Biology subjects

Zhao, N.

Publications and source records attributed to Zhao, N..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

MOSAIC: A Pipeline for MicrobiOme Studies Analytical Integration and Correction

Large-scale and consortium microbiome studies have enabled identification of reliable population-level biomedical signals, wherein integration is essential to eliminate unwanted variations between batches or studies and retain biological signals. Many strategies, each with distinct advantages and limitations, have been adapted or developed for microbiome data. The optimal strategy for a given study needs to be determined on a data-specific, case-by-case basis. Here, we develop the first-of-its-kind MicrobiOme Studies Analytical Integration and Correction (MOSAIC) pipeline to enable a convenient, fair, and comprehensive comparison of integration strategies. It includes modules for pre-processing, integration, and evaluation of artifact removal and signal preservation, using metrics relevant to common microbiome analyses, including alpha and beta diversities, disease prediction, and differential abundance analysis. We applied MOSAIC to extensive real-world and simulated data and found that though no single strategy excels in all aspects, yet certain strategies, the ComBat and ConQuR families, perform better overall.

bioinformatics↗

bioRxiv2024

RPS19 and RPL5, the most commonly mutated genes in Diamond Blackfan anemia, impact DNA double-strand break repair

Diamond Blackfan anemia (DBA) is caused by germline heterozygous loss-of-function pathogenic variants (PVs) in ribosomal protein (RP) genes, most commonly RPS19 and RPL5. In addition to red cell aplasia, individuals with DBA are at increased risk of various cancers. Importantly, the mechanism(s) underlying cancer predisposition are poorly understood. We found that DBA patient-derived lymphoblastoid cells had persistent {gamma}-H2AX foci following ionizing radiation (IR) treatment, suggesting DNA double-strand break (DSB) repair defects. RPS19- and RPL5-knocked down (KD) CD34+ cells had delayed repair of IR-induced DSBs, further implicating these RPs in DSB repair. Assessing the impact of RPS19- and RPL5-KD on specific DSB repair pathways, we found RPS19-KD decreased the efficiency of pathways requiring extensive end-resection, whereas RPL5-KD increased end-joining pathways. Additionally, RAD51 was reduced in RPS19- and RPL5-KD and RPS19- and RPL5-mutated DBA cells, whereas RPS19-deficient cells also had a reduction in PARP1 and BRCA2 proteins. RPS19-KD cells had an increase in nuclear RPA2 and a decrease in nuclear RAD51 foci post-IR, reflective of alterations in early, critical steps of homologous recombination. Notably, RPS19 and RPL5 interacted with poly(ADP)-ribose chains noncovalently, were recruited to DSBs in a poly(ADP)-ribose polymerase activity-dependent manner, and interacted with Ku70 and histone H2A. RPL5s recruitment, but not RPS19s, also required p53, suggesting that RPS19 and RPL5 directly participate in DSB repair via different pathways. We propose that defective DSB repair arising from haploinsufficiency of these RPs may underline the cancer predisposition in DBA. KEY POINTSO_LIRPS19- and RPL5-deficiency alters DNA double-strand break repair, which may underlie cancer predisposition in Diamond Blackfan anemia. C_LIO_LIRPS19 and RPL5 bind PAR chains and are rapidly recruited to sites of DNA double-strand breaks, suggesting active roles in repair. C_LI

molecular biology↗

bioRxiv2024

Faecalibacterium prausnitzii regulates carbohydrate metabolic functions of the gut microbiome in C57BL/6 mice

The probiotic impact of microbes on host metabolism and health depends on both host genetics and bacterial genomic variation. Faecalibacterium prausnitzii is the predominant human gut commensal emerging as a next-generation probiotic. Although this bacterium exhibits substantial intraspecies diversity, it is unclear whether genetically distinct F. prausnitzii strains might lead to functional differences in the gut microbiome. Here, we isolated and characterized a novel F. prausnitzii strain (UT1) that belongs to the most prevalent but underappreciated phylogenetic clade in the global human population. Genome analysis showed that this butyrate-producing isolate carries multiple putative mobile genetic elements, a clade-specific defense system, and a range of carbohydrate catabolic enzymes. Multiomic approaches were used to profile the impact of UT1 on the gut microbiome and associated metabolic activity of C57BL/6 mice at homeostasis. Both 16S rRNA and metagenomic sequencing demonstrated that oral administration of UT1 resulted in profound microbial compositional changes including a significant enrichment of Lactobacillus, Bifidobacterium, and Turicibacter. Functional profiling of the fecal metagenomes revealed a markedly higher abundance of carbohydrate-active enzymes (CAZymes) in UT1-gavaged mice. Accordingly, UT1-conditioned gut microbiota possessed the elevated capability of utilizing starch in vitro and exhibited a lower availability of microbiota-accessible carbohydrates in the feces. Further analysis uncovered a functional network wherein UT1 reduced the abundance of mucin-degrading CAZymes and microbes, which correlated with a concomitant reduction of mucin glycans in the gut. Collectively, our results reveal a crucial role of UT1 in facilitating the carbohydrate metabolism of the gut microbiome and expand our understanding of the genetic and phenotypic diversity of F. prausnitzii.

