Biology subjects

Fertig, E. J.

Publications and source records attributed to Fertig, E. J..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

bioinformatics↗

bioRxiv2022

Transfer learning of an in vivo-derived senescence signature identifies conserved and tissue-specific senescence across species and diverse pathologies

Senescent cells (SnCs) contribute to normal tissue development and repair but accumulate with aging where they are implicated in a number of pathologies and diseases. Despite their pathological role and therapeutic interest, SnC phenotype and function in vivo remains unclear due to the challenges in identifying and isolating these rare cells. Here, we developed an in vivo-derived senescence gene expression signature using a model of the foreign body response (FBR) fibrosis in a p16Ink4a-reporter mouse, a cell cycle inhibitor commonly used to identify SnCs. We identified stromal cells (CD45-CD31- CD29+) as the primary p16Ink4a expressing cell type in the FBR and collected the cells to produce a SnC transcriptomic signature with bulk RNA sequencing. To computationally identify SnCs in bulk and single-cell data sets across species and tissues, we used this signature with transfer learning to generate a SnC signature score (SenSig). We found senescent pericyte and cartilage-like fibroblasts in newly collected single cell RNAseq (scRNASeq) data sets of murine and human FBR suggesting populations associated with angiogenesis and secretion of fibrotic extracellular matrix, respectively. Application of the senescence signature to human scRNAseq data sets from idiopathic pulmonary fibrosis (IPF) and the basal cell carcinoma microenvironment identified both conserved and tissue-specific SnC phenotypes, including epithelial-derived basaloid and endothelial cells. In a wound healing model, ligand-receptor signaling prediction identified putative interactions between SnC SASP and myeloid cells that were validated by immunofluorescent staining and in vitro coculture of SnCs and macrophages. Collectively, we have found that our SenSig transfer learning strategy from an in vivo signature outperforms in vitro-derived signatures and identifies conserved and tissue-specific SnCs and their SASP, independent of p16Ink4a expression, and may be broadly applied to elucidate SnC identity and function in vivo.

bioengineering↗

bioRxiv2022

Using Mathematical Modeling to Distinguish Intrinsic and Acquired Targeted Therapeutic Resistance in Head and Neck Cancer

The promise of precision medicine has been limited by the pervasive therapeutic resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies the mechanism and timing of cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

cancer biology↗

bioRxiv2021

Identifying Gene-wise Differences in Latent Space Projections Across Cell Types and Species in Single Cell Data using scProject

Latent space techniques have emerged as powerful tools to identify genes and gene sets responsible for cell-type and species-specific differences in single-cell data. Transfer learning methods can compare learned latent spaces across biological systems. However, the robustness that comes from leveraging information across multiple genes in transfer learning is often attained at the sacrifice of gene-wise precision. Thus, methods are needed to identify genes, defined as important within a particular latent space, that significantly differ between contexts. To address this challenge, we have developed a new framework, scProject, and a new metric, projectionDrivers, to quantitatively examine latent space usage across single-cell experimental systems while concurrently extracting the genes driving the differential usage of the latent space between defined contrasts. Here, we demonstrate the efficacy, utility, and scalability of scProject with projectionDrivers and provide experimental validation for predicted species-specific differences between the developing mouse and human retina.

bioinformatics↗

bioRxiv2021

Age-related Immune-Stromal Networks Inhibit Response to Regenerative Immunotherapies

Aging is associated with immunological changes that compromise response to infections and vaccines, exacerbate inflammatory diseases and could potentially mitigate tissue repair. Even so, age-related changes to the immune response to tissue damage and regenerative medicine therapies remain unknown. Here, we characterized how aging induces senescence and immunological changes that inhibit tissue repair and therapeutic response to a clinical regenerative biological scaffold derived from extracellular matrix. Tissue signatures of inflammation and interleukin (IL)-17 signaling increased with injury and treatment in aged animals, and computational analysis uncovered age-associated senescent-T cell communication that promotes type 3 immunity in T cells. Local inhibition of type 3 immune activation using IL17-neutralizing antibodies improved healing and restored therapeutic response to the regenerative biomaterial, promoting muscle repair in older animals. These results provide insights into tissue immune dysregulation that occurs with aging that can be targeted to rejuvenate repair.

bioengineering↗

bioRxiv2021

Fibroblast activation protein regulates natural killer cell migration, extravasation and tumor infiltration

