Biology subjects

Zheng, Z.

Publications and source records attributed to Zheng, Z..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Drug Response Modeling across Cancers: Proteomics vs. Transcriptomics

Cancer cell lines are the most common in-vitro models for the evaluation of anti-cancer drug sensitivities. Past studies have been conducted to decipher and characterize the pharmacogenomic feature of cell lines based on other omics data, such as genomic mutation data and whole-genome RNA sequencing (RNA-seq) profiles. In particular, proteomic data is also an essential component for the characterization of tumours. However, different from RNA-seq datasets rich in numerous transcriptome profiles of cancer cell lines and cell viability assay of drug responses, the pharmacogenomic protein quantifications are relatively scarce. With the availability of the recently enriched proteomic dataset ProCan-DepMapSanger, we systematically evaluated the interplays among genomic mutations, transcription, and protein expressions across cancer cell lines. In general, blood cancers have higher RNA-protein correlations than those in solid cancers. The differential expression analysis on protein data helped identify more expressional and functional impact of genomic mutations of cancer genes. We also integrated the proteomic map with drug molecular chemical features to construct a bi-modal machine learning model to infer the drug sensitivities of cancer cell lines. Our results demonstrated that protein quantifications can lead to better drug response prediction performance than the model trained on transcriptome profiles. In addition, integrating protein data with drug chemical features, represented as molecular graphs and learned by Graph Neural Network, outperformed the state-of-the-art model DeepOmicNet for drug response prediction in proteomics.

bioinformatics↗

bioRxiv2024

State-specific Peptide Design Targeting G Protein-coupled Receptors

G protein-coupled receptors (GPCRs) play critical roles in various physiological processes and serve as pivotal targets for drug discovery. Peptides are particularly compelling therapeutic agents for targeting GPCRs, as they frequently exhibit superior affinity, selectivity, and potency compared to small-molecule drugs. However, the states (active and inactive) of GPCRs profoundly influence their interactions with ligands, highlighting the need for state-specific strategies in peptide design. To address this, we developed an efficient state-specific peptide design pipeline targeting GPCRs. This framework enables the tailored design of agonists or antagonists based on the active or inactive conformational states of GPCR structures. A state-specific folding model, optimized for GPCR-peptide complexes, was used to select high-potential peptides from the pool of designed candidates. Using this approach, we successfully identified both agonist and antagonist peptides for the Apelin Receptor (APJR) and the Growth Hormone Secretagogue Receptor (GHSR), as well as a competitive inhibitor for the Glucagon-Like Peptide-1 Receptor (GLP-1R).

bioinformatics↗

bioRxiv2024

Reconstitution of SPO11-dependent double-strand break formation

Homologous meiotic recombination starts with DNA double-strand breaks (DSBs) generated by SPO11 protein1. SPO11 is critical for meiosis in most species but the DSBs it makes are also dangerous because of their mutagenic2 and gametocidal3 potential, so cells must foster SPO11s beneficial functions while minimizing its risks4. SPO11 mechanism and regulation remain poorly understood. Here we report reconstitution of DNA cleavage in vitro with purified recombinant mouse SPO11 bound to its essential partner TOP6BL. Similar to their yeast orthologs5,6, SPO11- TOP6BL complexes are monomeric (1:1) in solution and bind tightly to DNA. Unlike in yeast, however, dimeric (2:2) assemblies of mouse SPO11-TOP6BL cleave DNA to form covalent 5' attachments requiring SPO11 active site residues, divalent metal ions, and SPO11 dimerization. Surprisingly, SPO11 can also manifest topoisomerase activity by relaxing supercoils and resealing DNA that it has nicked. Structure modeling with AlphaFold37 illuminates the protein-DNA interface and suggests that DNA is bent prior to cleavage. Deep sequencing of in vitro cleavage products reveals a rotationally symmetric base composition bias that partially explains DSB site preferences in vivo. Cleavage is inefficient on complex DNA substrates, partly because SPO11 is readily trapped in DSB-incompetent (presumably monomeric) binding states that exchange slowly. However, cleavage is improved by using substrates that favor DSB-competent dimer assembly, or by fusing SPO11 to an artificial dimerization module. Our results inform a model in which intrinsically feeble dimerization restrains SPO11 activity in vivo, making it exquisitely dependent on accessory proteins that focus and control DSB formation so that it happens only at the right time and the right places.

