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Results for “systems biology”

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bioRxiv2021

Evolution of Phenotypic Plasticity under Host-Parasite Interactions

Robustness and plasticity are essential features that allow biological systems to cope with complex and variable environments. Through the evolution of a given environment, the former, the insensitivity of phenotypes, is expected to increase, whereas the latter, the changeability of phenotypes, tends to diminish. However, in nature, plasticity is preserved to a certain degree. One possible cause for this is environmental variation, with one of the most important "environmental " factors being inter-species interactions. As a first step toward investigating phenotypic plasticity in response to an ecological interaction, we present the study of a simple two-species system consisting of hosts and parasites. Hosts are expected to evolve to achieve a phenotype that optimizes fitness and increases the robustness of the corresponding phenotype by reducing phenotypic fluctuations. Conversely, plasticity evolves in order to avoid certain phenotypes being attacked by parasites. By simulating evolution using the host gene-expression dynamics model, we analyze the evolution of genotype-phenotype mapping. If the interaction is weak, the fittest phenotype of the host evolves to reduce phenotypic variances. In contrast, if a sufficient degree of interaction occurs, the phenotypic variances of hosts increase to escape parasite attacks. For the latter case, we found two strategies: if the noise in the stochastic gene expression is below a certain threshold, the phenotypic variance increases via genetic diversification, whereas above the threshold, it is increased due to noise-induced phenotypic plasticity. We examine how the increase in the phenotypic variances due to parasite interactions influences the growth rate of a single host, and observed a trade-off between the two. Our results help elucidate the roles played by noise and genetic mutations in the evolution of phenotypic plasticity and robustness in response to host-parasite interactions. Author summaryPlasticity and phenotypic variability induced by internal or external perturbations are common features of biological systems. However, certain environmental conditions initiate evolution to increase fitness and, in such cases, phenotypic variability is not advantageous, as has been demonstrated by previous laboratory and computer experiments. As a possible origin for such plasticity, we investigated the role of host-parasite interactions, such as those between bacteria and phages. Different parasite types attack hosts of certain phenotypes. Through numerical simulations of the evolution of host genotype-phenotype mapping, we found that, if the interaction is sufficiently strong, hosts increase phenotypic plasticity by increasing phenotypic fluctuations. Depending on the degree of noise in gene expression dynamics, there are two distinct strategies for increasing the phenotypic variances: via stochasticity in gene expression or via genetic variances. The former strategy, which can work over a faster time scale, leads to a decline in fitness, whereas the latter reduces the robustness of the fitted state. Our results provide insights into how phenotypic variances are preserved and how hosts can escape being attacked by parasites whose genes mutate to adapt to changes in parasites. These two host strategies, which depend on internal and external conditions, can be verified experimentally, for example, via the transcriptome analysis of microorganisms.

evolutionary biology↗

bioRxiv2022

FunOrder 2.0 - a fully automated method for the identification of co-evolved genes

Coevolution is an important biological process that shapes interacting species or even proteins - may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems. AUTHOR SUMMARYCoevolution is a process which arises between different species or even different proteins that interact with each other. Any change occurring in one partner must be met by a corresponding change in the other partner to maintain the interaction throughout evolution. These interactions may occur in symbiotic relationships or between rivaling species. Within an organism, consecutive enzymes of metabolic pathways are also subjected to coevolution. We developed a fully automated method, FunOrder 2.0, for the detection of co-evolved proteins, which will contribute to a better understanding of protein interactions within an organism. We demonstrate that this method can be used to identify essential genes of the secondary metabolism of fungi, but FunOrder 2.0 may also be used to detect pathogenicity factors or remains of horizontal gene transfer next to many other biological systems that were shaped by coevolution.