Natural killer (NK) cells play a critical role in physiologic and pathologic conditions such as pregnancy, infection, autoimmune disease and cancer. In cancer, numerous strategies have been designed to exploit the cytolytic properties of NK cells, with variable success. A major hurdle to NK-cell focused therapies is NK cell recruitment and infiltration into tumors. While the chemotaxis pathways regulating NK recruitment to different tissues are well delineated, the mechanisms human NK cells employ to physically migrate are ill-defined. We show for the first time that human NK cells express fibroblast activation protein (FAP), a cell surface protease previously thought to be primarily expressed by activated fibroblasts. FAP degrades the extracellular matrix to facilitate cell migration and tissue remodeling. We used novel in vivo zebrafish and in vitro 3D culture models to demonstrate that FAP knock out and pharmacologic inhibition restrict NK cell migration, extravasation, and invasion through tissue matrix. Notably, forced overexpression of FAP promotes NK cell invasion through matrix in both transwell and tumor spheroid assays, ultimately increasing tumor cell lysis. Additionally, FAP overexpression enhances NK cells invasion into a human tumor in immunodeficient mice. These findings demonstrate the necessity of FAP in NK cell migration and present a new approach to modulate NK cell trafficking and enhance cell-based therapy in solid tumors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/429622v3_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@d6d9e5org.highwire.dtl.DTLVardef@17b6790org.highwire.dtl.DTLVardef@cc581eorg.highwire.dtl.DTLVardef@1ad2129_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Phenotypic shifts of tumor associated macrophages and STAT3 mediated suppression of myeloid derived suppressor cells drive sensitization of HER2+ tumor immunity.

Understanding how novel therapeutic combinations alter solid tumor microenvironments (TME) in immunosuppressive tumors such as breast cancer is essential to improve their responses to immune checkpoint inhibitors (ICIs). Entinostat, an oral histone deacetylase inhibitor (HDACi), has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs, but the precise alterations induced by entinostat and mechanisms of synergy with ICIs remain unknown. Here, we employ single-cell RNA-sequencing on HER2 overexpressing breast tumors from mice treated with entinostat + ICIs to characterize these changes across cell types in the TME. This analysis demonstrates that treatment with entinostat induces a shift from a pro-tumor to an anti-tumor TME signature characterized predominantly by changes in the myeloid cells. Notably, myeloid-derived suppressor cells (MDSCs) are shifted toward the less suppressive granulocytic phenotype in association with reduced signaling through the STAT3 pathway. In addition, tumor-associated macrophages are shifted toward an anti-tumor M1 phenotype by epigenetic reprogramming. Overall, these entinostat-induced TME changes reduce immunosuppression and increase mechanisms of tumor cell killing to improve ICI responses and broaden the population of patients who could potentially benefit from immunotherapy.

immunology↗

bioRxiv2020

nearBynding: A flexible pipeline characterizing protein binding to local RNA structure

The etiology of diseases driven by dysregulated mRNA metabolism can be elucidated by characterizing the responsible RNA-binding proteins (RBPs). Although characterizations of RBPs have been mainly focused on their binding sequences, not much has been investigated about their preferences for RNA structures. We present nearBynding, an R/Bioconductor pipeline that incorporates RBP binding sites and RNA structure information to discern structural binding preferences for an RBP. nearBynding visualizes RNA structure at and proximal to sites of RBP binding transcriptome-wide, analyzes CLIP-seq data without peak-calling, and provides a flexible scaffold to study RBP binding preferences relative to diverse RNA structure data types.

bioinformatics↗

bioRxiv2020

Immune Selection Pressure Contributes to Pancreatic Cancer Immune Evasion

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune evasion in pancreatic cancer is induced in response to T cell-based immune selection pressure, and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide the first evidence that T cell-dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune evasion. mT3-2D cells derived from a Kras+/LSL-G12D; Trp53+/LSL-R172H; Pdx1-Cre (KPC) mouse model of pancreatic cancer were grown in immunocompetent and immunodeficient C57BL/6 mice, and analyzed to determine the impacts of adaptive immunity specifically on malignant epithelial cells as well as on whole tumors. We found that immune selection pressure, via signal transducer and activator of transcription 1 (STAT1), stimulates malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and alters intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA approved drug, ruxolitinib, overcomes these tumor-protective responses and improves anti-PD1 antibody therapeutic efficacy. These findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.

cancer biology↗

bioRxiv2020

Aging interacts with tumor biology to produce major changes in the immune tumor microenvironment

