Biology subjects

Zhang, T.

Publications and source records attributed to Zhang, T..

At least 307 records · Page 17Linked to original sources

bioRxiv2023

Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes

Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. Here we introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to other existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it is capable in providing reliable estimates of overall genome copies, and species-level taxonomic profiles.

bioinformatics↗

bioRxiv2023

Transcriptional shut-off of MAP kinase signaling enables pluripotency maintenance during diapause

Exposure of unicellular or multicellular organisms to adverse environmental conditions, including nutrient deprivation, may induce a state of suspended animation or diapause. The diapause minimizes the organisms reliance on external energy sources and ensures survival. Among different forms of diapause, embryonic diapause, caused by a limited supply of nutrients to the growing embryos, is particularly challenging for the organism. Diapause embryos stop developing at the peak of pluripotent cell differentiation and maintain this undifferentiated yet entirely vital state despite the overall reduction in anabolic processes and genome-wide transcriptional repression. Using ES cells commonly employed to study the mechanism of embryonic diapause, we solve the paradox of the cell maintenance in an undifferentiated ES cell state during diapause. We find that broad transcriptional repression by long-term inhibition of the bromodomain and extra-terminal (BET) proteins causes diapause. These diapause ES cells upregulate a functionally linked group of genes encoding negative regulators of MAP kinase signaling (NRMKS), which play a crucial role in ES cell differentiation. We find that elevated NRMKS expression is a hallmark of the diapause cells and cells exposed to diapause-inducing conditions, including mTOR inhibition, and is required for the maintenance of ES cell pluripotency during diapause. Mechanistically, exposure of ES cells to diapause-inducing conditions leads to rapid decline of the Capicua transcriptional repressor (CIC) at the NRMKS gene promoters, followed by transcriptional upregulation of NRMKS genes. The mTOR and BET-dependent transcriptional switch supporting the undifferentiated state of the diapause ES cells suggests a broader usage of this mechanism in maintaining the undifferentiated state of metabolically dormant stem- or stem-like cells in different tissues.

cell biology↗

bioRxiv2023

Function-associated scRNA-seq on single lung cancer organoids unravels the immune landscape of tumor parenchyma

In vitro models coupled with multimodal approaches are urgently needed to decipher the local tumor immune microenvironment (TIME) owing to the heterogeneous nature of immune cells and their diverse spatial distributions. Here we generate primary lung cancer organoids (pLCOs) by isolating the tumor cell clusters, including the infiltrated immune cells, from dissected lung cancer samples. A FascRNA-seq platform allowing both phenotypic evaluation and the scRNA-seq of all the single cells in an organoid was developed to dissect the TIME in individual pLCOs. Our analysis on 171 individual pLCOs derived from 7 patients revealed that pLCOs retained the fundamental features as well as intra-tumor heterogeneity of local TIME in the parenchyma of parental tumor tissues, providing a series of models with consistent genetic background but various TIME. Linking the single cell transcriptome data of individual pLCOs with their responses to ICB allowed us to confirm the pivotal role of CD8+ Ts in ICB induced anti-tumor immunity, to identify the potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity.

cancer biology↗

bioRxiv2023

Structure basis for recognition of 26RFa by the pyroglutamylated RFamide peptide receptor

The neuropeptide 26RFa, a member of the RF-amide peptide family, activates the pyroglutamylated RF-amide peptide receptor (QRFPR), a class A GPCR. The 26RFa/QRFPR system plays critical roles in energy homeostasis, making QRFPR an attractive drug target for treating obesity, diabetes, and eating disorders. However, the lack of structural information has hindered our understanding of the peptide recognition and regulatory mechanism of QRFPR, impeding drug design efforts. In this study, we determined the cryo-EM structure of the Gq-coupled QRFPR bound to 26RFa. The structure reveals a unique assembly mode of the receptor extracellular regions and the peptide N-terminus and elucidates the recognition mechanism of the C-terminal heptapeptide of 26RFa within the transmembrane binding pocket of QRFPR. The study also clarifies the similarities and distinctions in the binding pattern of the RF-amide moiety in five RF-amide peptides and the RY-amide segment in neuropeptide Y. These findings deepen our understanding of the RF-amide peptides recognition, aiding in the rational design of drugs targeting QRFPR and other RF-amide receptors.