microbiology↗

bioRxiv2024

The fission yeast SUMO-targeted Ubiquitin Ligase Slx8 functionally associates with clustered centromeres and the silent mating type region at the nuclear periphery

The SUMO-targeted Ubiquitin ligase (STUbL) family is involved in multiple cellular processes via a wide range of mechanisms to maintain genome stability. One of the evolutionarily conserved functions of STUbL is to promote changes in the nuclear positioning of DNA lesions, targeting them to the nuclear periphery. In Schizossacharomyces pombe, the STUbL Slx8 is a regulator of SUMOylated proteins and promotes replication stress tolerance by counteracting the toxicity of SUMO conjugates. In order to study the dynamic dialectic between Ubiquitinylation and SUMOylation in the nuclear space of the S. pombe genome, we analyzed Slx8 localization. Unexpectedly, we did not detect replication stress-induced Slx8 foci. However, we discovered that Slx8 forms a single nuclear focus, enriched at the nuclear periphery, which marks both clustered centromeres at the spindle pole body and the silent mating type region. The formation of this single Slx8 focus requires the E3 SUMO ligase Pli1, poly-SUMOylation and the histone methyl transferase Clr4 that is responsible for the heterochromatin histone mark H3-K9 methylation. Finally, we established that Slx8 promotes centromere clustering and gene silencing at heterochromatin domains. Altogether, our data highlight evolutionarily conserved and functional relationships between STUbL and heterochromatin domains to promote gene silencing and nuclear organization. HighlightsO_LIThe S. pombe STUbL Slx8 forms a single nuclear focus enriched at the nuclear periphery in a SUMO-chain-dependent manner. C_LIO_LISlx8 foci mark clustered centromeres and the silenced mating type region but not telomeres. C_LIO_LIH3-K9 methylation by Crl4 promotes the single nuclear Slx8 focus C_LIO_LISlx8 promotes centromere clustering and gene silencing. C_LI

cell biology↗

bioRxiv2024

A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing

BackgroundSelecting highly variable features is a crucial step in most analysis pipelines of single-cell RNA-sequencing (scRNA-seq) data. Despite numerous methods proposed in recent years, a systematic understanding of the best solution is still lacking. ResultsHere, we systematically evaluate 47 highly variable gene (HVG) selection methods, consisting of 21 baseline methods developed based on different data transformations and mean-variance adjustment techniques and 26 hybrid methods developed based on mixtures of baseline methods. Across 19 diverse benchmark datasets, 18 objective evaluation criteria per method, and 5,358 analysis settings, we observe that no single baseline method consistently outperforms the others across all datasets and criteria. However, hybrid methods as a group robustly outperform individual baseline methods. Based on these findings, a new HVG selection approach, mixture HVG selection (mixHVG), that incorporates top-ranked features from multiple baseline methods is proposed as a better solution to HVG selection. An open source R package mixhvg is developed to enable convenient use of mixHVG and its integration into users data analysis pipelines. ConclusionOur benchmark study not only provides a systematic comparison of existing methods, leading to a better HVG selection solution, but also creates a pipeline and resource consisting of diverse benchmark data and criteria for evaluating new methods in the future.

bioinformatics↗

bioRxiv2024

Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT)

BackgroundMicrobiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. ResultsHere, we introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations. ConclusionsWe present QuanT, a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data. QuanTs distinct non-parametric method markedly enhances downstream analyses, serving as a valuable tool for data integration and comprehensive analysis in microbiome research.

bioinformatics↗

bioRxiv2024

Weighted variance component test for the integrative multi-omics analysis of microbiome data