Advanced age is strongly correlated with both increased cancer incidence and general immune decline. The immune tumor microenvironment (ITME) has been established as an important prognostic of both therapeutic efficacy and overall patient survival. Thus, age-related immune decline is an important consideration for the treatment of a large subset of cancer patients. Current studies of aging-related immune alterations are predominantly performed on non-cancerous tissue, requiring additional study into the effects of age on tumor immune infiltration. We leverage large scale transcriptional data sets from The Cancer Genome Atlas and the Genotype-Tissue Expression project to distinguish normal age-related immune alterations from age-related changes in tumor immune infiltration. We demonstrate that while there is overlap between the normal immune aging phenotype and that of the ITME, there are several changes in immune cell abundance that are specific to the ITME, particularly in T cell, NK cell, and Macrophage populations. These results suggest that aged immune cells are more susceptible to tumor suppression of cytotoxic immune cell infiltration and activity than normal tissues, which creates an unfavorable ITME in older patients in excess of normal immune decline with age and may inform the application of existing and emerging immunotherapies for this large population of patients. We additionally identify that age-related increases in tumor mutational burden are associated with decreased DNA methylation and increased expression of the immune checkpoint genes PDL1, CD80, and LAG3 which may have implications for therapeutic application of immune checkpoint blockade in older patients.

cancer biology↗

bioRxiv2020

A novel mechanism of natural killer cell response to anti-CTLA-4 therapy identified by integrative analysis of mouse and human tumors

BackgroundTumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including notably immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun to use single cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in bench to bedside research. MethodWe previously developed a computational transfer learning approach to identify shared biology between independent high-throughput single-cell RNA sequencing (scRNA-seq) datasets. In the present study, we test this frameworks ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and further expand its application beyond comparison of scRNA-seq datasets into comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry. ResultsWe found a conserved signature of NK cell activation in anti-CTLA-4 responsive mice and human tumors. In human melanoma, we found that the NK cell activation signature correlates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 independent of the antibody binding receptor (FcR), and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation. ConclusionsThese data demonstrate the ability of our transfer learning approach to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many immuno-oncology questions, enhancing bench to bedside research and enabling better understanding and treatment of disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/125625v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1ca0c13org.highwire.dtl.DTLVardef@1dedc51org.highwire.dtl.DTLVardef@5277eforg.highwire.dtl.DTLVardef@4007c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

While single-cell ATAC-seq analysis methods allow for robust clustering of cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

bioinformatics↗

bioRxiv2019

Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development.

The development of single-cell RNA-Sequencing (scRNA-Seq) has allowed high resolution analysis of cell type diversity and transcriptional networks controlling cell fate specification. To identify the transcriptional networks governing human retinal development, we performed scRNA-Seq over retinal organoid and in vivo retinal development, across 20 timepoints. Using both pseudotemporal and cross-species analyses, we examined the conservation of gene expression across retinal progenitor maturation and specification of all seven major retinal cell types. Furthermore, we examined gene expression differences between developing macula and periphery and between two distinct populations of horizontal cells. We also identify both shared and species-specific patterns of gene expression during human and mouse retinal development. Finally, we identify an unexpected role for ATOH7 expression in regulation of photoreceptor specification during late retinogenesis. These results provide a roadmap to future studies of human retinal development, and may help guide the design of cell-based therapies for treating retinal dystrophies.

neuroscience↗

bioRxiv2019

Integrated single cell and bulk multi-omics reveals heterogeneity and early changes in pathways associated with cetuximab resistance in HNSCC sensitive cell lines

Identifying potential mechanisms of resistance while tumor cells still respond to therapy is critical to delay acquired resistance. We generated the first comprehensive multi-omics, bulk and single cell data in sensitive head and neck squamous cell carcinoma (HNSCC) cells to identify immediate responses to cetuximab. Two pathways potentially associated with resistance were focus of the study: regulation of receptor tyrosine kinases through the transcription factor TFAP2A, and epithelial-to-mesenchymal transition (EMT) process. Single cell RNA-seq demonstrates heterogeneity, with cell specific TFAP2A and VIM expression profiles in response to treatment. RNA-seq and ATAC-seq reveal global changes within five days of cetuximab therapy, suggesting early onset of mechanisms of resistance; and corroborates cell line heterogeneity, with different TFAP2A targets or EMT markers affected by therapy. Lack of TFAP2A reduces HNSCC growth and is enhanced by cetuximab and JQ1. Regarding the EMT process, short term cetuximab therapy has the strongest effect on inhibiting migration. TFAP2A silencing does not affect cell migration, supporting an independent role for both mechanisms in resistance. Overall, we show that immediate adaptive transcriptional and epigenetic changes induced by cetuximab are heterogeneous and cell type dependent; and independent mechanisms of resistance arise while tumor cells are still sensitive to therapy.

cancer biology↗

bioRxiv2019

Human cortical neural stem cells generate regional organizer states in vitro before committing to excitatory neuronal fates

Better understanding the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here we used RNA-sequencing, cell imaging and lineage tracing of mouse and human in vitro NSCs to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs developed from pluripotency in vitro, they first transitioned through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines varied in these early patterning states leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analysis of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon.