biophysics↗

bioRxiv2023

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

bioinformatics↗

bioRxiv2023

Viruses that 'infect' viruses: Cas12f1 and Cas9 gene drive in HSV1

Synthetic CRISPR-Cas9 gene drive has been developed as a potential tool to control harmful species. However, Cas9 gene drive faces high resistance rate and mitigation strategies developed so far are difficult to implement. Furthermore, studying the resistance to gene drive is time consuming and challenging in higher organisms. We here tackled these two challenges simultaneously by generating Cas9 and Un1Cas12f1 gene drive in a fast-replicating DNA virus, HSV1. We assessed the transmission dynamics and resistance formation through phenotypical staining and next-generation sequencing, and demonstrated that HSV1 supported fast and effective transmission of gene drives, and the Un1Cas12f1 gene drives yielded greater conversion and lower resistance than did the Cas9 gene drives. This positions the Un1Cas12f1 gene drive as a promising alternative, and HSV1 emerges as a dependable and swift platform for gene drive assessment. The gene drive viruses function like pathogens that specifically infect viruses, offering potential applications in attenuating viral infections.

microbiology↗

bioRxiv2023

Multi-task machine learning reveals the neuroanatomy fingerprint of mental processing

Mental processing delineates the functions of human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of functional neuroanatomy substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a functional neuroanatomy fingerprint, i.e., a unique and reliable pattern of functional neuroanatomy that underlies the mental process. To address this question, we utilized a multi-task deep learning model to disentangle the functional neuroanatomy fingerprint of seven different and representative mental processes including Emotion, Gambling, Language, Motor, Relational, Social, and Working Memory. Results based on the functional magnetic resonance imaging data of two independent cohorts of 1235 subjects from the US and China consistently show that each of the seven mental processes possessed a functional neuroanatomy fingerprint, which is represented by a unique set of functional activity weights of whole-brain regions characterizing the degree of each region involved in the mental process. The functional neuroanatomy fingerprint of a specific mental process exhibits high discrimination ability (93% classification accuracy and AUC of 0.99) with those of the other mental processes, and is robust across different datasets and using different brain atlases. This study provides a solid functional neuroanatomy foundation for investigating the neural mechanism of mental processing. One-Sentence SummaryThere exists a functional neuroanatomy fingerprint to underlie the mental process.

bioinformatics↗

bioRxiv2023

Perceptual error based on Bayesian cue combination drives implicit motor adaptation

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating ones effector, supported by Bayesian cue integration, underpins the sensorimotor systems implicit adaptation.

neuroscience↗

bioRxiv2023

LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model

Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. Here, we report an ultra-lightweight deep graph network, named LightRoseTTA, to achieve accurate and high-efficient prediction for proteins. Notably, three highlights are possessed by our LightRoseTTA: (i) high-accurate structure prediction for proteins, being competitive with RoseTTAFold on multiple popular datasets including CASP14 and CAMEO; (ii) high-efficient training and inference with an ultra-lightweight model, costing only one week on one single general NVIDIA 3090 GPU for model-training (vs 30 days on 8 high-speed NVIDIA V100 GPUs for RoseTTAFold) and containing only 1.4M parameters (vs 130M in RoseTTAFold); (iii) low dependency on multi-sequence alignments (MSA, widely-used homologous information), achieving the best performance on three MSA-insufficient datasets: Orphan, De novo, and Orphan25. Besides, our LightRoseTTA is transferable from general proteins to antibody data, as verified in our experiments. We visualize some case studies to demonstrate the high-quality prediction, and provide some insights on how the structure predictions facilitate the understanding of biological functions. We further make a discussion on the time and resource costs of LightRoseTTA and RoseTTAFold, and demonstrate the feasibility of lightweight models for protein structure prediction, which may be crucial in the resource-limited research for universities and academy institutions. We release our code and model to speed biological research.