biochemistry↗

bioRxiv2024

Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a [~]91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a [~]92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a [~]95% and [~]96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached [~]97% for ONT and [~]98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

bioinformatics↗

bioRxiv2024

Spotiphy enables single-cell spatial whole transcriptomics across the entire section

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections1, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage2,3. Here we present Spotiphy (Spot imager with pseudo single-cell resolution histology), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimers disease (AD) and normal mouse brains, Spotiphy delivers the most precise cellular compositions. For the first time, Spotiphy reveals novel astrocyte regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.

systems biology↗

bioRxiv2024

Epigenetic constraint of cellular genomes evolutionarily links genetic variation to function

Cellular diversity is a product of evolution acting to drive divergent regulatory programs from a common genome. Here, we use cross-cell-type epigenetic conservation to gain insight into the impact of selective constraints on genome function and phenotypic variation. By comparing chromatin accessibility across hundreds of diverse cell-types, we identify 1.4% of the human genome safeguarded by conserved domains of facultative heterochromatin, which we term regions under "cellular constraint". We calculate single-base resolution cellular constraint scores and demonstrate robust prediction of functionally important coding and non-coding loci in a cell-type-, trait-, and disease-agnostic manner. Cellular constraint annotation enhances causal variant identification, drug discovery, and clinical diagnostic predictions. Furthermore, cell-constrained sequences share paradoxical evolutionary signals of positive and negative selection, suggesting a dynamic role in driving human adaptation. Overall, this study demonstrates that evolutionary chromatin dynamics can be leveraged to inform the translation of genetic discoveries into effective biological, therapeutic, and clinical outcomes.

genetics↗

bioRxiv2024

Does Unfairness Evoke Anger or Disgust? A Quantitative Neurofunctional Dissection Based on 25 Years of Neuroimaging

Over the last decades, the traditional Homo economicus model has been increasingly challenged by convergent evidence underscoring the impact of emotions on decision-making. A classic example is the perception of unfairness operationalized in the Ultimatum Game where humans readily sacrifice personal gains to punish those who violate fairness norms. While the emotional mechanism underlying costly punishments has been widely acknowledged, the distinct contributions of moral emotions (anger or disgust) remain debated, partly due to methodological limitations of the conventional experiments. Here, we capitalize on a quantitative neurofunctional dissection approach by combining recent developments in neuroimaging meta-analyses, behavioral-level, network-level, and neurochemical-level decoding and data from 3,266 participants from functional neuroimaging studies to determine the common and distinct neural representations between unfairness and the two moral emotions. Experience of unfairness engaged a widespread bilateral network encompassing insular, cingulate, and frontal regions, with dorsal striatal regions mediating the decision to reject unfair offers. Disgust engaged a defensive-avoidance circuit encompassing amygdalar, occipital, and frontal regions, while anger engaged non-overlapping systems including mid-cingulate, thalamic, and frontal regions. Unfairness and anger or disgust respectively commonly engaged the anterior and mid-insula, while the latter additionally showed common recruitment of ventrolateral prefrontal and orbitofrontal cortices. Multimodal network, behavioral, and serotonergic decoding provided a more granular and convincing dissection of these results. Findings indicate a shared neuroaffective basis underlying the impact of emotions on unfairness-induced punishment behavior and suggest a common brain circuit has been evolutionarily shaped to protect individuals from personal harm and enforce societal norms.

neuroscience↗

bioRxiv2024

Evaluation of scalp-based targeting methods of DLPFC for TMS therapy: EEG cap F3, Beam F3, CPC F3, and adjusted Beam F3

BackgroundRepetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) offers a promising approach for treating depression. Scalp-based targeting methods, including EEG Cap, Beam F3, CPC F3, and adjusted Beam F3, have evolved to improve clinical application. ObejectivesTo evaluate and compare the accuracy, reliability, speed, and training efficacy of these four methods. MethodsThree trained technicians conducted repeated DLPFC targeting measurements on 10 3D-printed head models. Accuracy was assessed by calculating targeting error and electric field strength at the subgenual anterior cingulate cortex anti-correlated peak. Reliability was evaluated through inter- and intra-rater variability, and training efficacy was examined by comparing targeting errors between trained technicians and novices. ResultsCPC F3, Beam F3, and adjusted Beam F3 demonstrated comparable accuracy in terms of electric field strength, but CPC F3 had the lowest targeting error (4.2 mm). CPC F3 also exhibited the highest reliability (inter-rater variability at 3.3 mm, intra-rater variability at 2.3 mm). The EEG cap was the fastest method (80 seconds), while CPC F3 was faster than Beam F3 but slower than EEG Cap. No significant differences in targeting error between trained technicians and novices were noted with CPC F3 and adjusted Beam F3. ConclusionThe comparative analysis of four scalp-based targeting methods provides insights that can enhance clinical decision-making and the practical application. While accuracy among CPC F3, Beam F3, and adjusted Beam F3 was comparable, CPC F3 emerges as highly practical due to its significantly better reliability, training efficacy, moderate speed, and straightforward implementation.

neuroscience↗

bioRxiv2024

Spatiotemporal evidence accumulation through saccadic sampling for object recognition

Visual object recognition has been extensively studied under fixation conditions, but our natural viewing involves frequent saccadic eye movements that scan multiple local informative features within an object (e.g., eyes and mouth in a face image). These saccades would contribute to object recognition by subserving the integration of sensory information across local features, but mechanistic models that explain human behavior during natural object recognition have yet to be established due to the presumed complexity of the interactions between the visual and oculomotor systems. Here, we employ a framework of perceptual decision making and show that human face and object categorization behavior with saccades can be quantitatively explained by a model that simply accumulates the sensory evidence available at each moment. Our model can fit human object-recognition behavior even under conditions in which people freely make saccades to scan local features, departing from past studies that required controlled eye movements to test trans-saccadic integration. Moreover, further experimental results confirmed that active saccade commands (efference copy) do not substantially contribute to behavioral performance. Therefore, we propose that object recognition with saccades can be approximated by a parsimonious decisionmaking model without assuming complex interactions between the visual and oculomotor systems.

neuroscience↗

bioRxiv2024

Hippocampal neuronal activity is aligned with action plans

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards, and actions, where the extent of tuning depends on ongoing task demands. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or if a more generic computation can account for these observations. To disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice where space, auditory tones, rewards, and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans toward deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

neuroscience↗

bioRxiv2024

Maize genetic diversity identifies moisture-dependent root-branch signaling pathways

Plants grow complex root systems to extract unevenly distributed resources from soils. Spatial differences in soil moisture are perceived by root tips leading to the patterning of new root branches towards available water, a process called hydropatterning. Little is known about hydropatterning behavior and its genetic basis in crops plants. Here, we develop an assay to measure hydropatterning in maize and reveal substantial differences between tropical/subtropical and temperate maize breeding germplasm that likely resulted from divergent selection. Genetic dissection of hydropatterning confirmed the regulatory role of auxin and revealed that the gaseous hormone ethylene acts to locally inhibit root branching from air-exposed tissues. These findings demonstrate the crop relevance of hydropatterning and establish its genetic basis.