bioinformatics↗

bioRxiv2023

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures

Neurofunctional dysregulations in spatially discrete areas or isolated pathways have been suggested as neural markers for attention deficit hyperactivity disorder (ADHD). However, multiscale perspectives into the neurobiological underpins of ADHD spanning multiple biological systems remain sparse. This points to the need of multi-levels of analysis encompassing brain functional organization and its correlation with molecular and cell-specific transcriptional signatures are stressed. Here, we capitalized on diffusion mapping embedding model to derive the functional connectome gradient, and deployed multivariate partial least square (PLS) method to uncover the enrichment of neurotransmitomic, cellular and chromosomal connectome-transcriptional signatures of ADHD. Compared to typical control, ADHD children presented connectopic cortical perturbations in lateral orbito-frontal and superior temporal regions, which had also been validated in another independent sample. This gradient-derived variants in ADHD further aligned spatially with distributions of GABAA/BZ and 5-HT2A receptors and co-varied with genetic transcriptional expression. Cognitive decoding and gene-expression annotation showed the correlates of these variants in memory, emotional regulation and spatial attention. Moreover, the gradient-derived transcriptional signatures of ADHD exhibited enriched expression of oligodendrocyte precursors and endothelial cells, and were mainly involved as variants of chromosome 18, 19 and X. In conclusion, our findings bridged in-vivo neuroimging assessed functional brain organization patterns to a multi-level molecular pathway in ADHD, possibly shedding light on the interrelation of biological systems that may coalesce to the emergence of this disorder.

neuroscience↗

bioRxiv2025

Factor Analysis of Multimodal MRI, Biofluid and Vascular Health Biomarkers Reveals Latent Constructs of Brain Health

Individual imaging and fluid biomarkers provide insights into specific components of brain health, but integrated multimodal approaches are necessary to capture the complex, interrelated biological systems that contribute to brain homeostasis and neurodegenerative disease. Using data from the Brain and Cognitive Health (BACH) cohort study (N=127; mean age=67 years, 68% women), we performed an exploratory factor analysis to identify latent constructs of brain health. We included multimodal neurovascular imaging markers, brain atrophy metrics, plasma Alzheimers disease (AD) biomarkers and cardiovascular risk factors. Five constructs emerged: "Brain & Vascular Health" (greater hippocampal volume, basal ganglia enlarged perivascular spaces [ePVS], cerebral blood flow and HDL cholesterol; lower ventricle volume and BMI); "Structural Integrity" (greater cortical thickness, fractional anisotropy and basal ganglia ePVS); "Fluid Transport" (greater white matter ePVS and Free Water); "AD Biomarkers" (higher phosphorylated tau [pTau]181 and pTau217; lower amyloid-beta 42/40 ratio); and "Neuronal Injury" (higher glial fibrillary acidic protein and neurofilament light chain). All constructs were associated with age ({beta}=-0.70-0.39, p[≤].014), except for Fluid Transport (p>.05). Brain & Vascular Health and Structural Integrity (partial r=.305, p<.001), and AD Biomarkers and Neuronal Injury (partial r=.248, p=.005) were positively correlated. Only Brain & Vascular Health was associated with global cognition ({beta}=0.27, SE=0.13, p=.043). These findings provide a data-driven framework for examining distinct constructs underlying vascular health, fluid regulation and neurodegenerative pathology. We demonstrate the utility of using multiple biomarkers to probe these biological systems, paving the way for future research to explore how these systems change across diverse neurodegenerative conditions.

neuroscience↗

bioRxiv2023

Multifaceted proteome analysis at solubility, redox, and expression dimensions for target identification

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, we devise the first high- throughput redox proteomics approach integrated with expression analysis (REX) and combine it with Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, we analyzed HCT116 cells treated with auranofin, showing great improvement compared with previous such studies. Then we applied PISA-REX to study proteome remodeling upon stimulation of human monocytes by interferon . We also studied the proteome changes in plasmacytoid dendritic cells isolated from wild type vs. Ncf1- mutant mice treated with interferon , showing that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

biochemistry↗

bioRxiv2025

IsoformMapper: A Web Application for Protein-Level Comparison of Splice Variants through Structural Community Analysis

