Biology subjects

Tang, J.

Publications and source records attributed to Tang, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

A highly contiguous hexaploid wheat genome assembly facilitates analysis of 1RS translocation and mining of a new adult plant resistance locus to yellow rust disease

High-quality genome information is essential for efficiently deciphering and improving crop traits. Here we report a highly contiguous hexaploid genome assembly for the key wheat breeding parent Zhou8425B, an elite 1BL/1RS translocation line with durable adult plant resistance (APR) against rust diseases. By using HiFi and Hi-C sequencing reads, a 14.75 Gb genome assembly, with contig N50 and scaffold N50 values reaching 70.94 and 735.11 Mb, respectively, was developed. Comparison with 16 previously sequenced common wheat cultivars revealed unique chromosomal structural features in Zhou8425B. Notably, the 1RS translocation in Zhou8425B was apparently longer and carried more genes encoding AP2/ERF-ERF and B3 transcription factors relative to its counterpart in several genome sequenced 1BL/1RS varieties and rye lines. Aided by Zhou8425B genome assembly, a new APR locus (i.e., YrZH3B) against yellow rust (YR) disease was finely mapped to a 1 - 2 Mb interval on chromosome 3BS. Analysis with 212 Zhou8425B derivative varieties showed that pyramiding of YrZH3B with two other APR loci (YrZH22 and YrZH84) significantly decreased YR severity and enhanced grain yield, with triple combination (YrZH3B/YrZH22/YrZH84) having the highest effects. Our data demonstrate the high value of Zhou8425B assembly in studying wheat genome and agronomically important genes.

genomics↗

bioRxiv2023

Broadly neutralizing antibody induction by non-stabilized SARS-CoV-2 Spike mRNA vaccination in nonhuman primates

Immunization with mRNA or viral vectors encoding spike with diproline substitutions (S-2P) has provided protective immunity against severe COVID-19 disease. How immunization with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike elicits neutralizing antibodies (nAbs) against difficult-to-neutralize variants of concern (VOCs) remains an area of great interest. Here, we compare immunization of macaques with mRNA vaccines expressing ancestral spike either including or lacking diproline substitutions, and show the diproline substitutions were not required for protection against SARS-CoV-2 challenge or induction of broadly neutralizing B cell lineages. One group of nAbs elicited by the ancestral spike lacking diproline substitutions targeted the outer face of the receptor binding domain (RBD), neutralized all tested SARS-CoV-2 VOCs including Omicron XBB.1.5, but lacked cross-Sarbecovirus neutralization. Structural analysis showed that the macaque broad SARS-CoV-2 VOC nAbs bound to the same epitope as a human broad SARS-CoV-2 VOC nAb, DH1193. Vaccine-induced antibodies that targeted the RBD inner face neutralized multiple Sarbecoviruses, protected mice from bat CoV RsSHC014 challenge, but lacked Omicron variant neutralization. Thus, ancestral SARS-CoV-2 spike lacking proline substitutions encoded by nucleoside-modified mRNA can induce B cell lineages binding to distinct RBD sites that either broadly neutralize animal and human Sarbecoviruses or recent Omicron VOCs. One Sentence SummaryNon-stabilized SARS-CoV-2 Spike mRNA vaccination activated B cells that target either conserved epitopes on SARS-CoV-2 Omicron variants of concern, or cross-neutralizing epitopes on pre-emergent Sarbecoviruses.

immunology↗

bioRxiv2023

Effective High-Accuracy Prediction of Protein Structures from Easily Obtainable Artificial Homologous Sequences by Structure-Stability-Based Selection

High-resolution protein structure determination by experimental techniques is notoriously costly and labor intensive. This problem is mostly solved with arrival of deep-learning-based computational prediction by AlphaFold2 but only for those proteins with enough naturally occurring homologous sequences. Here, we attempt to close the remaining gap by employing artificially generated, structure-stability-selected homologous sequences as an input for AlphaFold2. We showed that only one round of selection of deeply mutated sequences of a few mutations is sufficient to bring the accuracy of predicted structures to better than 2 [A] RMSD from their respective native structures for four of the five proteins experimented. The performance for three out of five proteins is even better than AlphaFold2 with naturally occurring sequences. The only protein with predicted structure of >2 [A] (at 2.92 [A]) RMSD is due to a fully exposed (i.e., likely flexible) {beta}-hairpin. The result supports a future of determining protein structures at low cost and fast turnaround by integrating simple molecular biology experiments (deep mutational scanning and in vivo or in vitro selection) with high-throughput sequencing. The technique proposed here can be further extended to predict structures of protein complexes as well as proteins with posttranslational modifications.

