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bioRxiv2022

scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching

Single-cell RNA-sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is however challenging due to technical and biological noise, and as the cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational framework that utilizes topological priors to decouple, enhance, and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We demonstrate scPrismas use across diverse biological systems and tasks, including analysis and manipulation of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas, and circadian rhythm in the suprachiasmatic nucleus in the brain. We further show how scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type, and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrismas flexibility in utilizing diverse prior knowledge, and inference of topologically-informative genes. scPrisma can be used both as a stand-alone workflow for signal analysis, and, as it does not embed the data to lower dimensions, as a prior step for downstream single-cell analysis.

bioinformatics↗

bioRxiv2021

Synthetic cell-based materials extract positional information from morphogen gradients

Dynamic biomaterials composed of synthetic cellular structures have the potential to adapt and functionally differentiate guided by physical and chemical cues from their environment. Inspired by developing biological systems, which efficiently extract positional information from chemical morphogen gradients in the presence of environmental uncertainties, we here investigate the analogous question: how well can a synthetic cell determine its position within a synthetic multicellular structure? In order to calculate positional information in such systems, we created and analyzed a large number of replicas of synthetic cellular assemblies, which were composed of emulsion droplets connected via lipid bilayer membranes. The droplets contained cell-free two-node feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. We found that in our system, simple anterior-posterior differentiation is possible, but positional information is limited by gene expression noise, and is also critically affected by the temporal evolution of the morphogen gradient and the life-time of the cell-free expression system contained in the synthetic cells. Using a 3D printing approach, we demonstrate morphogen-based differentiation also in larger tissue-like assemblies.

synthetic biology↗

bioRxiv2022

SAIBR: A simple, platform-independent method for spectral autofluorescence correction

Biological systems are increasingly viewed through the lens of mathematics, physics, and systems approaches that demand accurate quantification of gene expression and local protein concentrations. Such approaches have benefited greatly from the revolution in genetic engineering sparked by CRISPR/Cas9. By facilitating the tagging of genes at their genomic loci, CRISPR/Cas9 allows us to use fluorescence to monitor proteins that are expressed at or near endogenous levels under native regulatory control. However, due to their typically lower expression levels, quantitative experiments using endogenously-tagged genes can run into limits imposed by autofluorescence (AF). AF is often a particular challenge in the illumination bands occupied by the most efficient fluorescent proteins (GFP, mNeonGreen). Stimulated by our work in C. elegans, we describe and validate Spectral Autofluorescence Image correction By Regression (SAIBR), a simple, platform-independent protocol, and associated GUI-based FIJI plugin to correct for autofluorescence using standard filter sets and illumination conditions. Fully validated for use in C. elegans embryos and tested in diverse systems, including starfish oocytes and fission yeast, SAIBR achieves accurate quantitation of fluorophore signal and enables reliable detection and quantification of even weakly expressed proteins. Thus, SAIBR provides a highly accessible, low barrier way to incorporate AF correction as standard for researchers working on a broad variety of cell and developmental systems. Summary StatementImplemented as an easy-to-use Fiji Plugin, SAIBR provides effective autofluorescence correction for cells and tissues using standard imaging conditions.

cell biology↗

bioRxiv2018

A data-driven approach to characterising intron signal in RNA-seq data

RNA-seq datasets can contain millions of intron reads per sequenced library that are typically removed from downstream analysis. Only reads overlapping annotated exons are considered to be informative since mature mRNA is assumed to be the major component sequenced, especially when examining poly(A) RNA samples. In this paper, we demonstrate that intron reads are informative and that pre-mRNA is the major source of intron signal. Making use of pre-mRNA signal, our index method combines differential expression analyses from intron and exon counts to categorise changes observed in each count set, giving additional genes with evidence of transcriptional changes when compared to a classic approach. Considering the importance of intron retention in some biological systems, another novel method, superintronic, looks for evidence of intron retention after accounting for the presence of pre-mRNA signal. The results presented here overcomes deficiencies and biases in previous works related to intron reads by exploring multiple sources for intron reads simultaneously using a data-driven approach, and provides a broad overview into how intron reads can be utilised in relation to multiple aspects of transcriptional biology.

