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Results for “molecular biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

A NOVEL PREGNANT RAT MODEL FOR LABOR INDUCTION AND AUGMENTATION WITH OXYTOCIN

BackgroundDespite the widespread use of oxytocin for induction of labor, mechanistic insights into maternal and neonatal wellbeing are lacking because of the absence of an animal model that recapitulates modern obstetric practice. ObjectiveThe objectives of this research were to create and validate a hi-fidelity animal model that mirrors labor induction with oxytocin in parturients and to assess its translational utility. Study DesignThe study was performed in timed-pregnant Sprague Dawley dams. The model consisted of a subcutaneously implanted microprocessor-controlled infusion pump on gestational day 18 that was pre-programmed to deliver an escalating dose of intravenous oxytocin on gestational day 21 to induce birth. Once predictable delivery of healthy pups was achieved, we validated the model with molecular biological experiments on the uterine myometrium and telemetry-supported assessment of changes in intrauterine pressure. Finally, we applied this model to test the hypothesis that labor induction with oxytocin was associated with oxidative stress in the newborn brain with a comprehensive array of biomarker assays and oxidative stress gene expression studies. ResultsDuring the iterative model development phase, we confirmed the optimal gestational age for pump implantation, the concentration of oxytocin, and the rate of oxytocin administration. Exposure to anesthesia and surgery during pump implantation was not associated with significant changes in the cortical transcriptome. Activation of pump with oxytocin on gestational day 21 resulted in predictable delivery of pups within 8-12 hours. Increased frequency of change of oxytocin infusion rate was associated with dystocic labor. Labor induction and augmentation with oxytocin was associated with increased expression of the oxytocin receptor gene in the uterine myometrium, decreased expression of the oxytocin receptor protein on the myometrial cell membrane, and cyclical increases in intrauterine pressure. Examination of the frontal cortex of vaginally delivered newborn pups born after oxytocin-induced labor did not reveal an increase in oxidative stress compared to saline-treated control pups. Specifically, there were no significant changes in oxidative stress biomarkers involving both the oxidative stress (reactive oxygen/nitrogen species, 4-hydroxynonenal, protein carbonyl) and the antioxidant response (total glutathione, total antioxidant capacity). In addition, there were no significant differences in the expression of 16 genes emblematic of the oxidative stress response pathway. ConclusionsCollectively, we provide a viable and realistic animal model for labor induction and augmentation with oxytocin. We demonstrate its utility in addressing clinically relevant questions in obstetric practice that could not be mechanistically ascertained otherwise. Based on our findings, labor induction with oxytocin is not likely to cause oxidative stress in the fetal brain. Adoption of our model by other researchers would enable new lines of investigation related to the impact of perinatal oxytocin exposure on the mother-infant dyad.

pharmacology and toxicology↗

bioRxiv2021

Structural remodeling of SARS-CoV-2 spike protein glycans reveals the regulatory roles in receptor binding affinity