plant biology↗

bioRxiv2024

Scalable and universal prediction of cellular phenotypes

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic levels. The vast combinatorial space of possible perturbations and responses makes exhaustive experimentation infeasible. Recent advances in machine learning have shown that training on diverse datasets enables transfer learning across tasks, capturing patterns that generalize and improving performance on previously unseen problems. Inspired by this principle, we present Prophet, a transformer-based model pretrained on a vast, heterogeneous collection of perturbation experiments. This pretraining allows Prophet to predict the outcomes of untested genetic or chemical perturbations in novel cellular contexts, spanning phenotypes such as gene expression, viability, and morphology. By leveraging shared structure across apparently disconnected assays, Prophet provides a scalable framework for large-scale virtual screening and prioritization of informative experiments. Prophet consistently outperforms baseline models, including those trained on single phenotypes, showing that transfer learning between phenotypes not only is possible but improves predictive accuracy. Its capabilities extends to in vivo developmental systems, where it recapitulates known lineage biology and proposes new candidates. In a large-scale in silico screen for melanoma, Prophet identified and experimentally validated compounds with selective activity that mirrored clinically approved therapies, demonstrating its ability to transform perturbation biology into a predictive and scalable engine for therapeutic discovery.

cell biology↗

bioRxiv2024

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗

bioRxiv2024

NanoLAS 2.0: A Comprehensive Update on a Nanobody-Focused Platform with Advanced Visualization and Docking Simulation Features

SummaryNanobodies, a unique subclass of antibodies initially discovered in camelids, characterized by the absence of light chains and consisting solely of a heavy chain variable region. This distinctive structure endows nanobodies with inherent advantages in the realms of disease treatment and biopharmaceutical applications. Presently, research and applications concerning nanobodies are experiencing rapid growth. However, existing databases suffer from non-uniform data sources and a lack of data standardization. To address these issues, we developed the NanoLAS database in 2023. Despite the progress in data integration made by NanoLAS, there was room for improvement in search functionality, three-dimensional structural display, and other areas. Building upon this foundation, we introduce the comprehensively updated NanoLAS 2.0. This version offers updates to data sources, precise 3D structural presentation, and molecular docking simulation capabilities, refines the multi-condition search mechanism, and incorporates a brand-new sequence viewer as well as epitope prediction functionality. Additionally, to cater to the needs of researchers, we have designed a user-friendly and intuitive interface. In summary, we anticipate that NanoLAS 2.0 will serve as a powerful and easy-to-use research tool, facilitating researchers in their exploration of nanobodies and propelling advancements in the field of nanobody research and application. AvailabilityNanoLAS 2.0 is available at https://www.nanolas2.online Contactbingdinghuang@sztu.edu.cn and wangxin@sztu.edu.cn

bioinformatics↗

bioRxiv2024

Muller glia cell cycle re-activation by simultaneous cyclin D1 overexpression and p27kip1 knockdown promotes retinal regeneration in mice

Harnessing the regenerative potential of endogenous stem cells to restore lost neurons is a promising strategy for treating neurodegenerative disorders. Muller glia (MG), the primary glial cell type in the retina, exhibit extraordinary regenerative abilities in zebrafish, proliferating and differentiating into neurons post-injury. However, the regenerative potential of mouse MG is limited by their inherent inability to re-enter the cell cycle, constrained by high levels of the cell cycle inhibitor p27Kip1 and low levels of cyclin D1. Here, we report a method to drive robust MG proliferation by adeno-associated virus (AAV)-mediated cyclin D1 overexpression and p27Kip1 knockdown. MG proliferation induced by this dual targeting vector was self-limiting, as MG did not undergo uncontrolled proliferation. As shown by single-cell RNA-sequencing, cell cycle reactivation led to suppression of interferon signaling, activation of reactive gliosis, and downregulation of glial genes in MG. Over time, the majority of the MG daughter cells retained the glial fate, resulting in an expanded MG pool. Interestingly, about 1% MG daughter cells expressed markers for retinal interneurons, suggesting latent neurogenic potential in a small MG subset. By establishing a safe, controlled method to promote MG proliferation in vivo while preserving retinal integrity, this work provides a valuable tool for combinatorial therapies integrating neurogenic stimuli to promote neuron regeneration.

neuroscience↗

bioRxiv2024

Optimization of gene knockout approaches and practical solutions to sgRNA selection challenges in hPSCs with inducible Cas9 system