bioinformatics↗

bioRxiv2026

A First-In-Class Broad Spectrum Inhibitor of Copper Exporting P1B-type ATPases

Copper (Cu) transporting ATPases represent a highly conserved subclass of P-type ATPases with critical roles in Cu export and metalloenzyme synthesis. Despite their important biological roles and association with a wide range of human diseases, no high-affinity small-molecule inhibitors have been described. Here, we identify MKV3 as a first-in-class inhibitor of Cu-transporting P-type ATPases that targets a conserved Cu+ entry site to the translocation pathway. In silico docking against the Xenopus ATP7B structure revealed a highly conserved pocket suitable for pharmacological inhibition. MKV3 bound human ATP7A and ATP7B with nanomolar affinity, competed with N-terminal metal-binding domains for access to the Cu+ entry site, and selectively inhibited Escherichia coli CopA ATPase activity and Cu+ transport. Mechanistically, MKV3 blocked chaperone-mediated Cu+ delivery to the intramembranous CPC site of CopA that is essential for its transport function. We further identified a single charged P-domain residue that governed MKV3 affinity and potency across species. Functionally, MKV3 phenocopied the genetic loss of Cu+-ATPases in bacteria, fungi, plants, zebrafish, and mammals, impairing copper-dependent enzymes, transporter trafficking, and copper tolerance. These findings establish a conserved, druggable vulnerability in Cu+-ATPases and introduce MKV3 as a broadly active chemical tool to modulate copper homeostasis across biological kingdoms. Significance StatementCopper-transporting P1B-type ATPases are essential for copper homeostasis in all domains of life, yet have lacked pharmacological inhibitors. This work identifies MKV3 as the first small-molecule inhibitor of Cu+-ATPases in bacteria, fungi, plants and animals, and defines a conserved, druggable Cu+ entry pocket that governs metal delivery to the transmembrane pathway. MKV3s ability to potentiate copper-mediated killing in multidrug-resistant bacterial pathogens highlights its potential as an antimicrobial adjuvant, while its attenuation of mammalian ATP7A/B function offers promise in oncology and copper-related diseases. Collectively, these findings establish a new tool for targeting of Cu+-ATPases with wide-ranging applications across biological systems.

cell biology↗

bioRxiv2026

Quantitative measurement of synthetic repression curves reveals design challenges for genetic circuit engineering under growth arrest

Despite the fact that microbes in natural environments spend most of their time in growth arrest, we understand little about how this physiological state affects the performance of engineered genetic circuits. Here, we measure repression curves from a library of genetic NOT gates at single-cell resolution in Escherichia coli under both active growth and growth arrest to systematically investigate how growth arrest affects circuit behavior. We find that the impact of growth arrest on circuit performance is almost entirely dominated by a single effect: a >100-fold reduction in unrepressed expression levels. Growth arrest caused gene expression noise to increase moderately and had only minimal impacts on the sensitivity and sharpness of the repression curves. Our work shows both that conventional genetic circuit design paradigms are currently insufficient to develop circuits that can function properly under growth arrest, but also that addressing the reduction in just a single performance parameter would be sufficient to resolve this problem. This work expands our understanding of bacterial gene regulation under growth arrest and lays the groundwork for new design paradigms that will be essential in ensuring the safe and reliable performance of synthetic biology systems in real-world environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/703179v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@104ecd2org.highwire.dtl.DTLVardef@57f992org.highwire.dtl.DTLVardef@1c215c2org.highwire.dtl.DTLVardef@122a8c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Super-enhancer-driven CACNA2D2 is an EWSR1::WT1 signature gene encoding a diagnostic marker for desmoplastic small round cell tumor (DSRCT)

Desmoplastic small round cell tumor (DSRCT) is a highly aggressive cancer predominantly occurring in male adolescents and young adults. The lack of a comprehensive understanding on the biology of the disease is paralleled by its dismal survival rates (5-20%). To overcome this challenge, we first identified and prioritized urgently needed resources for clinicians and researchers. Thus, we established genome-wide single-cell RNA-sequencing and bulk proteomic data of in vitro and in vivo-generated knockdown models of the pathognomonic DSRCT fusion oncoprotein (EWSR1::WT1) and combined them with an original systems-biology-based pipeline including patient data and the largest histology collection of DSRCTs and morphological mimics available to date. These novel tools were enriched with curated public datasets including patient- and cell line-derived ChIP-seq, bulk and single-cell RNA-seq studies resulting in a multi-model and multi-omic toolbox for discovery analyses. As a proof of concept, our approach revealed the alpha-2/delta subunit of the voltage-dependent calcium channel complex, CACNA2D2, as a highly overexpressed, super-enhancer driven, direct target of EWSR1::WT1. Single-cell and bulk-level analyses of patient samples and xenografted cell lines highlighted CACNA2D2 as a critical component of our newly established EWSR1::WT1 oncogenic signature, that can be employed to robustly identify DSRCT in reference sets. Finally, we show that CACNA2D2 is a highly sensitive and specific single biomarker for fast, simple, and cost-efficient diagnosis of DSRCT. Collectively, we establish a large-scale multi-omics dataset for this devastating disease and provide a blueprint of how such toolbox can be used to identify new and clinically relevant diagnostic markers, which may significantly reduce misdiagnoses, and thus improve patient care.

