage loop and renders H1 HA0 12-fold resistant to trypsin activation, whereas its cleavage by transmembrane serine protease 2 (TMPRSS2) is not affected. Along this line, we found that the poor cleavage of H16 HA0, which is unusual in carrying an N346 residue, only applies when it is performed by extracellular proteases. Since H16 HA also exhibits a very low fusion pH, we propose that gull H16N3 virus may carry a much more stable HA than other avian IAVs. Collectively, our mutagenesis approach revealed new determinants of HA stability and cleavability, with relevance for viral surveillance and vaccine production. IMPORTANCEThe presence of influenza A viruses (IAV) throughout the animal world, particularly avian species, represents a constant threat for zoonotic infections or a new influenza pandemic. To be transmissible among humans, a zoonotic IAV requires mutations in the viral hemagglutinin (HA) that increase the acid-stability of this mediator of viral entry. Understanding the determinants of HA stability is also important to produce vaccines with high shelf-stability. By combining random mutagenesis with selection of acid-stable viruses, we identified new stabilizing mutations located in different parts of HA. Besides, we discovered a mutation that renders HA resistant to cleavage by extracellular proteases. Since this residue is naturally occurring in H16 HA, we propose that the gull H16N3 virus may differ from other avian IAVs in carrying an environmentally stable HA. Hence, our study delivers new insight in factors that modulate HA acid-stability and cleavability, with relevance for viral surveillance and vaccine production.

microbiology↗

bioRxiv2024

Recursive RePair: Increasing the Scalability of RePair by Decreasing Memory Usage

The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as Re2Pair), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare Re2Pair to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method Re2Pair achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. Re2Pair is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair 2012 ACM Subject ClassificationTheory of computation [->] Formal languages and automata theory

genomics↗

bioRxiv2024

Diversification of gene expression across extremophytes and stress-sensitive species in the Brassicaceae

Plants are composed of diverse cell types that facilitate adaptations to the environment, yet cross-species comparisons of such response programs at single-cell resolution remain scarce. To explore this diversity, here we profiled >200,000 root cells from five Brassicaceae species, including stress-sensitive species (Arabidopsis thaliana, Sisymbrium irio), extremophytes (Eutrema salsugineum, Schrenkiella parvula), and the polyploid crop Camelina sativa under control, NaCl, and abscisic acid (ABA) treatments. We developed a computational pipeline to characterize the conservation and divergence of cell-type gene expression across the Brassicaceae, revealing that approximately half of previously defined Arabidopsis cell-type markers fail to maintain conserved expression in one or more non-Arabidopsis species. Therefore, we curated a refined set of pan-Brassicaceae markers and identified orthologs whose expression profiles have diverged across lineages. Using in situ hybridization, we mapped distinct cortex subpopulations to specific cortical layers across species, and found flavonoid biosynthesis programs preferentially localized to the inner cortex layer, linking cortex-layer specification to metabolic specialization. Cell-type contributions to stress responses differed among species and across treatments, with lineage-specific losses of responsiveness occurring less frequently but evolutionarily more favored than lineage-specific gains. In C. sativa, sub-genomes contributed equally to stress responses, and homeologs with divergent responses typically lacked other signatures of functional divergence. Together, this work establishes a foundational root single-cell atlas and an analytical framework for multi-species comparative transcriptomics, providing insights into how stress responses diversify across cell types, stress-sensitive to stress-adapted species, and in crop lineages.

plant biology↗

bioRxiv2024

Emerging neurodevelopmental mechanisms in patient induced pluripotent stem cells-derived spheroids modelling SCN1A Dravet Syndrome

SCN1A encodes Na1.1, a voltage-gated sodium channel preferentially expressed in GABAergic interneurons, and it is the major cause of Dravet Syndrome (DS), a rare condition of developmental and epileptic encephalopathy (DEE). Among over 1000 DS mutations reported to date, almost all cause SCN1A loss-of function (LoF). A reduction in NaV1.1 function in inhibitory neurons would subsequently cause an over-excitation of glutamatergic neurons resulting in seizures, which are exacerbated by the use of sodium channel blocking common anti-seizure medications (ASM). In this study we generated and assessed 3D spheroids enriched with GABAergic neurons from SCN1A DS patient to establish a 3D human-derived DS model. To investigate developmental disruptions in DS pathophysiology we profiled the transcriptome of patient-derived spheroids and subsequently, tested the capability of this 3D in vitro model to reveal the cellular mechanisms of DS and predict drug response. In summary, our patient iPSC-derived neuronal model of SCN1A DS revealed a profound dysregulation of developmental processes which correlated with functional disruption in GABAergic neurons and predicted response to fenfluramine, an ASM increasingly used for the treatment of DS.