molecular biology↗

bioRxiv2023

MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

Fluorescent microscopy imaging is vital to capturing single-cell spatial data, characterizing tissue organization and facilitating comprehensive analysis of cellular state. Advancements in fluorescent microscopy imaging technologies have enabled precise downstream cellular analysis, particularly in cell segmentation. Accurate segmentation of individual cells allows better profiling and understanding of cell properties and behaviors. The majority of existing segmentation methods predominantly concentrate on enhancing segmentation algorithms, and their effectiveness strongly relies on the input stained image quality. Factors such as high cellular density, indistinct cell boundaries, and staining artifacts can result in uneven and low-quality staining, particularly causing missing or unclear membrane staining. These artifacts adversely impact the efficacy of the subsequent cell segmentation methods. To tackle this insufficient membrane staining, we propose a novel approach, MO_SCPLOWEMC_SCPLOW-GAN, to generate high-quality membranes for cells with missing or weak membranes. Inspired by advanced style transfer techniques in computer vision, MO_SCPLOWEMC_SCPLOW-GAN styles the content of the cells with missing or weak membranes into cells with integrated membrane staining. Considering the differences in membrane morphology between epithelial/tumor cells and immune cells, MO_SCPLOWEMC_SCPLOW-GAN deals with tumor and immune cells separately, not only enhancing membrane staining for cells with partially weak membrane signals but also generating membranes for cells with only nuclear channels. The proposed MO_SCPLOWEMC_SCPLOW-GAN is evaluated using the publicly available CosMx dataset. Experimental results demonstrate significant improvements in image staining quality, more accurate representation of membrane morphology characteristics, and better performance in downstream segmentation tasks. MO_SCPLOWEMC_SCPLOW-GAN is flexibly adapted and applied to other spatially resolved transcriptomics datasets, such as MERFISH and FISHseq. Our work provides a new perspective on tackling the challenges in cell segmentation from fluorescent microscopy image restoration. The implementation of MO_SCPLOWEMC_SCPLOW-GAN is open-source and available at the github repository https://github.com/OmicsML/Mem-GAN. The interactive webserver-based demo of MO_SCPLOWEMC_SCPLOW-GAN can be accessed at http://omicsml.ai/memgan.

bioinformatics↗

bioRxiv2023

Multi-modal microvascular cerebral blood flow velocity mapping with 14T single-vessel MRI and optical microscopy in the mouse brain

In this study, we imaged the same penetrating cortical vessels in a mouse using ultrahigh field single-vessel MRI at 14 T and high-resolution optical microscopy imaging. The optical imaging was performed through a chronic sealed cranial window, while the single-vessel MRI was facilitated by a custom-designed, chronically implanted radiofrequency coil surrounding the window. The MRI and optical imaging were performed sequentially focused on the same penetrating cortical arterioles and surfacing venules within the whisker barrel cortex. With MRI, we obtained high-resolution multi-gradient echo (MGE) images and single-vessel phase contrast (PC) velocity maps. With optical imaging, we acquired microvascular angiograms using 2-Photon Microscopy (2PM) and Optical Coherence Tomography (OCT) and measured the blood flow velocity using Dynamic Light Scattering OCT (DLS-OCT). The MGE images, PC-based MRI velocity maps, OCT angiograms, and DLS-OCT velocity maps were coregistered with the 2PM microvascular angiograms. Using these tools, we cross-validated blood flow velocity in the penetrating cortical arterioles and surfacing venules measured by single-vessel MRI and OCT at rest. Our novel method demonstrates the possibility of combining ultrahigh field single-vessel MRI and high-resolution optical methods (e.g., 2PM and OCT) for studying brain structure and function with single microvessel precision.

bioengineering↗

bioRxiv2023

Targeted Deletion of All Known Thyroid Hormone Receptors Causes Maturation Retardation and Early-onset Degeneration of Cochlear Outer Sulcus