cancer biology↗

bioRxiv2018

Chance is an important element in phagolysosomal acidification that favors the macrophage

Microbial ingestion by a macrophage results in the formation of an acidic phagolysosome but the host cell has no information on the pH susceptibility of the ingested organism. This poses a problem for the macrophage and raises the fundamental question of how the phagocytic cell optimizes the acidification process to prevail. We analyzed the dynamical distribution of phagolysosomal pH in murine and human macrophages that had ingested live or dead Cryptococcus neoformans cells, or inert beads. Phagolysosomal acidification produced a range of pH values that approximated normal distributions, but these differed from normality depending on ingested particle type. Analysis of the increments of pH reduction revealed no forbidden ordinal patterns, implying that phagosomal acidification process was a stochastic dynamical system. Using simulation modeling, we determined that by stochastically acidifying a phagolysosome to a pH within the observed distribution, macrophages sacrificed a small amount of overall fitness to gain the benefit of reduced variation in fitness. Hence, chance in the final phagosomal pH introduces unpredictability to the outcome of the macrophage-microbe, which implies a bet-hedging strategy that benefits the macrophage. While bet hedging is common in biological systems at the organism level, our results show its use at the organelle and cellular level.

microbiology↗

bioRxiv2020

Enhancing multi-bioreactor platforms for automated measurements and reactive experiment control

New small-scale, low-cost bioreactors provide researchers with exquisite control of environmental parameters of microbial cultures over long durations, allowing them to perform sophisticated, high-quality quantitative experiments that are particularly useful in systems biology, synthetic biology and bioengineering. However, existing setups are limited in their automated measurement capabilities, primarily because sensitive and specific measurements require bulky, expensive, stand-alone instruments. Here, we present ReacSight, a generic and flexible strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. On the hardware side, ReacSight leverages a pipetting robot for sample collection, handling and loading. On the software side, ReacSight provides a versatile instrument control architecture and a generic event system for reactive experiment control. ReacSight is ideally suited to integrate open-source, open-hardware components but can also accommodate closed-source, GUI-only components (e.g. cytometers). We use ReacSight to assemble a platform for cytometry-based characterization and reactive optogenetic control of parallel yeast continuous cultures. Using a dedicated bioreactor array, we showcase its capabilities on three applications. First, we achieve parallel real-time control of gene expression with light in different bioreactors. Second, we explore the impact of nutrient scarcity on fitness and cellular stress using well-controlled, high-information content competition assays. Third, we exploit nutrient scarcity to achieve dynamic control over the composition of a two-strain consortium. To illustrate the genericity of ReacSight, we also assemble an equivalent platform using the optogenetic-ready, open-hardware and commercially available Chi.Bio bioreactors.

synthetic biology↗

bioRxiv2022

Drug-induced adaptation along a resistance continuum in cancer cells

Advancements in rational drug design over the past decades have consistently produced new cancer therapies, but such treatments are inevitably countered through an adaptive process that fosters therapy resistance. Malignant cells achieve drug resistance through intrinsic and acquired mechanisms, rooted in genetic and non-genetic determinants. In particular, recent work has highlighted the role of intrinsic cellular heterogeneity in the emergence of transient drug-tolerant persister cells that survive drug treatment, as well as non-genetically driven cell plasticity toward stable resistance. However, these models do not account for the role of dose and treatment duration as extrinsic forces in eliciting cancer cell adaptation. Here, we show that these two components together drive the resistance of ovarian cancer cells to targeted therapy along a trajectory of cellular adaptation, that we denote the resistance continuum. We report that gradual dose exposure and prolonged treatment promote a continuous increase in fitness, and show that this process is mediated by evolving transcriptional, epigenetic and genetic changes that promote multiple cell state transitions. The resistance continuum is underpinned by the assembly of gene expression programs and epigenetically reinforced stress response regulation. Using both in vivo and in vitro models, we found that this process involves widespread reprogramming of cell survival pathways, including interferon response, lineage reprogramming, metabolic rewiring and oxidative stress regulation. Together, the resistance continuum reveals the dynamic nature of cellular adaptation, and carries implications for cancer therapies, as initial exposure to lower doses primes cells over time for increased resistance to higher doses. Beyond cancer, such continuous adaptation exposes a basic aspect of cellular plasticity, which may also be deployed in other biological systems such as development, immune response and host-pathogen interactions.