Metabolic dysregulation and alterations have been linked to various diseases and conditions. Innovations in high-throughput technology now allow rapid profiling of the metabolome and metagenome -- often the gene content of bacterial populations -- for characterizing metabolism. Due to the small sample sizes and high dimensionality of the data, pathway analysis (wherein the effect of multiple genes or metabolites on an outcome is cumulatively assessed) of metabolomic data is commonly conducted and also represents a standard for metagenomic analysis. However, how to integrate both data types remains unclear. Recognizing that a metabolic pathway can be complementarily characterized by both metagenomics and metabolomics, we propose a weighted variance components framework to test if the joint effect of genes and metabolites in a biological pathway is associated with outcomes. The approach allows analytic p-value calculation, correlation between data types, and optimal weighting. Power simulations show that our approach often outperforms other strategies while maintaining type I error. The approach is illustrated on real data.

bioinformatics↗

bioRxiv2024

Intra-genomic genes-to-genes correlation enables genome representaion

The bacterial pan-genome consists of core genes shared by all members of a taxonomy and accessory genes found in only a subset. The correlation among genes within the pan-genome could advance our understanding of evolution and tackle medical challenges. Here, we discovered a strong intra-genomic correlation among bacterial pan-genes within each of Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, and Campylobacter jejuni. With a convolutional neural network assisted core genome knock-out simulation, we found that different combinations of fewer than 20 highly variable core genes could recover the sub-species type classified by complete core genome with accuracy >95%. This observation led us to test the genes-to-genes predictability: with more than 52,000 assemblies from each species, combinations of highly variable core genes could predict the sequence variants of other core genes (average accuracy >94%) within the same genome and could also predict sequence variants (average accuracy >91%) as well as the presence (average AUROC >0.91) of some accessory genes. Furthermore, combinations of highly variable core genes could also predict multiple antibiotic resistances (AUROC >0.80) in large published datasets of E. coli, S. aureus, and Mycobacterium tuberculosis. Collectively, we propose that genes within the same genome can strongly correlate with each other. Therefore, the strain phylogeny and the status of other genes could be uniformly represented by combinations of highly variable core genes, which could further represent certain phenotypes including in vitro resistance.

bioinformatics↗

bioRxiv2024

Adaptive loss of shortwave cone opsins during genomic evolution in cartilaginous fish

There is ample evidence of the loss of the shortwave visual protein gene in cartilaginous fish. However, the underlying logic and mechanisms by which organisms need to adapt to the environment behind the opsin loss are still unclear. Here, we report the assembly structure of the whole genome of Okamejei kenojei and Prionace glauca, identify TcMar difference between skates/rays and sharks/chimaeras, and analyze the distribution characteristics and intra group differentiation of opsin-related genes in cartilaginous fish. By establishing a zebrafish model with short wave opsin gene deletion, we find blue or violet light via short-wave sensitive opsins SWS1 or SWS2 can cause the photoreceptor layer thinning (the biomarker for age-related macular degeneration in human eye) through enactive the cell aging. The loss of sws is helpful for alleviating the short-wave light damage on the eye. Since the tapetum lucidum in the eye is found broad existence in various cartilaginous fish, a logical hypothesis was creatively proposed that the existence of tapetum lucidum on the retina of cartilaginous fish is interdependent with the loss of shortwave visual protein gene, providing a new perspective and explanatory path for further understanding of the evolution of visual genes in cartilaginous fish.

bioinformatics↗

bioRxiv2024

Predicting pain genes: multi-modal data integration using probabilistic classifiers and interaction networks

Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible "pain genes". Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics (eg. transcriptomics, proteomics, etc.), protein-protein interaction (PPI) network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a "pain score" per gene. The top ranked genes were then validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of "pain" genes. As such, a PPI network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research.

bioinformatics↗

bioRxiv2024

CBP/P300 BRD Inhibition Reduces Neutrophil Accumulation and Activates Antitumor Immunity in TNBC