Alternative splicing (AS) plays a key role in numerous cellular processes by enabling the existence of different protein isoforms with potentially different functions and fine-tuning isoform abundance. However, despite its biological importance, tools to compare the domain composition of AS-derived protein isoforms have lagged as they require both structural data and an appropriate methodology for isoform comparison. Recent advances in AI-driven protein structure prediction, particularly AlphaFold2, have made accurate structural determination of splicing isoforms easily accessible, paving the way for new tools to compare their structures and asses the functional consequences of AS at the protein structure level. Here, we present IsoformMapper, a web resource that enables researchers to study alternative splicing at the protein structural level using protein network community analysis, which captures 3D physical interactions between protein regions often missed by traditional domain analysis, allowing comparisons of isoforms across any biological system. As a proof of concept, we used our tool to a case study, in which we studied how GENOMES UNCOUPLED1 (GUN1) dependent retrograde signaling regulates plant de-etiolation through alternative splicing in Arabidopsis. In response to light, we demonstrate that GUN1 regulates the expression of specific spliceosome components, consequently modifying alternative splicing of key genes encoding components essential for establishing of photosynthesis. Additionally, the gun1 mutant showed altered splice variant ratios for PNSL2, CHAOS, and SIG5. Our tool revealed that these isoforms have significantly different protein community structures, illustrating the impact of AS on protein function and demonstrating the practical value of the tool. (247 words) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/641708v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c006dcorg.highwire.dtl.DTLVardef@1025b07org.highwire.dtl.DTLVardef@1a64bf6org.highwire.dtl.DTLVardef@c512af_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract.C_FLOATNO IsoformMapper enables structural comparison of protein isoforms through a multi-step analysis pipeline. Starting with AlphaFold2 protein structure predictions of splice variants, the method generates weighted network representations for each isoform. The web application processes multiple AlphaFold predictions per isoform, with the conformational variability between predictions represented as weighted edges in the network representation. These networks are then analyzed using the InfoMap community detection algorithm to identify structural communities, ultimately allowing visualization and comparison of protein isoform structures through interactive network displays and alluvial diagrams. The method is applicable to protein isoforms from any biological system, providing comprehensive analysis of alternative splicing consequences at the protein level by examining structural domains as integrated units. This holistic approach enables users to evaluate potential functional differences between isoforms based on their structural organization. C_FIG

bioinformatics↗

bioRxiv2025

Molecular Mechanisms of Chinese Medicine in Regulating Colorectal Cancer Immune Microenvironment: Insights from Single-Cell Transcriptomics and Network Pharmacology

BackgroundThe molecular mechanisms underlying the efficacy of Traditional Chinese Medicine (TCM) in colorectal cancer treatment remain largely unexplored. We developed a computational systems biology approach integrating single-cell transcriptomics with network pharmacology to elucidate the potential mechanisms of TCM in modulating colorectal cancer progression. MethodsWe developed an integrated computational pipeline for multi-omics data analysis combining single-cell transcriptomics with network pharmacology. Raw single-cell RNA-seq data from 3 normal tissues and 3 colorectal tumors were obtained from GEO database and processed using a customized workflow in R. Quality control, normalization, and dimensionality reduction were performed using the Seurat v4.0 algorithm, followed by unsupervised clustering to identify cell subpopulations. Differentially expressed genes (DEGs) were identified using MAST algorithm with adjusted p-value < 0.05 and |log2FC| > 1.0. These computationally identified DEGs were subsequently mapped to a comprehensive Traditional Chinese Medicine (TCM) database using a network pharmacology approach to predict herb-target interactions. In parallel, we integrated TCGA RNA-seq data (STAR-counts) with clinical information, applying log2(TPM+1) transformation for normalization. We then implemented a machine learning-based correlation analysis to construct gene-cell-immunity-pathway networks, using weighted gene co-expression network analysis (WGCNA) to identify key regulatory modules. ResultsOur computational analysis of single-cell RNA-seq data identified 109 differentially expressed genes (DEGs) that define the molecular signature of colorectal cancer microenvironment. Clustering algorithms revealed 14 distinct cell subpopulations, with predominant immune cell infiltration, particularly B and T lymphocytes, suggesting a complex immune regulatory network. Network pharmacology analysis mapped these DEGs to potential therapeutic targets, computationally predicting interactions with 140 traditional Chinese herbs. These herbs were classified into 8 functional categories. Through integrative multi-omics analysis and pathway enrichment algorithms, we identified core regulatory networks comprising 23 genes and 39 significantly enriched signaling pathways (FDR < 0.01) that orchestrate immune cell function in the tumor microenvironment. Notably, our analysis in silico revealed previously uncharacterized gene-pathway interactions that may explain the immunomodulatory effects of specific herbal compounds. ConclusionsOur systems biology and computational analysis revealed a potential mechanism by which 8 categories of Chinese herbal medicines and 23 genes across 39 signaling pathways may regulate colorectal cancer progression through modulation of specific gene regulatory networks and immune cell functions. These findings demonstrate the value of integrative computational approaches in elucidating complex biological mechanisms of traditional medicines