cancer biology↗

bioRxiv2020

scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

Constructing and comparing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNAseq) data has the potential to reveal critical components in the underlying regulatory networks regulating different cellular transcriptional activities. Here, we present a robust and powerful machine learning workflow--scTenifoldNet--for comparative GRN analysis of single cells. The scTenifoldNet workflow, consisting of principal component regression, low-rank tensor approximation, and manifold alignment, constructs and compares transcriptome-wide single-cell GRNs (scGRNs) from different samples to identify gene expression signatures shifting with cellular activity changes such as those associated with pathophysiological processes and responses to environmental perturbations. We used simulated data to benchmark scTenifoldNets performance, and then applied scTenifoldNet to several real data sets. In real-data applications, scTenifoldNet identified highly specific changes in gene regulation in response to acute morphine treatment, an antibody anticancer drug, gene knockout, double-stranded RNA stimulus, and amyloid-beta plaques in various types of mouse and human cells. We anticipate that scTenifoldNet can help achieve breakthroughs through constructing and comparing scGRNs in poorly characterized biological systems, by deciphering the full cellular and molecular complexity of the data. HighlightsO_LIscTenifoldNet is a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignment C_LIO_LIscTenifoldNet uses single-cell RNA sequencing (scRNAseq) data to construct single-cell gene regulatory networks (scGRNs) C_LIO_LIscTenifoldNet compares scGRNs of different samples to identify differentially regulated genes C_LIO_LIReal-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested. C_LI Short abstractWe present scTenifoldNet--a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignment--for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing (scRNAseq). scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.

bioinformatics↗

bioRxiv2021

Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological system. Adaptive Laboratory Evolution (ALE) leverages natures problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/452699v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@8e1759org.highwire.dtl.DTLVardef@9ee7d5org.highwire.dtl.DTLVardef@866f04org.highwire.dtl.DTLVardef@1e27b29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2017

Pathogen Population Structure Can Explain Hospital Outbreaks

ObjectiveTo analyze Hospital Acquired Infection (HAI) outbreaks using microbial population biology dynamics in order to understand outbreaks as a biological system.\n\nDesignComputational modeling study.\n\nMethodsThe majority of HAI transmission models describe dynamics on the level of the host rather than on the level of the pathogens themselves. Accordingly, epidemiologists often cannot complete transmission chains without direct evidence of either host-host contact or large reservoir populations. Here, we propose an ecology-based model to explain the transmission of pathogens in hospitals. The model is based upon metapopulation biology, which describes a group of interacting localized populations and island biogeography, which provides a basis for how pathogens may be moving between locales. Computational simulation trials are used to assess the applicability of the model.\n\nResultsResults indicate that pathogens survive for extended periods without the need for large reservoirs by living in localized ephemeral populations while continuously transmitting pathogens to new seed populations. Computational simulations show small populations spending significant portions of time at sizes too small to be detected by most surveillance protocols. The number and type of these ephemeral populations enable the overall pathogen population to be sustained.\n\nConclusionsBy modeling hospital pathogens as a metapopulation, observations characteristic of hospital acquired infection outbreaks for which there has previously been no sufficient biological explanation, including how and why empirically successful interventions work, can now be accounted for using population dynamic hypotheses. Epidemiological links between temporally isolated outbreaks are explained via pathogen population dynamics and potential outbreak intervention targets are identified.

epidemiology↗

bioRxiv2021

Sialoglycan microarray encoding reveals differential sialoglycan binding of phylogenetically-related bacterial AB5 toxin B subunits