bioinformatics↗

bioRxiv2023

Human Pancreatic α-Cell Heterogeneity and Trajectory Inference Analysis Using Integrated Single Cell- and Single Nucleus-RNA Sequencing Platforms

Prior studies have shown that pancreatic -cells can transdifferentiate into {beta}-cells, and that {beta}-cells de-differentiate and are prone to acquire an -cell phenotype in type 2 diabetes (T2D). However, the specific human -cell and {beta}-cell subtypes that are involved in -to-{beta}-cell and {beta}-to--cell transitions are unknown. Here, we have integrated single cell RNA sequencing (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) of isolated human islets and human islet grafts and provide additional insight into -{beta} cell fate switching. Using this approach, we make seven novel observations. 1) There are five different GCG-expressing human -cell subclusters [1, 2, -{beta}-transition 1 (AB-Tr1), -{beta}-transition 2 (AB-Tr2), and -{beta} (AB) cluster] with different transcriptome profiles in human islets from non-diabetic donors. 2) The AB subcluster displays multihormonal gene expression, inferred mostly from snRNA-seq data suggesting identification by pre-mRNA expression. 3) The 1, 2, AB-Tr1, and AB-Tr2 subclusters are enriched in genes specific for -cell function while AB cells are enriched in genes related to pancreatic progenitor and {beta}-cell pathways; 4) Trajectory inference analysis of extracted - and {beta}-cell clusters and RNA velocity/PAGA analysis suggests a bifurcate transition potential for AB towards both - and {beta}-cells. 5) Gene commonality analysis identifies ZNF385D, TRPM3, CASR, MEG3 and HDAC9 as signature for trajectories moving towards {beta}-cells and SMOC1, PLCE1, PAPPA2, ZNF331, ALDH1A1, SLC30A8, BTG2, TM4SF4, NR4A1 and PSCK2 as signature for trajectories moving towards -cells. 6) Remarkably, in contrast to the events in vitro, the AB subcluster is not identified in vivo in human islet grafts and trajectory inference analysis suggests only unidirectional transition from -to-{beta}-cells in vivo. 7) Analysis of scRNA-seq datasets from adult human T2D donor islets reveals a clear unidirectional transition from {beta}-to--cells compatible with dedifferentiation or conversion into -cells. Collectively, these studies show that snRNA-seq and scRNA-seq can be leveraged to identify transitions in the transcriptional status among human islet endocrine cell subpopulations in vitro, in vivo, in non-diabetes and in T2D. They reveal the potential gene signatures for common trajectories involved in interconversion between - and {beta}-cells and highlight the utility and power of studying single nuclear transcriptomes of human islets in vivo. Most importantly, they illustrate the importance of studying human islets in their natural in vivo setting.

cell biology↗

bioRxiv2023

An automated archival single-nucleus total RNA sequencing platform mapping integrative and retrospective cell atlas of gliomas

Single-cell RNA sequencing (scRNA-seq) has dramatically transformed biomedical research within laboratory settings. It has been extensively employed to investigate the heterogeneity and plasticity of glioma, the most prevalent brain tumor. However, the clinical diagnosis and treatment of glioma remain complex and challenging, highlighting the need for comprehensive cancer research. Currently available scRNA-seq platforms are insufficient to fulfill the demands posed by large-scale clinical applications. Here, we present an automated high-throughput single-nucleus total RNA sequencing platform, known as AAsnRandom-seq. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, designed to accommodate a diverse range of sample types. The performance and versatility of AAsnRandom-seq are validated using over one hundred clinical FFPE and frozen samples. AAsnRandom-seq was applied to archival FFPE samples of various glioma subtypes, including rare clinical samples, and matched primary-recurrent glioblastomas (GBMs), delving into the comprehensive molecular characteristic of glioma at single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles within different glioma clusters are detected. Promising recurrence-related targets and pathways are identified from the matched primary-recurrent GBMs. AAsnRandom-seq holds significant application value on large-scale integrative and retrospective clinical research using archived specimens.