Tumor-associated neutrophils (TANs) have been shown to promote immunosuppression and tumor progression, and a high TAN frequency predicts poor prognosis in triple-negative breast cancer (TNBC). Dysregulation of CREB binding protein (CBP)/P300 function has been observed with multiple cancer types. The bromodomain (BRD) of CBP/P300 has been shown to regulate its activity. In this study, we found that IACS-70654, a novel and selective CBP/P300 BRD inhibitor, reduced TANs and inhibited the growth of neutrophil-enriched TNBC models. In the bone marrow, CBP/P300 BRD inhibition reduced the tumor-driven abnormal differentiation and proliferation of neutrophil progenitors. Inhibition of CBP/P300 BRD also stimulated the immune response by inducing an IFN response and MHCI expression in tumor cells and increasing tumor-infiltrated CTLs. Moreover, IACS-70654 improved the response of a neutrophil-enriched TNBC model to docetaxel and immune checkpoint blockade. This provides a rationale for combining a CBP/P300 BRD inhibitor with standard-of-care therapies in future clinical trials for neutrophil-enriched TNBC. SummaryIn neutrophil-enriched triple-negative breast cancer (TNBC) models, CREB binding protein (CBP)/P300 bromodomain (BRD) inhibition reduces tumor growth and systemic neutrophil accumulation while stimulating an antitumor immune response. This improves standard-of-care therapies, suggesting a potential therapeutic benefit of CBP/P300 BRD inhibitors for neutrophil-enriched TNBC.

cancer biology↗

bioRxiv2024

PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.

molecular biology↗

bioRxiv2024

A Mixed Effect Similarity Matrix Regression Model (SMRmix) for Integrating Multiple Microbiome Datasets at Community Level and its Application in HIV

BackgroundRecent studies have highlighted the importance of human microbiota in our health and diseases. However, in many areas of research, individual microbiome studies often offer inconsistent results due to the limited sample sizes and the heterogeneity in study populations and experimental procedures. This inconsistency underscores the necessity for integrative analysis of multiple microbiome datasets. Despite the critical need, statistical methods that incorporate multiple microbiome datasets and account for the study heterogeneity are not available in the literature. MethodsIn this paper, we develop a mixed effect similarity matrix regression (SMRmix) approach for identifying community level microbiome shifts between outcomes. SMRmix has a close connection with the microbiome kernel association test, one of the most popular approaches for such a task but is only applicable when we have a single study. SMRmix enables researchers to consolidate findings from diverse microbiome studies. ResultsVia extensive simulations, we show that SMRmix has well-controlled type I error and higher power than some potential competitors. We applied the SMRmix to two real-world datasets. The first, from the HIV-reanalysis consortium, integrated data from 17 studies on gut dysbiosis in HIV. Our analysis confirmed consistent associations between the gut microbiome and HIV infection as well as MSM (men who have sex with men) status, demonstrating greater power than competing methods. The second dataset involved 11 studies on the gut microbiome in colorectal cancer; analysis with SMRmix confirmed significant dysbiosis in affected individuals compared to healthy controls. ConclusionThe development of SMRmix enables the integration of multiple studies and effectively managing study heterogeneity, and provides a powerful tool for uncovering consistent associations between diseases and community-level microbiome data.

bioinformatics↗

bioRxiv2024

Neuronal lysosome transfer to oligodendrocyte precursor cells: a novel mechanism of neuron-glia communication and its role in neurodegenerative disease

Oligodendrocyte precursor cells (OPCs) shape brain function through intricate regulatory mechanisms. Here, we observed that OPC processes establish connections with neuronal somata, with smaller lysosomes positioned near these contact sites. Tracking lysosomes demonstrated neuronal lysosomes were attracted to and released at these contact points, eventually becoming incorporated into OPC processes, suggesting a selective, OPC-evoked release of lysosomes from neuronal soma and their ingestion by OPCs, highlighting a unique lysosome-mediated communication between neurons and OPCs. Diminished branching of OPC processes resulted in fewer neuron-OPC contacts, fostering larger lysosome accumulation in neurons, altered neuronal activity and escalated prevalence of senescent neurons during aging. A similar reduction in OPC branching and neuronal lysosome accumulation was evident in an early-stage Alzheimers disease mouse model. Together, these findings underscore the pivotal role of OPC processes in modulating neuronal activity through direct somatic contact and lysosome ingestion, presenting a prospective therapeutic avenue for addressing neurodegenerative diseases.

neuroscience↗

bioRxiv2024

Simulating Colloid Motion in Spinning Suspension: Internal Dynamics in Orbital Shaker