Glycans of the SARS-CoV-2 spike protein are speculated to play functional roles in the infection processes as they extensively cover the protein surface and are highly conserved across the variants. To date, the spike protein has become the principal target for vaccine and therapeutic development while the exact effects of its glycosylation remain elusive. Experimental reports have described the heterogeneity of the spike protein glycosylation profile. Subsequent molecular simulation studies provided a knowledge basis of the glycan functions. However, there are no studies to date on the role of discrete glycoforms on the spike protein pathobiology. Building an understanding of its role in SARS-CoV-2 is important as we continue to develop effective medicines and vaccines to combat the disease. Herein, we used designed combinations of glycoengineering enzymes to simplify and control the glycosylation profile of the spike protein receptor-binding domain (RBD). Measurements of the receptor binding affinity revealed the regulatory effects of the RBD glycans. Remarkably, opposite effects were observed from differently remodeled glycans, which presents a potential strategy for modulating the spike protein behaviors through glycoengineering. Moreover, we found that the reported anti-SARS-CoV-(2) antibody, S309, neutralizes the impact of different RBD glycoforms on the receptor binding affinity. Overall, this work reports the regulatory roles that glycosylation plays in the interaction between the viral spike protein and host receptor, providing new insights into the nature of SARS-CoV-2. Beyond this study, enzymatic remodeling of glycosylation offers the opportunity to understand the fundamental role of specific glycoforms on glycoconjugates across molecular biology. Covert art LegendsThe glycosylation of the SARS-CoV-2 spike protein receptor-binding domain has regulatory effects on the receptor binding affinity. Sialylation or not determines the "stabilizing" or "destabilizing" effect of the glycans. (Protein structure model is adapted from Protein Data Bank: 6moj. The original model does not contain the glycan structure.) SignificanceGlycans extensively cover the surface of SARS-CoV-2 spike (S) protein but the relationships between the glycan structures and the protein pathological behaviors remain elusive. Herein, we simplified and harmonized the glycan structures in the S protein receptor-binding domain and reported their regulatory roles in human receptor interaction. Opposite regulatory effects were observed and were determined by discrete glycan structures, which can be neutralized by the reported S309 antibody binding to the S protein. This report provides new insight into the mechanism of SARS-CoV-2 S protein infection as well as S309 neutralization.

biochemistry↗

bioRxiv2021

Methylothon: a versatile course-based high school research experience in microbiology and bioinformatics-- with pink bacteria

Methylothon is an inquiry-based high school learning module in microbial ecology, molecular biology, and bioinformatics that centers around pink-pigmented plant-associated methylotrophic bacteria. Here we present an overview of the modules learning goals, describe course resources (available for public use on http://methylothon.com), and relate lessons learned from adapting Methylothon for remote learning during the pandemic in spring of 2021. The original in-person version of the module allows students to isolate their own strains of methylotrophic bacteria from plants they sample from the environment, to identify these using PCR, sequencing, and phylogenetic analysis, and to contribute their strains to original research in a university lab. The adapted version strengthens the focus on bioinformatics and increases its flexibility and accessibility by making the lab portion optional and adopting free web-based tools. Student feedback and graded assignments from Spring 2021 revealed that the lesson was especially effective at introducing the concepts of BLAST and phylogenetic trees, and that students valued and felt inspired by the opportunity to conduct hands-on work and to participate in community science.

scientific communication and education↗

bioRxiv2021

GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation

BackgroundOver the past three decades there have been numerous molecular biology developments that have led to an explosion in the number of gene expression studies being performed. Many of these gene expression studies publish their data to the public database GEO, making them freely available. By analysing gene expression datasets, researchers can identify genes that are differentially expressed between two groups. This can provide insights that lead to the development of new tests and treatments for diseases. Despite the wide availability of gene expression datasets, analysing them is difficult for several reasons. These reasons include the fact that most methods for performing gene expression analysis require programming proficiency. ResultsWe developed the GEOexplorer software package to overcome several of the difficulties in performing gene expression analysis. GEOexplorer was therefore developed as a web application, that can perform interactive and reproducible microarray gene expression analysis, while producing a wealth of interactive visualisations to facilitate result exploration. GEOexplorer is implemented in R using the Shiny framework and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of exploratory data analysis and differential gene expression analysis intuitively and generate a broad spectrum of publication ready outputs. ConclusionGEOexplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/GEOexplorer/). GEOexplorer provides a solution for performing interactive and reproducible analyses of microarray gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on GEO microarray datasets.

bioinformatics↗

bioRxiv2021

Proteomic analysis of mouse kidney tissue associates peroxisomal dysfunction with early diabetic kidney disease