bioengineering↗

bioRxiv2023

UDP-glucosyltransferase 71C4 regulates seed development by redistributing phenylpropanoid metabolism in cotton

Seeds are important for plant reproduction, and hence it is important to identify genes regulating seed development. Here, we focused on a member of the glycosyltransferase (GT) family, UDP-glucosyltransferase 71C4 (UGT71C4), which influences the seed width, seed length, and yield traits (lint percentage and seed index) by regulating the lignin and flavonoid pathways of phenylpropane metabolism, affects the oil and protein contents of mature seeds, and controls the seed size by regulating cell proliferation. Overexpression of UGT71C4 leads to seed enlargement by activating expression of peroxiredoxins and the flavonoid metabolism pathway; induces accumulation of ROS, which promotes cell proliferation; and significantly improves yield traits, with the seed index increasing from 10.66 to 11.91 and protein increasing by 5.34%, but oil content decreasing 8.98%. Conversely, knockout of UGT71C4 causes seeds to become smaller; the lignin metabolism pathway to be activated, especially enzymes relating to lignin synthesis leading to increased ectopic deposition of lignin in the ovule and constrained ovule growth and development; and significant improvement of yield traits, with lint percentage increasing from 39.62% to 41.94%, seed index decreasing from 10.66 to 8.60, protein content decreasing 4.28%, and no significant change in oil content. Our research provides new insights into seed size regulation through UDP-glucosyltransferase, providing potential methods for improving plant yield.

plant biology↗

bioRxiv2023

Targeting CD74-positive macrophages improves neoadjuvant therapy in cervical cancer as revealed by single-cell transcriptomics analysis

Uterine cervical cancer (UCC) has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic strategy for cervical cancer. However, the efficacy of PD-1 (programmed cell death protein 1) blockade combined with neoadjuvant chemotherapy (NACT) and the ensuing alterations within the tumor microenvironment need further investigation. We conducted single-cell RNA sequencing using 46,950 cells from nine sequential human cervical cancer tissues representing different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and unveiled crucial factors involved in the combination therapy. Cell communication analysis revealed the inferred interaction strength decreased between T cell-cancer cells, while the communication between macrophage-cancer cells intensified after NACT therapy. We verified that macrophages are necessary for PD-1 blockade combination to exert antitumor effects in vitro. Detailed analysis unraveled the CD74-positive macrophages frequently interacted with the immunoreactive Epi 3 subgroup during neoadjuvant combination therapy. CD74 upregulation limited phagocytosis and stimulated M2 polarization. The CD74 blockade enhanced macrophage phagocytosis, resulting in decreased viability of cervical cancer cells in vitro and in vivo. This study unveils the dynamic microenvironment of UCC influenced by NACT and PD-1 blockade combination therapy, and targeting CD74 was a potential strategy to augment the therapy efficacy.

cancer biology↗

bioRxiv2023

Improving Hi-C contact matrices using genome graphs

Three-dimensional chromosome structure plays an important role in fundamental genomic functions. Hi-C, a high-throughput, sequencing-based technique, has drastically expanded our comprehension of 3D chromosome structures. The first step of Hi-C analysis pipeline involves mapping sequencing reads from Hi-C to linear reference genomes. However, the linear reference genome does not incorporate genetic variation information, which can lead to incorrect read alignments, especially when analyzing samples with substantial genomic differences from the reference such as cancer samples. Using genome graphs as the reference facilitates more accurate mapping of reads, however, new algorithms are required for inferring linear genomes from Hi-C reads mapped on genome graphs and constructing corresponding Hi-C contact matrices, which is a prerequisite for the subsequent steps of the Hi-C analysis such as identifying topologically associated domains and calling chromatin loops. We introduce the problem of genome sequence inference from Hi-C data mediated by genome graphs. We formalize this problem, show the hardness of solving this problem, and introduce a novel heuristic algorithm specifically tailored to this problem. We provide a theoretical analysis to evaluate the efficacy of our algorithm. Finally, our empirical experiments indicate that the linear genomes inferred from our method lead to the creation of improved Hi-C contact matrices. These enhanced matrices show a reduction in erroneous patterns caused by structural variations and are more effective in accurately capturing the structures of topologically associated domains.