Colloid suspensions in the form of mammalian or bacterial cell mixtures in orbital shakers are commonly encountered in biomedical research. An understanding of particle motion in these conditions would provide insight into bulk colloid behavior and distribution under short and long timeframes. Such data can be used to supplement and clarify existing concepts of biological phenomena encountered in the laboratory setting. It can also aid biomedical researchers in experiment design and data interpretation. We present a MATLAB based simulation of colloid motion under rotary agitation. Our simulation setup is modular and therefore designed to act as a scaffold that can be customized to simulate different mammalian, bacterial, or particle properties in liquid suspension.

biophysics↗

bioRxiv2023

C5aR Expression in Kidney Tubules, Macrophages and Fibrosis

The anaphylatoxin C5a and its receptor C5aR (CD88) are complement pathway effectors implicated in renal diseases, including ANCA-associated vasculitis. We investigated the kidney expression of C5aR and a second C5a receptor C5L2 by using immunohistochemistry and in situ hybridization on formalin-fixed, paraffin-embedded human and mouse kidney. C5aR was detected on interstitial macrophages and in multiple tubular regions, both distal and proximal; C5L2 had a similar expression pattern. The 5/6 nephrectomy model of chronic kidney injury exhibited increased C5aR expression by infiltrating cells within the fibrotic regions. Functional assessment of myeloid C5aR in vitro revealed that C5a induced the expression of chemokines and remodeling factors by macrophages, including CCL-3/-4/-7,-20, MMP-1/-3/-8/-12, and F3, and promoted survival by blocking neutrophil apoptosis. C5a activity was C5aR dependent, as demonstrated by reversal with the C5aR inhibitor avacopan. Collectively, these results suggest that myeloid C5aR may induce excessive inflammation in the kidney via immune cell recruitment, extracellular matrix destruction, and remodeling, resulting in fibrotic tissue deposition.

immunology↗

bioRxiv2023

Hypoxia drives HIF2-dependent macrophage cell cycle entry and susceptibility to lentivira transduction

Macrophages play critical roles across health and disease. Low oxygen conditions (hypoxia) have been associated primarily with cell cycle arrest in cultured dividing cells. Macrophages are typically quiescent in G0, though yolk sac and bone marrow derived macrophages frequently proliferate and monocyte-derived tissue macrophages are able to proliferate in response to tissue signals. Here we show that hypoxia (1% oxygen tension) results in reversible entry into the cell cycle in monocyte derived macrophages (MDM) and mouse peritoneal macrophages. Cell cycle progression is largely limited to G1/S phase with very little progression to G2/M. Mechanistically, this cell cycle transitioning is triggered by a HIF2-directed transcriptional program. The response is accompanied by increased expression of cell cycle-associated proteins, including CDK1, and reversible activation of the canonical mitogen-activated MEK-ERK proliferation pathway. CDK1 associated SAMHD1 phosphorylation at T592 in hypoxic macrophages renders them hyper-susceptible to lentiviral transduction. Furthermore, PHD inhibitors, which activate HIFs, are able to recapitulate HIF2-dependent cell cycle entry in macrophages, as well as susceptibility to lentiviral transduction. Finally, we demonstrate that tumour associated macrophages (TAM) in lung cancers exhibit transcriptomic profiles representing responses to low oxygen and cell cycle progression at single cell level. This work uncovers HIF2 driven macrophage cell cycle progression in low oxygen conditions that culminates in SAMHD1 phosphorylation and high susceptibility to lentiviral transduction. These findings have implications for inflammation, neoplasia and pathogen defence.

cell biology↗

bioRxiv2023

Genetically encoded affinity reagents (GEARs): A toolkit for visualizing and manipulating endogenous protein function in vivo.

Probing endogenous protein localization and function in vivo remains challenging due to laborious gene targeting and monofunctional alleles. Here, we develop a multifunctional, universal, and adaptable toolkit based on genetically encoded affinity reagents (GEARs). GEARs use nanobodies and single chain variable fragments (scFv), which recognize small epitopes, enabling fluorescent visualization and selective degradation of protein targets in vivo. Furthermore, we delineate a CRISPR/Cas9-based epitope tagging pipeline to demonstrate its utility for producing knock-in alleles that have broad multifunctionality. We use GEARs to examine the native behaviour of the pioneer transcription factor Nanog and the planar cell polarity protein Vangl2 during early zebrafish development. Together, this toolkit provides a versatile system for probing and perturbing endogenous protein function while circumventing challenges associated with conventional gene targeting and is broadly available to the model organism community.

developmental biology↗