immunology↗

bioRxiv2019

Imprinting effects of UBE3A loss on synaptic gene networks and Wnt signaling pathways

The genomically imprinted UBE3A gene encodes a E3 ubiquitin ligase whose loss from the maternal allele leads to the neurodevelopmental disorder Angelman syndrome. However, the mechanisms by which loss of maternal UBE3A contribute to severe neurodevelopmental phenotypes are poorly understood. Previous studies of UBE3A function have focused on mouse models or single targets, but these approaches do not accurately reflect the complexity of imprinted gene networks in the brain nor the systems-level cognitive dysfunctions in Angelman syndrome. We therefore utilized a systems biology approach to better elucidate how UBE3A loss impacts the early postnatal brain in a novel CRISPR/Cas9 engineered rat Angelman model of a complete Ube3a deletion. Strand-specific transcriptome analysis of offspring derived from maternally or paternally inherited Ube3a deletions revealed the expected parental expression patterns of Ube3a sense and antisense transcripts by postnatal day 2 (P2) in hypothalamus and day 9 (P9) in cortex, when compared to wild-type sex-matched littermates. The dependency of genome-wide effects on parent-of-origin, Ube3a genotype, and time (P2, P9) was investigated through transcriptome (RNA-seq of cortex and hypothalamus) and methylome (whole genome bisulfite sequencing of hypothalamus). Weighted gene co-expression and co-methylation network analyses identified co-regulated networks in maternally inherited Ube3a deletion offspring correlated with postnatal age that were enriched in developmental processes including Wnt signaling, synaptic regulation, neuronal and glial functions, epigenetic regulation, ubiquitin, circadian entrainment, and splicing. Furthermore, using this novel rat model, we showed that loss of the paternally expressed Ube3a antisense transcript resulted inboth unique and overlapping dysregulated gene pathways, predominantly at the level of differential methylation, when compared to loss of maternal Ube3a. Together, these results provide the most holistic examination to date of the molecular impacts of UBE3A loss in brain, supporting the existence of interactive epigenetic networks between maternal and paternal transcripts at the Ube3a locus.\n\nAuthor SummaryThe neurodevelopmental disorder Angelman syndrome is caused by loss of UBE3A from the maternal chromosome. UBE3A is a genomically imprinted gene, which results in parent-of-origin specific expression of a protein from the mother and a noncoding RNA from the father. While mouse models have been useful in investigating diverse roles for UBE3A, their partial mutations are of limited utility for investigating parental imprinting effects or identifying a complete list of downstream differences in gene pathways relevant to developing therapies for Angelman syndrome. To address this limitation, we utilized a novel rat model with a CRISPR/Cas9 engineered full UBE3A deletion and systems biology approaches to better understand how UBE3A loss affects early postnatal brain development. We discovered that UBE3A loss has widespread effects on many important neuronal and cellular pathways and uncovered interesting interactions between maternal and paternal genes that were not previously considered. Taken together, our findings provide the most comprehensive view of UBE3As influences in the brain, which are relevant to the understanding and development of treatments for Angelman syndrome and related neurodevelopmental disorders.

