Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 235 records · Page 13Linked to original sources

bioRxiv2024

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

Characterizing cell activities within a spatially resolved context is essential to enhance our understanding of spatially-induced cellular states and features. While single-cell RNA-seq (scRNA-seq) offers comprehensive profiling of cells within a tissue, it fails to capture spatial context. Conversely, subcellular spatial transcriptomics (SST) technologies provide high-resolution spatial profiles of gene expression, yet their utility is constrained by the limited number of genes they can simultaneously profile. To address this limitation, we introduce VISTA, a novel approach designed to predict the expression levels of unobserved genes specifically tailored for SST data. VISTA jointly models scRNA-seq data and SST data based on variational inference and geometric deep learning, and incorporates uncertainty quantification. Using four SST datasets, we demonstrate VISTAs superior performance in imputation and in analyzing large-scale SST datasets with satisfactory time efficiency and memory consumption. The imputation of VISTA enables a multitude of downstream applications, including the detection of new spatially variable genes, the discovery of novel ligand-receptor interactions, the inference of spatial RNA velocity, the generation for spatial transcriptomics with in-silico perturbation, and an improved decomposition of spatial and intrinsic variations.

bioinformatics↗

bioRxiv2024

Foraging niche partitioning of three Myotis bat species and marine fish consumption by Myotis pilosus in a subtropical East Asian region

Most bats are insectivorous, but some species have evolved the ability to prey on fish. Although piscivory has been confirmed in the Ricketts big-footed myotis (Myotis pilosus), the extent of piscivory of other cohabiting Myotis species is uncertain. This study aims to explore the dietary niches and fish consumption of three Myotis species in a subtropical East Asian region, and specifically the fish diet of M. pilosus. Our findings reveal, for the first time, that M. pilosus consumes marine fishes, in contrast to previous research conducted in inland regions that suggested year-round consumption of cyprinids in freshwater habitats. We also observed seasonal variation in the diets of M. pilosus. It predominately hunted wide-banded hardyhead silverside, sailfin flying fish, and shorthead anchovy during the wet season, while mainly preying upon mullets during the dry months. In more inland areas, M. pilosus was found to primarily feed on invasive freshwater poeciliids. Furthermore, M. pilosus consumed more fish during the dry season, while there was a greater consumption of insects during the wet months. Most notably among our findings is the consumption of fish by two individuals of Horsfields myotis (M. horsfieldii), indicating that the species is potentially piscivorous. We revealed that both M. horsfieldii and M. pilosus consumed water striders, suggesting that foraging of aquatic insects could be driving the evolution of fishing behavior. Our findings have also shed light on the flexibility of foraging behavior in piscivorous bats.

ecology↗

bioRxiv2024

Berberine is a novel mitochondrial calcium uniporter (MCU) inhibitor that disrupts MCU-EMRE assembly

The mitochondrial calcium uniporter (MCU) complex mediates Ca2+ entry into mitochondrial, which plays a crucial role in regulating cellular energy metabolism and apoptosis. Dysregulation of MCU is implicated in various diseases, such as neurodegenerative disorders, cardiac diseases and cancer. Despite its importance, developing specific and clinically viable MCU inhibitors has been challenging. Here, we identify Berberine, a well-established drug with a documented safety profile, as a potent MCU inhibitor through a virtual screening of an FDA-approved drug library. Berberine localizes within mitochondria and directly binds to the juxtamembrane loop domain of MCU. This binding disrupts the interaction of MCU with its essential regulator, EMRE, thereby inhibiting rapid Ca2+ entry into the mitochondria. Notably, Berberine pretreatment reduces mitochondrial Ca2+ overload and mitigate ischemia/reperfusion-induced myocardial injury in mice. Our findings establish Berberine as a potent MCU inhibitor, offering a safe therapeutic strategy for diseases associated with dysregulated mitochondrial calcium homeostasis.

cell biology↗

bioRxiv2024

Potent LILRB1 D1D2-containing antibodies inhibit RIFIN-mediated immune evasions

The spread of drug-resistant malaria parasites presents a major challenge to global efforts in malaria control, increasing the urgency for new treatments and vaccines. A promising approach involves developing antibodies that can counteract the parasites immune evasion mechanisms. In this study, we designed a receptor-containing antibody, targeting the D1D2 domain of the LILRB1 receptor, using a structure-based rational approach. We began with the MDB1 antibody as a scaffold and replaced the LILRB1-D3D4 insertion domain with D1D2.v, a high-affinity variant optimized through yeast surface display. The modified D1D2.v-IgG efficiently blocked the interaction between RIFIN#1 (from PF3D7_1254800) and LILRB1, thereby reversing the inhibition of NK cell activity caused by RIFIN#1. To further enhance this effect, we developed NK-biAb, a bispecific antibody based on D1D2.v-IgG that targets both RIFIN#1 and NKG2D receptors. NK-biAb exhibited superior biological performance compared to D1D2.v-IgG alone. These findings provide a clear framework for designing antibodies that target immune evasion in malaria, potentially guiding the development of more effective treatments and vaccines.