BackgroundThe absence of efficient inhibitors for DKD progression reflects the gaps in our understanding of DKD molecular pathogenesis. A comprehensive proteomic analysis was performed on glomeruli and kidney cortex of diabetic mice with subsequent validation of findings in human biopsies and - omics datasets aiming to better understand the underlying molecular biology of early DKD development and progression. MethodsLC-MS/MS was employed to analyze the kidney proteome of DKD mouse models: Glomeruli of Ins2Akita mice 2 month and 4 month old, and cortex of db/db mice 6 month old. Following label-free quantification, the abundance of detected proteins were correlated with existing kidney datasets and functionally annotated. Tissue sections from 16 DKD patients were analyzed by IHC. ResultsPathway analysis of differentially expressed proteins in the early and late DKD versus controls predicted dysregulation in DKD hallmarks (such as peroxisomal lipid metabolism, {beta}-oxidation and TCA cycle) supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal proteins such as NUDT19, ACOX1, and AMACR and upregulated mitochondrial proteins related to aminoacid metabolism including GLS, GLDC, and GCAT. Several of these changes were also observed in the kidney cortex proteome of db/db mice. IHC of human kidney further confirmed the differential expression of NUDT19, AGPS, AMACR and CAT proteins in DKD. ConclusionsOur study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity. These proteins therefore represent potential markers of early DKD pathogenesis. Collectively, essential pathways associated with peroxisomes such as lipid {beta}-oxidation, plasmalogen synthesis, aminoacid metabolism and response to oxidative stress are downregulated in early DKD, providing new perspectives and potential markers of diabetic kidney dysfunction.

systems biology↗

bioRxiv2021

FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution

MotivationFactor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput data sets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shares information across a potentially large number of cells in a common weight matrix, allowing consistent interpretation of factors across cells and yielding improved latent variable estimates. ResultsWe compare FISHFactor to existing methods that rely on aggregating information through spatial binning and cannot combine information from multiple cells, and show that our method leads to more accurate results on simulated data. We demonstrate on a real data set that FISHFactor is able to identify major subcellular expression patterns and spatial gene clusters in a data-driven manner. Availability and ImplementationThe model implementation, data simulation and experiment scripts are available under https://www.github.com/bioFAM/FISHFactor. Contactb.velten@dkfz.de

bioinformatics↗

bioRxiv2021

Antibiofilm agents with therapeutic potential against enteroaggregative Escherichia coli

BackgroundEnteroaggregative Escherichia coli (EAEC) is a predominant but neglected enteric pathogen implicated in infantile diarrhoea and nutrient malabsorption. There are no non-antibiotic approaches to dealing with persistent infection by these exceptional colonizers, which form copious biofilms. We screened the Medicines for Malaria Venture Pathogen Box for chemical entities that inhibit EAEC biofilm formation. MethodologyWe used two EAEC strains, 042 and MND005E, in a medium-throughput crystal violet-based antibiofilm screen. Hits were confirmed in concentration-dependence, growth kinetic and time course assays and activity spectra were determined against a panel of genome-sequenced EAEC. Antibiofilm activity against isogenic EAEC mutants, molecular docking simulations and comparative genomic analysis were used to identify the mechanism of action of one hit. Principal findingsIn all, five compounds (1.25%) reproducibly inhibited biofilm accumulation by at least one strain by 30-85% while inhibiting growth by under 10%. Hits exhibited at least 10-fold greater antibiofilm activity than nitazoxanide, the only known EAEC biofilm inhibitor. Reflective of known EAEC heterogeneity, only one hit was active against both screen isolates, but three hits showed broad antibiofilm activity against a larger panel of strains. Mechanism of action studies point to the EAEC anti-aggregation protein (Aap), dispersin, as the target of compound MMV687800. ConclusionsThis study identified five compounds not previously described as anti-adhesins or Gram-negative antibacterials with significant and specific EAEC antibiofilm activity. One molecule, MMV687800, targets the EAEC Aap. In vitro small-molecule inhibition of EAEC colonization opens a way to new therapeutic approaches to preventing and treating EAEC infection. Author summaryDiarrhoea accounts for over half a million deaths in children under five annually. It additionally contributes to childhood malnutrition as well as growth and development deficiencies, particularly in low-income countries. Enteroaggregative Escherichia coli (EAEC) causes diarrhoea that is often persistent and can also contribute to growth deficiencies in young children. EAEC is a neglected pathogen that is often resistant to antimicrobial drugs. Small molecules that block EAEC colonization may hold the key to interfering with EAEC disease without promoting antimicrobial resistance. We screened the Medicines for Malaria Ventures Pathogen Box for chemicals that can interfere with EAEC biofilm formation, a key colonization indicator. Our screen identified five biofilm-inhibiting molecules that did not interfere with bacterial viability and therefore are unlikely to exert strong pressure for resistance. Molecular biology and computational investigations point to the EAEC anti-aggregative protein, also known as dispersin, as a possible target for one of these hit molecules. Optimizing EAEC antibiofilm hits will create templates that can be employed for resolving EAEC diarrhoea and related infections.