genomics↗

bioRxiv2019

Ultrafast Structural Changes Decomposed from Serial Crystallographic Data

Direct visualization of electronic and molecular events during chemical and biochemical reactions will offer unprecedented mechanistic insights. Ultrashort pulses produced by X-ray free electron lasers (XFELs) offer an unprecedented opportunity for direct observations of transient events as short-lived as tens of femtoseconds. This paper presents an in-depth analysis of the serial crystallographic datasets collected by Barends & Schlichting et al. (Science 350, 445, 2015) that probe the ligand photodissociation in carbonmonoxy myoglobin (MbCO), a long-serving hallmark for observing ultrafast dynamics in a biological system. This analysis reveals electron density changes that are caused directly by the formation of high-spin 3d atomic orbitals of the heme iron upon the CO departure and the dynamic behaviors of these newly formed orbitals in a time series within the first few picoseconds. The heme iron is found vibrating at a high frequency and developing a positional modulation that causes the iron to pop out of and recoil back into the heme plane in succession. These findings here provide long-awaited visual validations for previous works using ultrafast spectroscopy and molecular dynamics simulations. This analysis also extracts electron density variations largely in the solvent during the first period of a low frequency oscillation previously detected by coherence spectroscopy. This work demonstrates the power and importance of the analytical methods in detecting and isolating transient, often very weak signals of electronic changes arising from chemical reactions in proteins.\n\nSummaryDirect imaging of ultrafast and subtle structural events during a biochemical reaction, such as a single electronic transition from one atomic or molecular orbital to another, is highly desirable but has been beyond the reach of protein crystallography. It entails the capability of observing changes in electronic distributions at both an ultrafast time scale and an ultrahigh spatial resolution (Itatani et al., Nature 432, 867, 2004). The recent developments in femtosecond serial crystallography at X-ray free electron lasers (XFELs) have brought the achievable temporal resolution within a striking distance. This paper presents the electron density map decomposed from the XFEL data that shows the remanence of several 3d atomic orbitals of the heme iron at an available spatial resolution although the map component is not an accurate image of the atomic orbitals. A key strategy that has enabled the findings here is a numerical deconvolution to resolve concurrent variations in a series of time-resolved electron density maps so that the electron densities influenced by an electron transfer event can be isolated as a partial change from the overwhelming presence of the bulk electrons that are not directly involved in bonding. Even at the limited spatial resolution, the subtle changes in electron distribution due to a spin crossover can be decoupled from far greater changes due to atomic displacements. Direct observations of electronic orbitals could offer unprecedented mechanistic insights into a myriad of chemical and biochemical reactions such as electron transfer in redox reactions, and formation, rupture, and isomerization of chemical bonds.\n\nLigand photodissociation in carbonmonoxy myoglobin (MbCO) has been a benchmark for studying ultrafast protein dynamics in a biological system. A number of studies in time-resolved crystallography have progressively improved the time resolution (from [S]rajer et al., Science 274, 1726, 1996 to Barends et al., Science 350, 445, 2015). This paper presents an in-depth analysis of the serial crystallographic datasets of MbCO that Barends & Schlichting et al. (2015) contributed to the Protein Data Bank. First, a component of electron density distributions clearly shows the characteristic shape of the high-spin 3d orbitals reappeared at the heme iron upon the photodissociation of the CO ligand despite the limited accuracy of the orbital image due to the available spatial resolution. Second, the dynamic behaviors of these newly regained 3d orbitals within picoseconds after the photolysis provide long-awaited structural validation for previous spectroscopic observations and computational simulations. Specifically, the newly formed densities are oscillating with the heme iron at a high frequency of a thousand wavenumbers and developing a positional modulation during the first few picoseconds (Champion, Science 310, 980, 2005). The iron pops out of the heme plane at a few picoseconds and recoils back and pops out again afterwards. The dominant oscillation at a low frequency of several tens wavenumbers previously detected by coherence spectroscopy can be clearly resolved from the time series of electron density maps. The associated changes in electron density during the first cycle of the oscillation are largely located in the solvent rather than on the protein or heme, which suggests that the low frequency oscillations in a number of heme proteins, including MbCO, likely originate from a photolysis triggered pressure wave propagating in the solvated protein. Finally, these findings of chemical signals are isolated from coexisting thermal artifacts also by the numerical deconvolution. It is modeled in this study that the ultrashort XFEL pulses cause a transient spike of the local temperature at the heme site of hundreds of K.

biophysics↗

bioRxiv2020

Systems and classical biology approaches unraveled role of olfactory receptors in progression of kidney fibrosis

The olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are distributed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction. Transcriptional analysis demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, and Olfr161 during the progression of kidney fibrosis. In conclusion, our results highlight the impact of systems biology in determination of the underlying mechanisms of chronic diseases and indicate the importance of time-course approaches to unravel the patterns of gene expression. The novel ORs proposed in this study could be the subject of further functional investigations in the kidney.

systems biology↗

bioRxiv2016

Matching models across abstraction levels with Gaussian Processes

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learning methodology to automatically reconcile the predictions of different models across abstraction levels. Our approach is based on defining a correction map, a random function which modifies the output of a model in order to match the statistics of the output of a different model of the same system. We use two biological examples to give a proof-of-principle demonstration of the methodology, and discuss its advantages and potential further applications.

Biophysics↗

bioRxiv2017

Convection - Diffusion model of talking bacteria

Quorum sensing is cell to cell communication process through chemical signals formally known as autoinducers. When the concentration of quorum sensing molecules reached threshold concentration bacteria are in active state or quorum state. In this article, we propose a mathematical model of quorum sensing systems and study this biological system numerically. Moreover, we compare the different numerical scheme with the batch culture of P.aeruginosa. We observed a negative diffusion coefficient which plays an important role in the quorum sensing mechanism.

biophysics↗

bioRxiv2021

Robust Single-cell Matching and Multi-modalAnalysis Using Shared and Distinct FeaturesReveals Orchestrated Immune Responses

The ability to align individual cellular information from multiple experimental sources, techniques and systems is fundamental for a true systems-level understanding of biological processes. While single-cell transcriptomic studies have transformed our appreciation for the complexities and contributions of diverse cell types to disease, they can be limited in their ability to assess protein-level phenotypic information and beyond. Therefore, matching and integrating single-cell datasets which utilize robust protein measurements across multiple modalities is critical for a deeper understanding of cell states, and signaling pathways particularly within their native tissue context. Current available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely upon a large number of shared features across datasets for mutual Nearest Neighbor (mNN) matching. This approach is unsuitable when applied to single-cell proteomic datasets, due to the limited number of parameters simultaneously accessed, and lack of shared markers across these experiments. Here, we introduce a novel cell matching algorithm, Matching with pARtIal Overlap (MARIO), that takes into account both shared and distinct features, while consisting of vital filtering steps to avoid sub-optimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multi-modal methods, including spatial techniques, and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via CODEX imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid via CITE-seq. This cross-platform integrative analysis enabled the identification of unique orchestrated immune responses within the lung of complement-expressing macrophages and their impact on the local tissue microenvironment. MARIO thus provides an analytical framework for unified analysis of single-cell data for a comprehensive understanding of the underlying biological system.

bioinformatics↗

bioRxiv2023

Cleavable crosslinkers redefined by novel MS3-trigger algorithm

Crosslinking MS is currently transitioning from a routine tool in structural biology to enabling structural systems biology. MS-cleavable crosslinkers could substantially reduce the associated search space expansion by allowing an MS3-based approach for identifying crosslinked peptides. However, MS2-based approaches currently outperform approaches utilising MS3. We show here that MS3-trigger sensitivity and specificity were hampered algorithmically. Our four-step MS3-trigger algorithm greatly outperformed currently employed methods and comes close to reaching the theoretical limit.

bioinformatics↗

bioRxiv2023

Oblique Line Scan Illumination Enables Expansive, Accurate and Sensitive Single Protein Measurements in Solution and in Living Cells

Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in cells from which data-rich metrics can be extracted. SMT has been successfully applied to a variety of biological questions and model systems, aiming to unravel the spatiotemporal regulation of molecular mechanisms that govern protein function, downstream pathway effects, and cellular function. While powerful, SMLM often suffers from low throughput and illumination inhomogeneity, along with microscope and user-induced technical biases. Due to technical limitations in scaling SMLM techniques, a tradeoff between spatiotemporal resolution and throughput has been made historically, restricting broad application of these technologies. Here we address these limitations using Oblique Line Scan (OLS), a robust single-objective light-sheet based illumination and detection modality that achieves nanoscale spatial resolution and sub-millisecond temporal resolution across a 250 x 190 m field of view. We demonstrate OLS-enabled SMT on Halo-Tagged proteins in living cells capturing protein motion up to 14 m2 /s. By exploiting the adaptability of the acquisition frame rate and the improved rejection of out of focus light, we extend the utility of OLS beyond cellular compartments with in-solution SMT (isSMT) for single-molecule measurement of ligand-protein interactions and disruption of protein-protein interactions (PPI). We illustrate the versatility of OLS by showcasing two-color SMT, STORM, and single molecule fluorescence recovery after photobleaching (FRAP). OLS expands the range of SMLM applications and paves the way for robust, high-throughput single-molecule investigations of protein dynamics required for drug screening and systems biology studies, both in cells and in solution.