genomics↗

bioRxiv2023

MYC amplifies mitotic perturbations elicited by LXY18 to enable synthetic lethality

The MYC oncoprotein represents an intriguing target for cancer treatment, but its therapeutic potential has been hindered by the absence of specific pharmacological inhibitors. In this study, we demonstrate that the phenoxy quinoline compound LXY18 selectively targets and eliminates cells overexpressing MYC, leaving non-transformed cells unharmed. This synthetic lethality arises from an acute induction of multipolarity, resulting in a persistent arrest in early mitosis followed by cell death in mitosis or after mitotic slippage. Distinctively, LXY18s action contrasts with other antimitotic compounds, as they either fail to induce mitotic arrest or elicit mitotic arrest irrespective of MYC abundance. Furthermore, the MYC abundance in a panel of 98 tumor cell lines correlates with their sensitivity to LXY18. Collectively, our findings uncover LXY18 as an MYC- enabled mitotic blocker and open a new avenue to selectively target MYC-overexpressing tumor cells without affecting normal cells.

cancer biology↗

bioRxiv2023

Attila and Aetius on the roof: The succession phenomenon in the roof greening area of a primary school in Beijing during the lockdown period of the COVID-19

"Succession" refers to the process of certain species replacing others over time. It is of great significance for understanding the characteristics and evolution of ecosystems. This article compares the changes in school rooftop green areas before and after the COVID-19 epidemic (2019, 2023), and studies the situation of wild grass invading roofs during the three years of the epidemic, as well as the response of original rooftop plants to the invasion. The results showed that: [circled1] invasive weeds were concentrated in 2 families and 4 species, forming a population advantage in most of the invaded planting boxes; [circled2] The succession has had a significant impact on the roof greening area; [circled3] Among the two cultivated plants (Phedimus aizoon (PA) and Sedum sarmentosum (SS)), SS was greatly affected, with the community almost disappearing, while PA was almost unaffected.

ecology↗

bioRxiv2023

Evidence for impaired hippocampal circuitry in schizophrenia and its link to memory dysfunction

Pattern separation and pattern completion are opposing yet complementary components of mnemonic processing that heavily rely on the hippocampus. It has been shown that processing within the dentate gyrus (DG) subfield promotes pattern separation while operations within the CA3 subfield are important for pattern completion. Schizophrenia has been associated with anatomical and functional hippocampal abnormalities, including within the DG and CA3. We hypothesized that an impairment in hippocampal circuitry in individuals with first-episode schizophrenia leads to deficits in pattern separation (mnemonic discrimination) and pattern completion (recognition memory), that these deficits contribute to delusions, and that antipsychotic treatment improves circuit functioning. We measured behavioral and neural responses during the identification of new, repeated, and similar stimuli using high-resolution fMRI in 45 medication-free or minimally-treated patients with first-episode schizophrenia and 49 matched controls. We found recognition memory and pattern separation deficits in patients and a negative association between memory performance and the severity of delusions. Neural analyses revealed deficits in both univariate BOLD responses and multivariate patterns in the hippocampus during mnemonic discrimination in patients compared to controls. Importantly, by investigating the association between trial-level neural activity and behavior before and after treatment, we found that antipsychotics normalized DG activity during pattern separation and CA3 activity during pattern completion. Lastly, trial-level cortical responses during mnemonic discrimination predicted performance in patients at baseline, suggesting a compensatory role. This study provides new insight into the impact of schizophrenia and antipsychotic treatment on memory systems and uncovers systems-level contributions to pattern separation and pattern completion.

neuroscience↗

bioRxiv2023

Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase (DPYD)

Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.

pharmacology and toxicology↗