biochemistry↗

bioRxiv2024

Random Sanitization in DNA information storage using CRISPR-Cas12a

DNA information storage provides an excellent solution for metadata storage due to its high density, programmability, and long-term stability. However, current research in DNA storage primarily focuses on the processes of storing and reading data, lacking comprehensive solutions for the secure metadata wiping. Herein, we present a method of random sanitization in DNA information storage using CRISPR-Cas12a (RSDISC) based on precise control of the thermodynamic energy of primer-template hybridization. We utilize the collateral cleavage (trans-activity) of single-stranded DNA (ssDNA) by CRISPR-Cas12a to achieve selective sanitization of files in metadata. This method enables ssDNA degradation with different GC content, lengths, and secondary structures to achieve a sanitization efficiency up to 99.9% for 28,258 oligonucleotides in DNA storage within one round. We demonstrate that the number of erasable files could reach 1011.7 based on a model of primer-template hybridization efficiency. Overall, RSDISC provides a random sanitization approach to set the foundation of information encryption, file classification, memory deallocation and accurate reading in DNA data storage.

synthetic biology↗

bioRxiv2024

Scarless circular mRNA-based CAR-T cell therapy elicits superior anti-tumor efficacy

Messenger RNA (mRNA)-based transient expression of CAR shows optimal safety profiles and provides promising opportunities to address existing challenges of viral vector-based CAR-T therapies and to meet emerging medical needs in noncancerous indications. However, linear mRNAs are intrinsically unstable and thus just achieve compromised efficacy. Here, we engineered a permuted intron exon (PIE) platform to synthesize scarless circular mRNA (cmRNA) for potent CAR expression and long-lasting efficacy. cmRNA significantly increased amount and duration of anti-CD19 CAR expression on human T cells. cmRNA-based anti-CD19 CAR-T cells elicit superior anti-tumor efficacy over linear mRNA counterparts, demonstrated by parallel lines of evidence including in vitro specific cell-killing, cytokine release, transcriptomics patterns, and in vivo tumor elimination and survival benefit. We found that cmRNA-based anti-CD19 CAR-T efficiently eliminated target cells in vivo and provide long-lasting antitumor efficacy. These results suggested that cmRNA could be a potent platform for unleashing full potential of mRNA technologies in cell therapies.

immunology↗

bioRxiv2024

Neural representational precision of distance predicts arithmetic performance

Focusing on the distance between magnitudes as the start point to investigate the mechanism of relationship detecting and its contribution to mathematical thinking, this study explores the precision of neural representations of numerical distance and their impact on arithmetic performance. By employing neural decoding techniques and representational similarity analysis, the present study investigates how accurately the brain represents numerical distances and how this precision relates to arithmetic skills. Thirty-two children participated, completing a dot number comparison task during fMRI scanning and an arithmetic fluency test. Results indicated that neural activation patterns in the intra-parietal sulcus decoded the distance between the presented pair of dots, and higher precision in neural distance representation correlates with better arithmetic performance. These findings suggest that the accuracy of neural decoding can serve as an index of the neural representation precision and that the ability to precisely encode numerical distances in the brain is a key factor in mathematical abilities. This provides new insights into the neural basis of mathematical cognition and learning. HighlightsO_LIUtilizing representational similarity analysis and neural decoding techniques, the research proposes that the accuracy of neural decoding serve as an index of neural representation precision. C_LIO_LIThe precision of neural representation of numerical distances in the brain predicts task performance and math arithmetic proficiency in children. C_LIO_LIThese findings imply the potential significance of relational information in general cognition and learning beyond mathematical learning. C_LI