RationaleCRISPR/Cas9 has been extensively used to knock out genes, allowing the study of genetic loss-of-function in human pluripotent stem cells (hPSCs). However, the current use of the Cas9-sgRNA plasmid or iCas9 system for gene editing in hPSCs has resulted in limited and inconsistent editing efficiency, as well as labor-intensive work. Additionally, identifying single-guide RNAs (sgRNAs) with high cleavage efficiency and distinguishing them from ineffective ones, which efficiently induce frameshift INDELs (Indels and Deletions) but fail to eliminate target proteins expression, are major challenges in gene knockout experiments. MethodsThis study addresses above issues using an optimized doxycycline-induced spCas9-expressing hPSCs (hPSCs-iCas9) system. We initially developed this system by optimizing a number of parameters to maximize INDELs introducing efficiency in hPSCs-iCas9 cells. The INDELs determined by this system were then compared to predicted scores from three cleavage efficiency scoring algorithms to validate the algorithms accuracy and consistency. Furthermore, we conducted gene knockout using a set of sgRNAs targeting different exons of the ACE2 gene to achieve approximately 80% INDELs for each targeting locus. Western blotting was then performed to detect ACE2 protein expression levels, enabling the identification of potentially ineffective sgRNAs. ResultsSeveral critical factors, including cell tolerance to nucleofection stress, sgRNA stability, nucleofection frequency, and the cell-to-sgRNA ratio, were found to have significant impact on editing efficiency in hPSCs-iCas9. Fine-tuning these parameters markedly improved this efficiency, resulting in up to 93% INDELs for single gene knockout. The three scoring algorithms exhibited significant differences or even conflicts in scoring cleavage efficiency. Through comparing experimental observations to predicted scores, we discovered that the Benchling algorithm outperformed the other two in terms of accuracy and consistency. Furthermore, a sgRNA targeting exon 2 of ACE2 gene was quickly identified as ineffective, as evidenced by the edited cells pool containing 80% INDELs while ACE2 protein expression retained unchanged detected by Western blot. ConclusionThe findings of this study offer valuable insights into the optimal design of gene knockout experiments in hPSCs and provide practical solutions to sgRNA selection challenges for gene editing.

cell biology↗

bioRxiv2024

Precise Generation of Conformational Ensembles for Intrinsically Disordered Proteins Using Fine-tuned Diffusion Models

Intrinsically disordered proteins (IDPs) play pivotal roles in various biological functions and are closely linked to many human diseases including cancer, diabetes and Alzheimer disease. Structural investigations of IDPs typically involve a combination of molecular dynamics (MD) simulations and experimental data to correct for intrinsic biases in simulation methods. However, these simulations are hindered by their high computational cost and a scarcity of experimental data, severely limiting their applicability. Despite the recent advancements in structure prediction for structured proteins, understanding the conformational properties of IDPs remains challenging partly due to the poor conservation of disordered protein sequences and limited experimental characterization. Here, we introduce IDPFold, a method capable of generating conformational ensembles for IDPs directly from their sequences using fine-tuned diffusion models. IDPFold bypasses the need for Multiple Sequence Alignments (MSA) or experimental data, achieving accurate predictions of ensemble properties across numerous IDPs. By sampling conformations at the backbone level, IDPFold provides more detailed structural features and more precise property estimation compared to other state-of-the-art methods. IDPFold is ready to be used in the elucidate the sequence-disorder-function paradigm of IDPs.

bioinformatics↗

bioRxiv2024

Perpetual step-like restructuring of hippocampal circuit dynamics

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we found that CA1 population vectors decorrelated gradually within a session. In contrast, individual neurons exhibited predominantly step-like emergence and disappearance of place fields or sustained change in within-field firing. The changes were not restricted to particular parts of the maze or trials and did not require apparent behavioral changes. The same place fields emerged, disappeared, and reappeared across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally-driven perpetual step-like reorganization of the neuronal assemblies.

neuroscience↗