Thyroid hormone (TH) and its receptors (TRs) are crucial for cochlear late-stage development and endocochlear potential (EP) maintenance. However, the mechanism underlying EP reduction in the absence of TH or TRs remains elusive. Cochlear outer sulcus root cells undergo significant morphological changes during late-stage cochlear development and are believed to play a role in maintaining endolymph homeostasis and EP. Yet, it is unknown whether TH and/or TRs are necessary for root cell differentiation and function. Here, we elucidate the essential role of TH or TRs in postnatal root cell development and survival in mice. TH deficiency significantly delayed root cell differentiation. Otocyst-selective deletion of both Thra and Thrb, but not Thrb alone, leads to a similar impairment, accompanied by early degeneration of root cells, with the stria vascularis remaining unaffected. Furthermore, a 22% reduction in mean EP magnitudes was observed in conditional TRs double knockout mice at 4 months of age, less pronounced than in global TRs knockout mice. Transcriptome analysis reveals that TH deficiency downregulates a significant portion of root cell-enriched genes. These findings underscore the redundant roles of TR and TR{beta} in promoting the late-stage differentiation and survival of root cells. Additionally, they suggest that the expression of TRs in cochlear epithelium is crucial for maintaining an optimal EP magnitude, while TRs expressed in areas outside cochlear epithelium, particularly in spiral ligament fibrocytes, may also significantly contribute to EP maintenance. This study advances our understanding of TH in cochlear outer sulcus development and EP maintenance.

developmental biology↗

bioRxiv2023

TCM-CMap: A Novel Computational Methodology to Link Gene Perturbation to Therapeutic Targets in Complex TCM Prescriptions

Herbal medicine, especially Traditional Chinese medicine (TCM), is a valuable resource of natural products for drug discovery with obvious therapeutic effects. However, the mechanisms of action (MOAs) of herbal medicine are often unknown due to limited target information and the complexity of multiple ingredients and targets. This study developed a Herb-CMap algorithm to prioritize active ingredients and targets within herbal medicine by integrating transcriptomics- based gene perturbation data with a random walk algorithm. This methodology bridges the gap between gene perturbation of herbal medicine and its therapeutic target. Using the Suhuang antitussive capsule (Suhuang) for treating cough variant asthma (CVA) as a case study, we identify and experimentally verify that quercetin and luteolin directly interact with Il17a, Pik3cb, Pik3cd, Akt1, and Tnf. These interactions inhibit the IL-17 signaling pathway and inactivate PI3K, Akt, and NF- {kappa}B, preventing lung inflammation and treating CVA. Our findings demonstrate the potential of the Herb-CMap methodology to provide insights into the molecular MOAs of herbal medicine, thereby advancing drug discovery from herbal medicine.

systems biology↗

bioRxiv2023

A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

AO_SCPLOWBSTRACTC_SCPLOWThe fast-growing single-cell analysis community extends the horizon of quantitative analysis to numerous computational tasks. While the tasks hold vastly different targets from each other, existing works typically design specific model frameworks according to the downstream objectives. In this work, we propose a general single-cell analysis framework by unifying common computational tasks as posterior estimation problems. In light of conditional diffusion generative models, we introduce scDiff through the proposed framework and study different conditioning strategies. With data-specific conditions, scDiff achieves competitive performance against state-of-the-art in various benchmarking tasks. In addition, we illustrate the flexibility of scDiff by incorporating prior information through large language models and graph neural networks. Additional few-shot and zero-shot experiments prove the effectiveness of the prior conditioner on scDiff. Our implementation is publicly available at https://github.com/OmicsML/scDiff.

bioinformatics↗

bioRxiv2023

CellPLM: Pre-training of Cell Language Model Beyond Single Cells

The current state-of-the-art single-cell pre-trained models are greatly inspired by the success of large language models. They trained transformers by treating genes as tokens and cells as sentences. However, three fundamental differences between single-cell data and natural language data are overlooked: (1) scRNA-seq data are presented as bag-of-genes instead of sequences of RNAs; (2) Cell-cell relations are more intricate and important than inter-sentence relations; and (3) The quantity of single-cell data is considerably inferior to text data, and they are very noisy. In light of these characteristics, we propose a new pre-trained model CellPLM, which takes cells as tokens and tissues as sentences. In addition, we leverage spatially-resolved transcriptomic data in pre-training to facilitate learning cell-cell relationships and introduce a Gaussian mixture prior distribution as an additional inductive bias to overcome data limitation. CellPLM is the first single-cell pre-trained transformer that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x times higher inference speed compared to existing pre-trained models.

bioinformatics↗

bioRxiv2023

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings

Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.

bioinformatics↗

bioRxiv2023

Artemisitene protected against murine schistosomiasis japonica through anti-parasite activity and immune regulation