neuroscience↗

bioRxiv2024

Extensive mutual influences of SMC complexes shape 3D genome folding

Mammalian genomes are folded by the distinct actions of SMC complexes which include the chromatin loop-extruding cohesin, the sister-chromatid cohesive cohesin, and the mitotic chromosome-associated condensins. While these complexes function at different stages of the cell cycle, they co-exist on chromatin during the G2/M-phase transition, when genome structure undergoes a dramatic reorganization. Yet, how distinct SMC complexes affect each other and how their mutual interplay orchestrates the dynamic folding of 3D genome remains elusive. Here, we engineered all possible cohesin/condensin configurations on mitotic chromosomes to delineate the concerted, mutual influential action of SMC complexes. We find that: (i) The mitotic SMC complex condensin disrupts the focal accumulation of extrusive-cohesin at CTCF binding sites, thereby promoting the disassembly of interphase TADs and chromatin loops during mitotic progression. Conversely, extrusive-cohesin can impair condensin activity and alter mitotic chromosome helicity. (ii) Condensin diminishes cohesive-cohesin focal enrichment and, conversely, cohesive-cohesin can counteract condensin function and impede mitotic chromosome longitudinal shortening. (iii) The co-presence of extrusive- and cohesive-cohesin synergistically antagonizes condensin function and dramatically delays mitotic chromosome condensation. (iv) Extrusive-cohesin positions cohesive-cohesin at CTCF binding sites. However, cohesive-cohesin by itself is insufficient to mediate the formation of TADs or chromatin loop, implying non-overlapping function with extrusive-cohesin. Instead, cohesive-cohesin restricts chromatin loop expansion, potentially by limiting extrusive-cohesin movement. Collectively, our data describe a comprehensive three-way interplay among major SMC complexes that dynamically sculpts chromatin architecture during cell cycle progression.

cell biology↗

bioRxiv2024

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

neuroscience↗

bioRxiv2024

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual genes expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

bioinformatics↗

bioRxiv2024

Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine. One Sentence SummaryThis study establishes patient-derived tumor organoids (PDOs) from 31 Head and Neck Squamous Cell Carcinoma (HNSCC) patients, faithfully recapitulating characteristics of primary tumors and accurately predicting clinical responses to cisplatin treatment. We reveal intertumoral heterogeneity within PDOs and a hybrid epithelial-mesenchymal transition (hEMT) program conferring cisplatin resistance, highlighting amphiregulin (AREG) as a regulator of cellular plasticity and potential therapeutic target for HNSCC treatment.

cancer biology↗

bioRxiv2024

The m6A reader IGF2BP3 promotes gastric cancer progression via increasing the expression of FBXO32 protein

N6-methyladenosine (m6A) represents the most prevalent chemical modification on eukaryotic mRNA, with an accumulating body of literature indicating its pivotal significance in the pathogenesis of human cancers. Nevertheless, the precise molecular interplay between the m6A reader protein IGF2BP3 and gastric cancer remains to be thoroughly delineated. Our study uncovered that the expression of IGF2BP3 in gastric cancer tissues is markedly elevated in comparison to adjacent normal tissues, and this upregulation is tightly correlated with the incidence of lymph node metastasis, more advanced TNM stages, and deeper invasion depth of tumor in patients. In vitro experiments demonstrated that IGF2BP3 potentiates the proliferative, migratory, and invasive capacities of gastric cancer cells, while concurrently inhibiting apoptosis and augmenting the intracellular levels of aerobic glycolysis. In vivo experiments revealed that IGF2BP3 contributes to the growth of gastric cancer. Mechanistically, IGF2BP3 can increase the expression of FBXO32 protein by recognizing and binding to the m6A binding site on FBXO32 mRNA and further activate the downstream cGMP-PKG signaling pathway, thereby modulating various biological functions of gastric cancer cells and ultimately promoting the progression of gastric cancer. In summary, our findings suggest that IGF2BP3 upregulates the expression of FBXO32 protein in an m6A dependent manner and subsequently activates the cGMP-PKG signaling pathway, ultimately leading to the onset and progression of gastric cancer. Consequently, the targeting of the IGF2BP3/FBXO32/cGMP-PKG axis emerges as a promising therapeutic modality for the treatment of gastric cancer.

cancer biology↗

bioRxiv2024

Chemically induced cell plasticity enables the generation of high-fidelity embryo model