microbiology↗

bioRxiv2021

Mesowestern Blot: Simultaneous Analysis of Hundreds of Sub-Microliter Lysates

Western blotting is a widely-used technique for molecular-weight-resolved analysis of proteins and their post-translational modifications, but has been refractory to affordable scale-up. Here, we report the Mesowestern blot, which uses a 3D-printable gel-casting mold to enable affordable, high-throughput Western blotting with standard sample preparation and small (<1 uL) sample sizes. The casted polyacrylamide gel contains 336, 0.5 uL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution range. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder as well as cell lysate (RIPA buffer) demonstrate protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semi-dry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is approximately 2 orders of magnitude, with a limit of detection (for {beta}-actin) of around 30 ng of total protein from mammalian cell lysates (~30-3000 cells). Because the gel mold is 3D-printable, users have significant design freedom for custom layouts, and there are few barriers to adoption by the typical cell and molecular biology laboratory already performing Western blots.

biochemistry↗

bioRxiv2021

pGlycoQuant with a deep residual network for precise and minuscule-missing-value quantitative glycoproteomics enabling the functional exploration of site-specific glycosylation

Interpreting large-scale glycoproteomic data for intact glycopeptide identification has been tremendously advanced by software tools. However, software tools for quantitative analysis of intact glycopeptides remain lagging behind, which greatly hinders exploring the differential expression and functions of site-specific glycosylation in organisms. Here, we report pGlycoQuant, a generic software tool for accurate and convenient quantitative intact glycopeptide analysis, supporting both primary and tandem mass spectrometry quantitation for multiple quantitative strategies. pGlycoQuant enables intact glycopeptide quantitation with very low missing values via a deep residual network, thus greatly expanding the quantitative function of several powerful search engines, currently including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. The pGlycoQuant-based site-specific N-glycoproteomic study conducted here quantifies 6435 intact N-glycopeptides in three hepatocellular carcinoma cell lines with different metastatic potentials and, together with in vitro molecular biology experiments, illustrates core fucosylation at site 979 of the L1 cell adhesion molecule (L1CAM) as a potential regulator of HCC metastasis. pGlycoQuant is freely available at https://github.com/expellir-arma/pGlycoQuant/releases/. We have demonstrated pGlycoQuant to be a powerful tool for the quantitative analysis of site-specific glycosylation and the exploration of potential glycosylation-related biomarker candidates, and we expect further applications in glycoproteomic studies.

systems biology↗

bioRxiv2021

Dairy products as sources of methanogens for humans

Methanogens are detected in human gut from the first moments of life and there is a diversification of methanogens during infancy. However, the sources of acquisition of methanogens are not well elucidated. We therefore investigated 56 dairy products as potential sources of methanogens by applying molecular biology. In the presence of negative controls, we obtained an overall prevalence of methanogens in 85.7% (48/56) of samples by real-time PCR. Further PCR-sequencing identified 73.2% (41/56) of Methanobrevibacter smithii. We also found for the first time in dairy products 1.8% (1/56) of Methanobrevibacter oralis, 7.1% (4/56) of Methanobrevibacter millerae, 1.8% (1/56) of Methanobrevibacter ruminantium, 1.8% (1/56) of Methanocorpusculum sp. We observed a significant presence (p-value=0.001) of methanogens in fermented dairy products compared to unfermented dairy products. This study gives credit to the fact that dairy products could be considered as a source of methanogens for humans, especially for children.