neuroscience↗

bioRxiv2018

Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies reveal the prevalence of intra-and inter-tumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intra-and inter-tumor molecular heterogeneity. We devised a new algorithm, Expression Variation Analysis in Single Cells (EVAsc), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVAsc has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. We then apply EVAsc to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics, i.e. immunogenicity, cancer subtypes, and metastasis. Immune pathway heterogeneity in hematopoietic cell populations in breast tumors corresponded to the amount diversity present in the T-cell repertoire of each individual. In head and neck squamous cell carcinoma (HNSCC) patients, we found dramatic differences in pathway dysregulation across basal primary tumors. Within the basal primary tumors we also identified increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. Moreover, cells in HNSCC primary tumors had significantly more heterogeneity across pathways than cells in metastases, consistent with a model of clonal outgrowth. These results demonstrate the broad utility of EVAsc to quantify inter-and intra-tumor heterogeneity from scRNA-seq data without reliance on low dimensional visualization.

bioinformatics↗

bioRxiv2018

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species.

New approaches are urgently needed to glean biological insights from the vast amounts of single cell RNA sequencing (scRNA-Seq) data now being generated. To this end, we propose that cell identity should map to a reduced set of factors which will describe both exclusive and shared biology of individual cells, and that the dimensions which contain these factors reflect biologically meaningful relationships across different platforms, tissues and species. To find a robust set of dependent factors in large-scale scRNA- Seq data, we developed a Bayesian non-negative matrix factorization (NMF) algorithm, scCoGAPS. Application of scCoGAPS to scRNA-Seq data obtained over the course of mouse retinal development identified gene expression signatures for factors associated with specific cell types and continuous biological processes. To test whether these signatures are shared across diverse cellular contexts, we developed projectR to map biologically disparate datasets into the factors learned by scCoGAPS. Because projecting these dimensions preserve relative distances between samples, biologically meaningful relationships/factors will stratify new data consistent with their underlying processes, allowing labels or information from one dataset to be used for annotation of the other--a machine learning concept called transfer learning. Using projectR, data from multiple datasets was used to annotate latent spaces and reveal novel parallels between developmental programs in other tissues, species and cellular assays. Using this approach we are able to transfer cell type and state designations across datasets to rapidly annotate cellular features in a new dataset without a priori knowledge of their type, identify a species-specific signature of microglial cells, and identify a previously undescribed subpopulation of neurosecretory cells within the lung. Together, these algorithms define biologically meaningful dimensions of cellular identity, state, and trajectories that persist across technologies, molecular features, and species.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC=\"FIGDIR/small/395004_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (81K):\norg.highwire.dtl.DTLVardef@dd1c07org.highwire.dtl.DTLVardef@5b1109org.highwire.dtl.DTLVardef@bb6714org.highwire.dtl.DTLVardef@16c66f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

REVA: a rank-based multi-dimensional measure of correlation

The neighbors principle implicit in any machine learning algorithm says that samples with similar labels should be close to one another in feature space as well. For example, while tumors are heterogeneous, tumors that have similar genomics profiles can also be expected to have similar responses to a specific therapy. Simple correlation coefficients provide an effective way to determine whether this principle holds when features and labels are both scalar, but not when either is multivariate. A new class of generalized correlation coefficients based on inter-point distances addresses this need and is called \"distance correlation\". There is only one rank-based distance correlation test available to date, and it is asymmetric in the samples, requiring that one sample be distinguished as a fixed point of reference. Therefore, we introduce a novel, nonparametric statistic, REVA, inspired by the Kendall rank correlation coefficient. We use U-statistic theory to derive the asymptotic distribution of the new correlation coefficient, developing additional large and finite sample properties along the way. To establish the admissibility of the REVA statistic, and explore the utility and limitations of our model, we compared it to the most widely used distance based correlation coefficient in a range of simulated conditions, demonstrating that REVA does not depend on an assumption of linearity, and is robust to high levels of noise, high dimensions, and the presence of outliers. We also present an application to real data, applying REVA to determine whether cancer cells with similar genetic profiles also respond similarly to a targeted therapeutic.\n\nAuthor summarySometimes a simple question arises: how does the distance between two samples in multivariate space compare to another scalar value associated with each sample. Here, we propose theory for a nonparametric test to statistically test this association. This test is independent of the scale of the scalar data, and thus generalizable to any comparison of samples with both high-dimensional data and a scalar. We apply the resulting statistic, REVA, to problems in cancer biology motivated by the model that cancer cells with more similar gene expression profiles to one another can be expected to have a more similar response to therapy.

bioinformatics↗