Embryo models open exciting opportunities for improving our understanding of development and advancing medicine. However, current models depend on intricate procedures and the integration of separately generated cell types. Here, we aimed to establish a chemical-only strategy to induce cellular plasticity and generate embryo founder-like cells. Within 60 hours, chemical-induced pluripotent stem cells activated early lineage specifiers, Cdx2 and Gata6, while maintaining Oct4/ Pou5f1 expression. Following a second 36-hour chemical treatment, founder-like cells developed into all blastocyst fates and self-assembled into embryo-like structures. These progressed into high-fidelity late-streak gastrulation stages within 4.5 days, displaying primitive streak formation via epithelial-to-mesenchymal transition, along with germ-layer tissues, amnion, and ectoplacental cone. Fate trajectories and molecular signatures from founder-like cells to chemically-induced embryo model (ci-MO) were defined, revealing remarkable similarity up to Embryonic day (E) 7.5 embryo. Summarily, ci-MO offers a straightforward, rapid, small-molecule-only, and high-fidelity approach to study embryonic development and cell fate specification. HighlightsO_LIGeneration of a direct, efficient, and sophisticated chemically induced embryo model (ci-MO) C_LIO_LITwo-pulse chemical induction reprograms ESCs into founder-like cells capable of developing all embryo fates C_LIO_LIGastrulating ci-MOs display high morphological and molecular accuracy C_LIO_LIci-MOs reconstruct embryo developmental trajectories C_LI

cell biology↗

bioRxiv2024

Embracing Uncertainty Reshapes the ETHYLENE INSENSITIVE2-Activated Ethylene Signaling Framework

Signal transduction of the gaseous plant hormone ethylene by ETHYLENE INSENSITIVE2 (EIN2) is proposed to be regulated at multiple levels. EIN2 is phosphorylated by CONSTITUTIVE TRIPLE-RESPONSE1 (CTR1), subject to the F-Box proteins [EIN2-TARGETING PROTEIN1 (ETP1) and ETP2]-mediated degradation, and cannot activate ethylene signaling. Ethylene prevents EIN2 phosphorylation and degradation, and EIN2 accumulates and activates the signaling. MAOHUZI3 LIKE1 (MHL1) and MHL2 stabilize EIN2, and the mhl1 mhl2 double mutation confers ethylene insensitivity. Here, we reported that the regulation of EIN2-activated ethylene signaling can be independent of CTR1-mediated phosphorylation, ETP1/ETP2-directed degradation, and MHL1/MHL2-dependent stabilization. Both the etp1 etp2 double mutant and wild type showed identical ethylene dose-response curves, and the nontreated mhl1 mhl2 double mutant displayed substantial growth inhibition. The reported ethylene-insensitive root phenotype of mhl1 mhl2 seedlings requires sucrose and is associated with unknown loci. The ethylene receptor ETHYLENE RESPONSE1 (ETR1) and EIN2 interact at the rough endoplasmic reticulum. We propose that the docking of CTR1 to ETR1 promotes receptor signaling, which inhibits EIN2-activated ethylene signaling. The discrepancy between our findings and the current model is discussed. Our findings may disrupt the knowledge boundary of the present molecular model, developing a niche for findings outside the framework to advance our understanding of ethylene signaling.

plant biology↗

bioRxiv2024

Comprehensive single cell transcriptomics analysis of murine osteosarcoma uncovers Skp2 function in metastasis, genomic instability and immune activation and reveals additional target pathways

Osteosarcoma (OS) is the most common primary pediatric bone malignancy. One promising new therapeutic target is SKP2, encoding a substrate recognition factor of the SCF E3 ubiquitin ligase responsible for ubiquitination and proteasome degradation of substrate p27, thus driving cellular proliferation. We have shown previously that knockout of Skp2 in an immunocompetent transgenic mouse model of OS improved survival, drove apoptosis, and induced tumor inflammation. Here, we applied single-cell RNA-sequencing (scRNA-seq) to study primary OS tumors derived from Osx-Cre driven conditional knockout of Rb1 and Trp53. We showed that murine OS models recapitulate the tumor heterogeneity and microenvironment complexity observed in patient tumors. We further compared this model with OS models with functional disruption of Skp2: one with Skp2 knockout and the other with the Skp2-p27 interaction disrupted (resulting in p27 overexpression). We found reduction of T cell exhaustion and upregulation of interferon activation, along with evidence of replicative and endoplasmic reticulum-related stress in the Skp2 disruption models, and showed that interferon induction was correlated with improved survival in OS patients. Additionally, our scRNA-seq analysis uncovered decreased activities of metastasis-related gene signatures in the Skp2-disrupted OS, which we validated by observation of a strong reduction in lung metastasis in the Skp2 knockout mice. Finally, we report several potential mechanisms of escape from targeting Skp2 in OS, including upregulation of Myc targets, DNA copy number amplification and overexpression of alternative E3 ligase genes, and potential alternative lineage activation. These mechanistic insights into OS tumor biology and Skp2 function suggest novel targets for new, synergistic therapies, while the data and our comprehensive analysis may serve as a public resource for further big data-driven OS research.