neuroscience↗

bioRxiv2023

ONeSAMP 3.0: Effective Population Size via SNP Data for One Population Sample

O_LIThe genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Genetic estimators of (Ne) increasingly popular tools in population and conservation genetic studies. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. C_LIO_LIWe present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism (SNP) data collected from a single population sample using Approximate Bayesian Computation and local linear regression. C_LIO_LIWe demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is robust to the number of individual samples and number of loci included in and appears accurate even if the range of true Ne values is large. C_LIO_LIThis method is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU license at https://github.com/AaronHong1024/ONeSAMP_3 and also available with Bioconda (https://bioconda.github.io/index.html). C_LI

bioinformatics↗

bioRxiv2023

Building a Pangenome Alignment Index via Recursive Prefix-Free Parsing

MotivationPangenomics alignment has emerged as an opportunity to reduce bias in biomedical research. Traditionally, short read aligners--such as Bowtie and BWA--were used to index a single reference genome, which was then used to find approximate alignments of reads to that genome. Unfortunately, these methods can only index a small number of genomes due to the linear-memory requirement of the algorithms used to construct the index. Although there are a couple of emerging pangenome aligners that can index a larger number of genomes more algorithmic progress is needed to build an index for all available data. ResultsEmerging pangenomic methods include VG, Giraffe, and Moni, where the first two methods build an index a variation graph from the multiple alignment of the sequences, and Moni simply indexes all the sequences in a manner that takes the repetition of the sequences into account. Moni uses a preprocessing technique called prefix-free parsing to build a dictionary and parse from the input--these, in turn, are used to build the main run-length encoded BWT, and suffix array of the input. This is accomplished in linear space in the size of the dictionary and parse. Therein lies the open problem that we tackle in this paper. Although the dictionary scales nicely (sub-linear) with the size of the input, the parse becomes orders of magnitude larger than the dictionary. To scale the construction of Moni, we need to remove the parse from the construction of the RLBWT and suffix array. We accomplish this, in this paper by applying prefix-free parsing recursively on the parse. Although conceptually simple, this leads to an algorithmic challenge of constructing the RLBWT and suffix array without access to the parse. We solve this problem, implement it, and demonstrate that this improves the construction time by a factor of 8.9 the running time and by a factor of 2.7 the memory required. AvailabilityOur implementation is open source and available at https://github.com/marco-oliva/r-pfbwt. ContactMarco Oliva at marco.oliva@ufl.edu

bioinformatics↗

bioRxiv2023

Establishment of cell transcriptional identity during seed germination

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary to establish in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell-type specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell-type specific transcription begins and changes through germination. Here we describe a temporal analysis of the germinating Arabidopsis embryo at single-cell resolution. We define the highly dynamic cell-type specific patterns of gene expression and how these relate to changing cellular function as germination progresses. Underlying these are unique gene regulatory networks and transcription factor activity. We unexpectedly discover that most embryo cells transition through the same initial transcriptional state early in germination, after which cell-type specific gene expression is established. Furthermore, our analyses support previous findings that the earliest events leading to the induction of embryo growth take place in the vasculature. Overall, our study constitutes a general framework to characterise Arabidopsis cell states through embryo growth, allowing investigation of different genotypes and other plant species whose seed strategies may differ.

plant biology↗

bioRxiv2023

Recursive Prefix-Free Parsing for Building Big BWTs

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings suffixes (of more than a certain length) is a proper prefix of any of the other substrings suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the dictionary. The parse is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, recursive prefix-free parsing, which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt.

genomics↗

bioRxiv2022

PHYTOMap: Multiplexed single-cell 3D spatial gene expression analysis in plant tissue

Retrieving the complex responses of individual cells in the native three-dimensional tissue context is crucial for a complete understanding of tissue functions. Here, we present PHYTOMap (Plant HYbridization-based Targeted Observation of gene expression Map), a multiplexed fluorescence in situ hybridization method that enables single-cell and spatial analysis of gene expression in whole-mount plant tissue in a transgene-free manner and at low cost. We applied PHYTOMap to simultaneously analyze 28 cell type marker genes in Arabidopsis roots and successfully identified major cell types, demonstrating that our method can substantially accelerate the spatial mapping of marker genes defined in single-cell RNA-seq datasets in complex plant tissue.