microbiology↗

bioRxiv2021

Orthogonal control of gene expression in plants using synthetic promoters and CRISPR-based transcription factors

BackgroundThe construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression. ResultsA panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants. ConclusionsThe design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.

synthetic biology↗

bioRxiv2021

Genotyping strategies for detecting CRISPR mutations in polyploid species: a case study-based approach in hexaploid wheat

As a versatile tool for genome engineering, CRISPR-Cas9 has been revolutionizing the field of molecular biology, biotechnology, and crop improvement. By precisely targeting pre-selected genomic sites, CRISPR-Cas9 primarily induces insertions or deletions (indels) of variable size. Despite the significant advance in the technology per se, detecting these indels is the major and difficult part of the CRISPR program in polyploid species, like wheat, with relatively low mutation rates. A plethora of methods are available for detecting mutations, but no method is perfect for all mutation types. In this case study, we demonstrated a new, protocol for capturing length polymorphism from small indels using a nested PCR approach. This new method is tractable, efficient, and cost-effective in detecting and genotyping indels >3-bp. We also discussed the major genotyping platforms used in our wheat CRISPR projects, such as mismatch cleavage assay, restriction enzyme assay, ribonucleoprotein assay, and Sanger sequencing, for their advantages and pitfalls in wheat CRISPR mutation detection.

bioengineering↗

bioRxiv2021

On the reproductive mechanism of Gram-negative protocells

Bacterial protoplasts are known to reproduce independently of canonical molecular biological processes. Their reproduction is shown to be mediated entirely by the physicochemical properties of cell constituents. However, the physiochemical properties of the cell constituents are influenced by the environmental conditions like salinity, salt composition, and mechanical stresses experienced by a cell in natural environments. The influence of such environmental conditions on protoplast reproduction is seldom investigated. Here, we studied protoplast reproduction in their native environmental conditions. Contrary to the previous perceptions of protoplasts reproducing in an erratic manner, cells in our study reproduced in a defined sequence of steps. The process of their reproduction can be explained by an interplay between intracellular metabolism, the physicochemical properties of cell constituents, and the nature of cations in the growth media. We observed a minimal leakage of intracellular constituents during protoplast reproduction, suggesting an efficient reproduction. However, the efficiency of reproduction is determined by the environmental conditions. Under favorable environmental conditions, protoplasts reproduce with nearly similar efficiency to cells that possess a cell wall. In short, here we demonstrate the simplest method of cellular reproduction and the influence of environmental conditions on this process.

biophysics↗

bioRxiv2021

On the reproductive mechanisms of Gram-positive protocells

Prokaryotes are hypothesized to have evolved from more primitive protocells. Unlike present-day cells, protocells are thought to have been devoid of complex molecular biological processes. They are believed to have mediated reproduction entirely by biophysical forces under favorable environmental conditions. Despite this proposition, little is known about the actual mechanism of their reproduction. To understand the reproduction process of protocells in their native habitat, here we used a top-down approach to transform bacterial cells into a primitive lipid vesicle-like state. Given that environmental conditions are thought to have played an essential role in mediating protocell reproduction, we then studied these cells under the presumed environmental conditions of Archaean Eon Earth. Even in the absence of functioning biological processes, cells in our study reproduced in a defined sequence of steps, always leading to the formation of viable daughter cells. Their reproduction mechanism can be explained by the interaction between intracellular metabolism, physicochemical properties of cell constituents, and, most importantly, environmental conditions. Given the simplicity of this reproduction mechanism and its suitability to environmental conditions of early Earth, we propose that protocells reproduced by this process. Moreover, this method of reproduction is also in tune with the earlier theoretical propositions on protocells, the results of the top- down approach of building a minimal cell, and the paleontological record of the Achaean Eon. Our study is the first to bridge the gap between non-living systems like lipid vesicles, living cells, and the paleontology of the Archaean Eon.