cancer biology↗

bioRxiv2024

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

The rapid evolution of single-cell sequencing technologies has facilitated precise transcriptomics profiling at the single-cell level, shedding light on the intricate heterogeneity within cellular populations. Despite these advances, the inherent diversity of cells and data challenges such as noise, batch effects, and sparsity, underscores the pressing need for a unified model to learn and represent cellular states effectively. Single-cell Large Language Models (LLMs) have been crafted to bridge this gap yet exhibit limited performance on human cells. This short-fall may stem from the confounding effects of training data from diverse species, partly because of limited cells for the single species. Here, we have compiled a dataset of approximately 100 million human cells sequenced by multiple technolo-gies from human single-cell datasets with various file types deposited in public databases and websites. Leveraging these extensive data cohorts, we developed CellFM, a robust single-cell foundation model with an impressive 800 million parameters, marking an eight-fold increase over the current largest single-species model. To ensure the training of CellFM on the MindSpore AI framework from Huawei, we have integrated RetNet, a Transformer architecture variant with lin-ear complexity for a balance between efficiency and performance, serving as the backbone of our model. Our comprehensive experiments have shown that CellFM outperforms existing models across diverse applications, such as cell annotation, perturbation prediction, and gene function prediction.

bioinformatics↗

bioRxiv2024

ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation

Structure-based molecule generation represents a significant advancement in AI-aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of structural data on protein-ligand complexes, a challenge we term the Paradox of Sparse Chemical Space Generation. To address this limitation, we propose a novel latent variable approach that bridges the data gap between ligand-only and protein-ligand complexes, enabling the target-aware generative models to explore a broader chemical space and enhancing the quality of molecular generation. Drawing inspiration from quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables--an innovative integration of physical principles into deep learning frameworks. ECloudGen incorporates modern techniques, including latent diffusion models, Llama architectures, and a newly proposed contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a significantly broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in a case study designing V2R inhibitors. Furthermore, ECloudGens structurally ordered modeling of chemical space enables the development of a model-agnostic optimizer, extending its utility to molecular optimization tasks. This capability has been validated through a single-objective oracle benchmark and a complex multi-objective optimization scenario involving the redesign of endogenous BRD4 ligands. In conclusion, ECloudGen effectively addresses the Paradox of Sparse Chemical Space Generation through its integration of theoretical insights, advanced generative techniques, and real-world validation. The newly proposed technique of leveraging physical entities (such as electron clouds) as latent variables within a deep learning framework may prove useful for computational biology fields beyond AIDD.

bioinformatics↗

bioRxiv2024

Genomic and Genetic Insights into Mendel's Pea Genes

Pea, Pisum sativum, is an excellent model system through which Gregor Mendel established the foundational principles of inheritance. Surprisingly, till today, the molecular nature of the genetic differences underlying the seven pairs of contrasting traits that Mendel studied in detail remains partially understood. Here, we present a genomic and phenotypic variation map, coupled with haplotype-phenotype association analyses across a wide range of traits in a global Pisum diversity panel. We focus on a genomics-enabled genetic dissection of each of the seven traits Mendel studied, revealing many previously undescribed alleles for the four characterized genes, R, Le, I and A, and elucidating the gene identities and mutations for the remaining three uncharacterized traits. Notably, we identify: (1) a ca. 100kb deletion upstream of the Chlorophyll synthase (ChlG) gene, which generates aberrant transcripts and confers the yellow pod phenotype of gp mutants; (2) an in-frame premature stop codon mutation in a Dodeca-CLE41/44 signalling peptide which explains the parchmentless mutant phenotype corresponding to p; and (3) a 5bp in-frame deletion in a CIK-like receptor kinase gene corresponding to the fasciated stem phenotype fa, which Mendel described in terms of flower position, and we postulate the existence of a Modifier of fa (Mfa) locus that masks this meristem defect. Mendel noted the pleiotropy of the a mutation, including inhibition of axil ring anthocyanin pigmentation, a trait we found to be controlled by allelic variants of the gene D within an R2R3-MYB gene cluster. Furthermore, we characterize and validate natural variation of a quantitative genetic locus governing both pod width and seed weight, characters that Mendel deemed were not sufficiently demarcated for his analyses. This study establishes a cornerstone for fundamental research, education in biology and genetics, and pea breeding practices.

genetics↗