plant biology↗

bioRxiv2022

CRISPRi-based circuits for genetic computation in plants

Synthetic gene circuits can enable new cellular behaviours by integrating multiple input signals into customisable genetic programs. However, gene circuit development in plants has been limited by a lack of orthogonal and modular parts required for their construction. Here, we present a tool-kit of reversible CRISPRi-based gene circuits for use in plants. First, we created a range of engineered repressible promoters of different strengths and used them as integrators for the construction of NOT and NOR gates in Arabidopsis cells. Next, we determined the optimal processing system to express sgRNAs from RNA Pol II promoters to introduce NOR gate programmability and interface it with host regulatory sequences. Finally, we connected multiple NOR gates together in layered arrangements to create OR, NIMPLY, and AND logic functions. Our CRISPRi circuits are orthogonal, compact, reversible, programmable, and modular, providing a new platform for sophisticated and deliberate spatio-temporal control of gene expression in plants.

synthetic biology↗

bioRxiv2022

An environmentally-responsive transcriptional state modulates cell identities during root development

Roots are fundamental organs for plant development and response to their environment: they anchor the plant to its growth substrate, uptake nutrients and water vital to plant growth, and can sense and respond to a variety of biotic and abiotic stresses. The architecture of root systems and their growth are known to be strongly affected by the environmental conditions found in the soil. However, the acquisition of cell identities at the root meristem is still mainly viewed as ontogenetically driven, where a small number of stem cells generate all the cell types through stereotyped divisions followed by differentiation, along a simple developmental trajectory. The extent to which environmental cues precisely shape and affect these developmental trajectories remains an open question. We used single-cell RNA-seq, combined with spatial mapping, to deeply explore the trajectories of cell states at the tip of Arabidopsis roots, known to contain multiple developing lineages. Surprisingly, we found that most lineage trajectories exhibit a stereotyped bifid topology with two developmental trajectories rather than one. The formation of one of the trajectories is driven by a strong and specific activation of genes involved in the responses to various environmental stimuli, that affects only of a subset of the cells in multiple cell types simultaneously, revealing another layer of patterning of cell identities in the root that is independent of cell ontogeny. We demonstrate the robustness of this environmentally responsive transcriptional state by showing that it is present in a mutant where cell type identities are greatly perturbed, as well as in different Arabidopsis ecotypes. We also show that the root can adapt the proportion of cells that acquire this particular state in response to environmental signals such as nutrient availability. The discovery of this cell state reveals new layers of cell identity that may underpin the adaptive potential of plant development.

plant biology↗

bioRxiv2021

MONI: A Pangenomics Index for Finding MEMs

Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding -- but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large sequence collections of highly repetitive sequences. Compared to other read aligners - PuffAligner, Bowtie2, BWA-MEM, and CHIC - MONI used 2-11 times less memory and was 2-32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references. AvailabilityMONI is publicly available at https://github.com/maxrossi91/moni.

bioinformatics↗

bioRxiv2021

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. While these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare ten deconvolution tools and evaluate their performance while using each of eleven separate reference profiles. Specifically, we have run deconvolution tools on over 4,000 samples with known cell type proportions, spanning both immune and stromal cell types. Twelve of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single cell data. A final 3,728 clinical samples have been collected from the Framingham Cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool EPIC produces the highest correlation while GEDIT produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. In terms of reference choice, we find that the Human Primary Cell Atlas (HPCA) and references published by the EPIC authors produce accurate results for the largest number of tools and datasets. When applying deconvolution to blood samples, the leukocyte reference matrix LM22 is also a suitable choice, usually (but not always) outperforming HPCA and EPIC. Running time varies substantially across tools. For as many as 5052 samples, SaVanT and dtangle reliably finish in under one minute, while slower tools may require up to two hours. However, when using custom references, CIBERSORT can run very slowly, taking over 24 hours to complete for large datasets. We conclude that combining the best tools with optimal reference datasets can provide significant gains in accuracy when carrying out deconvolution tasks.

bioinformatics↗

bioRxiv2020

Two-target quantitative PCR to predict library composition for shallow shotgun sequencing

Shotgun sequencing enables retrieving high resolution information 40 from complex microbial communities. However, the technique is limited by missing information about host-to-microbe ratios observed in different sample types. This makes it challenging to plan sequencing experiments, especially in the context of high sample multiplexing and/or limited sequencing output. We evaluated a qPCR-based assay to predict host-to microbe ratio prior to sequencing. Using a two-target assay aimed at conserved human and bacterial genes, we predicted human-to-microbe ratios in two sample types and validated it on independently collected samples. The assay enabled accurate prediction for a broad range of sample compositions.

microbiology↗

bioRxiv2019

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