Vertebrate sialic acids (Sias) display much diversity in modifications, linkages and underlying glycans. Slide microarrays allow high-throughput explorations of sialoglycan-protein interactions. A microarray presenting [~]150 structurally-defined sialyltrisaccharides with various Sias linkages and modifications still poses challenges in planning, data sorting, visualization and analysis. To address these issues, we devised a simple 9-digit code for sialyltrisaccharides with terminal Sias and underlying two monosaccharides assigned from the non-reducing end, with three digits assigning a monosaccharide, its modifications, and linkage. Calculations based on the encoding system reveals >113,000 likely linear sialyltrisaccharides in nature. Notably a biantennary N-glycan with two terminal sialyltrisaccharides could thus have >1010 potential combinations and a triantennary N-glycan with three terminal sequences, >1015 potential combinations. While all possibilities likely do not exist in nature, sialoglycans encode enormous diversity. While glycomic approaches are used to probe such diverse sialomes, naturally-occurring bacterial AB5 toxin B subunits are simpler tools to track the dynamic sialome in biological systems. Sialoglycan microarray was utilized to compare sialoglycan-recognizing bacterial toxin B subunits. Unlike the poor correlation between B subunits and species phylogeny, there is stronger correlation with Sia-epitope preferences. Further supporting this pattern, we report a B subunit (YenB) from Yersinia enterocolitica (broad host range) recognizing almost all sialoglycans in the microarray, including 4-O-acetylated-Sias not recognized by a Y. pestis orthologue (YpeB). Differential Sia-binding patterns were also observed with phylogenetically-related B subunits from Escherichia coli (SubB), Salmonella Typhi (PltB), S. Typhimurium (ArtB), extra-intestinal E.coli (EcPltB), Vibrio cholera (CtxB), and cholera family homologue of E. coli (EcxB).

molecular biology↗

bioRxiv2021

Museum of Spatial Transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

bioinformatics↗

bioRxiv2022

Proteomics profiling reveals regulation of immune response to Salmonella Typhimurium infection in mice

Regulation of the immune response to Salmonella typhimurium (S. Typhimurium) infection is a complex process, influenced by genetic and environmental factors. Different inbred mouse strains show distinct levels of resistance to S. Typhimurium infection, ranging from susceptible (e.g., C57BL/6J) to resistant (e.g., DBA/2J) strains. However, the underlying molecular mechanisms contributing to the host response remain elusive. In this study, we present a comprehensive proteomics profiling of the spleen tissue from C57BL/6J and DBA/2J strains with different doses of S. Typhimurium infection by tandem tag mass coupled with two-dimensional liquid chromatography-tandem mass spectrometry. We identified and quantified 3,986 proteins, resulting in 475 differentially expressed proteins between C57BL/6J and DBA/2J strains. Functional enrichment analysis revealed that the mechanisms of innate immune responses to S. Typhimurium infection are associated with several signaling pathways, including the interferon signaling pathway. Our proteomic data also discovered a plausible gene in a genomic region that control different levels of resistance to S. Typhimurium infection. We further revealed the roles of macrophage cells and pro-inflammatory cytokines in the mechanisms under the natural resistance to S. Typhimurium. In summary, our results provide new insights into the genetic regulation of the immune response to S. Typhimurium infection in mice. Author SummarySalmonella infection (salmonellosis) is a common zoonotic disease that mainly propagates through contaminated food and drink. Various mouse strains display prominent disparities in responses to Salmonella invasion. Elucidating the heterogeneous immune reactions between different mouse strains can shed light on the fundamental molecular mechanisms of the innate immune system. Here, we employed a combination of proteomics and systems biology approaches to provide an unprecedented panorama of the inextricably interlaced immune signaling pathways in response to Salmonella infection in mice. Our results revealed the dynamics of cell signaling molecules elicited by inflammation and established new connections among them. We also identified a new candidate gene involved in the combat with pathogens. Our proteomic data and results contribute to understanding the intricate interactions of immune responses to Salmonella infection from molecular to systemic levels.

immunology↗

bioRxiv2019

ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output

MotivationTime courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.\n\nResultsWe have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude mass-spring oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms.\n\nAvailabilityECHOs full interface is available at https://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded at https://CRAN.R-project.org/package=echo.find.\n\nContacthurleyj2@rpi.edu\n\nSupplementary informationSupplementary data are available

bioinformatics↗

bioRxiv2020

Multiple latent clusterisation model for the inference of RNA life-cycle kinetic rates from sequencing data

We here propose a hierarchical Bayesian model to infer RNA synthesis, processing, and degradation rates from sequencing data, based on an ordinary differential equation system that models the RNA life cycle. We parametrize the latent kinetic rates, that rule the system, with a novel functional form, and estimate their parameters through 6 Dirichlet process mixture models. Owing to the complexity of this approach, we are able to simultaneously perform inference, clustering and model selection. We apply our method to investigate transcriptional and post-transcriptional responses of murine fibroblasts to the activation of proto-oncogene Myc. Our approach uncovers simultaneous regulations of the rates, which had not previously been observed in this biological system.

bioinformatics↗