biophysics↗

bioRxiv2021

RoPod, a customizable toolkit for non-invasive root imaging, reveals cell type-specific dynamics of plant autophagy

Arabidopsis root is a classic model system in plant cell and molecular biology. The sensitivity of plant roots to local environmental perturbation challenges data reproducibility and incentivizes further optimization of imaging and phenotyping tools. Here we present RoPod, an easy-to-use toolkit for low-stress live time-lapse imaging of Arabidopsis roots. RoPod comprises a dedicated protocol for plant cultivation and a customizable 3D-printed vessel with integrated microscopy-grade glass that serves simultaneously as a growth and imaging chamber. RoPod reduces impact of sample handling, preserves live samples for prolonged imaging sessions, and facilitates application of treatments during image acquisition. We describe a protocol for RoPods fabrication and provide illustrative application pipelines for monitoring root hair growth and autophagic activity. Furthermore, we showcase how the use of RoPods advanced our understanding of plant autophagy, a major catabolic pathway and a key player in plant fitness. Specifically, we obtained fine time resolution for autophagy response to commonly used chemical modulators of the pathway and revealed previously overlooked cell type-specific changes in the autophagy response. These results will aid a deeper understanding of the physiological role of autophagy and provide valuable guidelines for choosing sampling time during end-point assays currently employed in plant autophagy research.

cell biology↗

bioRxiv2021

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sub-lines.

Cultured cells are widely used in molecular biology despite poor understanding of how cell line genomes change in vitro over time. Previous work has shown that Drosophila cultured cells have a higher transposable element (TE) content than whole flies, but whether this increase in TE content resulted from an initial burst of transposition during cell line establishment or ongoing transposition in cell culture remains unclear. Here we sequence the genomes of 25 sub-lines of Drosophila S2 cells and show that TE insertions provide abundant markers for the phylogenetic reconstruction of diverse sub-lines in a model animal cell culture system. Analysis of DNA copy number evolution across S2 sub-lines revealed dramatically different patterns of genome organization that support the overall evolutionary history reconstructed using TE insertions. Analysis of TE insertion site occupancy and ancestral states support a model of ongoing transposition dominated by episodic activity of a small number of retrotransposon families. Our work demonstrates that substantial genome evolution occurs during long-term Drosophila cell culture, which may impact the reproducibility of experiments that do not control for sub-line identity.

genomics↗

bioRxiv2021

Structure and dynamics of human disease-complication network

A complication is an unanticipated disease arisen following, induced by a disease, a treatment or a procedure. We compile the Human Disease-Complication Network from the medical data and investigate the characteristics of the network. It is observed that the modules of the network are dominated by the classes of diseases. The relations between modules are unveiled in detail. Three nontrivial motifs are identified from the network. We further simulate the dynamics of motifs with the Boolean dynamic model. Each motif represents a specific dynamic behavior, which is potentially functional in the disease system, such as generating temporal progressions and governing the responses to fluctuating external stimuli. Author summaryAdvances in molecular biology lead to a new discipline of network medicine, investigating human diseases in a networked structure perspective. Recently, clinical records have been introduced to the research of complex networks of diseases. An important available medical dataset that has been overlooked so far is the complications of diseases, which are vital for human beings. We compile the Human Disease-Complication Network, representing the causality between the upstream diseases and their downstream complications. This work not only helps us to comprehend why certain groups of diseases appear collectively, but also provides a new paradigm to investigate the dynamics of disease progression. For clinical applications, the investigation of complications may yield new approaches to disease prevention, diagnosis and treatment.

bioinformatics↗

bioRxiv2021

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CO_SCPLOWELLC_SCPLOWOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CO_SCPLOWELLC_SCPLOWOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

bioinformatics↗