biophysics↗

bioRxiv2024

Quantifying Differential Rhythmicity based on Effect Sizes with LimoRhyde2

Current methods for assessing differential rhythmicity in genomic data focus on hypothesis testing and model selection, often assuming sinusoidal rhythms. A more appropriate approach is to estimate differences in rhythmic properties between two or more conditions using effect sizes. To address this gap, we extend LimoRhyde2, a method for quantifying rhythm-related effect sizes and their uncertainty in genome-scale data, to enable differential rhythmicity analyses. Through extensive testing, we validate the method for differential rhythmicity analysis and showcase how it improves biological interpretation for circadian systems biology.

bioinformatics↗

bioRxiv2024

Systems biology-enabled targeting of NF-κB and BCL2 overcomes microenvironment-mediated BH3-mimetic resistance in DLBCL.

In Diffuse Large B-cell Lymphoma (DLBCL), elevated anti-apoptotic BCL2-family proteins (e.g., MCL1, BCL2, BCLXL) and NF-{kappa}B subunits (RelA, RelB, cRel) confer poor prognosis. Heterogeneous expression, regulatory complexity, and redundancy offsetting the inhibition of individual proteins, complicate the assignment of targeted therapy. We combined flow cytometry fingerprinting, immunofluorescence imaging, and computational modeling to identify therapeutic vulnerabilities in DLBCL. The combined workflow predicted selective responses to BCL2 inhibition (venetoclax) and non-canonical NF-{kappa}B inhibition (Amgen16). Within the U2932 cell line we identified distinct resistance mechanisms to BCL2 inhibition in cellular sub-populations recapitulating intratumoral heterogeneity. Co-cultures with CD40L-expressing stromal cells, mimicking the tumor microenvironment (TME), induced resistance to BCL2 and BCLXL targeting BH3-mimetics via cell-type specific upregulation of BCLXL or MCL1. Computational models, validated experimentally, showed that basal NF-{kappa}B activation determined whether CD40 activation drove BH3-mimetic resistance through upregulation of RelB and BCLXL, or cRel and MCL1. High basal NF-{kappa}B activity could be overcome by inhibiting BTK to resensitize cells to BH3-mimetics in CD40L co-culture. Importantly, non-canonical NF-{kappa}B inhibition overcame heterogeneous compensatory BCL2 upregulation, restoring sensitivity to both BCL2- and BCLXL-targeting BH3-mimetics. Combined molecular fingerprinting and computational modelling provides a strategy for the precision use of BH3-mimetics and NF-{kappa}B inhibitors in DLBCL.

cancer biology↗

bioRxiv2025

ANI-netID: a genome similarity-network based biological identification system

Fungal identification is based on sequence similarity comparisons, with multiple genes often utilized in conjunction, similar to most taxonomic studies. The identification process involves the individual comparison of the similarities of each gene. However, owing to insufficient information, incomplete lineage sorting, gene transfer, hybridization, gene duplication, and loss, different species may match, depending on the DNA region used for comparison. Additionally, identification methods exist that set a uniform similarity threshold value, but they hit multiple species above the threshold. In this study, we introduced ANI-netID, which was developed to address these challenges. ANI-netID is an identification method based on the phylogenetic species concept, where the threshold value is determined by the combination of individuals with the lowest nucleotide similarity within groups (clique groups), such as species and genera. This checks whether the queried individual fall into the same clique group as the reference individual with the highest similarity. ANI-netID solves not only the above-mentioned problems but also indicates that the individual being identified may represent a new lineage, if the individual does not belong to any clique group.

microbiology↗