Schistosoma japonicum (Sj) infection induced liver granulomatous inflammation and fibrosis. As an active artemisinin analog, the implication of artemisitene (ATT) in schistosomiasis were unclear. Herein, we found that ATT significantly reduced the count of total adult worms and eggs, and increased the count of single males, injured the tegument in the surface of Sj adult worms & gynecophoral canal of males. The transcription of 98 genes in females and 48 genes in males were significantly changed, and these genes were closely related to cellular anatomical entity through gene ontology analysis. So, ATT might possess anti-parasite activity. Meanwhile, ATT treatment significantly lowered the level of glutamic oxaloacetic transaminase (AST) and glutamic pyruvic transaminase (ALT) in sera, the size of mesenteric lymph node, and granuloma, the collagen area and -SMA expression level in the liver. Liver transcriptome and multi-cytokines analysis indicated its immune regulation effect. Flow cytometry verified that the count of eosinophils in the liver were significantly increased, while the frequency of neutrophils, M1/M2 and Th1/Th2 index were significantly decreased. Therefore, we provided strong evidence that ATT has therapeutic potential through Sj clearance and anti-liver disease. Tegument development injury and immune regulation including type 2 immunity enhancement might be the mechanisms. Author summaryCurrently, there were still 290 million people worldwide who were infected by Schistosoma, and the treatment for schistosomiasis relies majorly on the use of a single drug-praziquantel. In this study, we described for the first time that artemisinin-derived artemisitene (ATT), chemically remarkably different from praziquantel, possessed the therapeutic effects on murine schistosomiasis japonica. ATT displayed both anti-Schistsosoma japonicum and anti-liver inflammation & liver fibrosis effect. Through RNA-seq and scanning electronic microscope of adult female & male worms from hepatoportal veins with or without ATT treatment, we found that the mechanisms of ATTs anti-parasites could be through injuring tegument development and then interrupting adult worms especially adult female worms clearance by immune cells such as eosinophils. Moreover, through RNA-seq of liver total RNA, ELISA of multi-cytokines in liver lysates and flow cytometry analysis of liver single cells, we found that the anti-liver diseases efficacy of ATT was associated with immune regulation especially type 2 immunity enhancement. Therefore, ATT possessed the therapeutic potential against schistosomiasis japonica and further researches were necessary for its future clinical use.

immunology↗

bioRxiv2023

Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

bioinformatics↗

bioRxiv2023

Mining drug-target interactions from biomedical literature using chemical and gene descriptions based ensemble transformer model

Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, with the potential to mine vast amounts of texts to facilitate the efficient extraction of DTIs from the literature. In this article, we approach the task of DTIs as an entity-relationship extraction problem, utilizing different pretrained transformer language models, such as BERT, to extract DTIs. Our results indicate that an ensemble approach, by combining gene descriptions from the Entrez Gene database with chemical descriptions from the Comparative Toxicogenomics Database (CTD), is critical for achieving optimal performance. The proposed model achieves an F1 score of 80.6 on the hidden DrugProt test set, which is the top-ranked performance among all the submitted models in the official evaluation. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of various gene textual descriptions sourced from Entrez Gene and UniProt databases to gain insights into their impact on the performance. Our findings highlight the potential of NLP-based text mining using gene and chemical descriptions to improve drug-target extraction tasks. Datasets utilized in this study are accessible at https://dtis.drugtargetcommons.org/.

bioinformatics↗

bioRxiv2023

SALL4B, not targeted by IMiD, is important for SALL4-mediated tumorigenesis

Immunomodulatory (IMiD) drugs have shown a prominent therapeutic activity in hematologic malignancies; however, their usage in solid tumors is limited. The oncofetal protein SALL4 is essential for cancer cell survival. While IMiDs can induce SALL4 degradation, they fail to induce cell death in SALL4-expressing cancer cell lines. Here, we observed that this inefficacy arose from their selective degradation of the long SALL4 isoform, while sparing the short SALL4B isoform. Selective silencing of SALL4B phenocopied total SALL4 depletion by inducing cancer apoptosis, underscoring the critical role of SALL4B in cancer maintenance. Recognizing that IMiDs cant degrade SALL4B, we performed a high-throughput screen to identify compound(s) that could achieve this. We identified a small molecule compound that degrades both SALL4 isoforms with enhanced potency towards SALL4B in a cereblon- and proteasome-dependent manner. This compound suppressed cancer cell proliferation and attenuated tumor development in both cell line and patient-derived xenograft models. Transcriptomic analyses further revealed convergent effects of genetic and pharmacologic SALL4B depletion on DNA damage response and replication pathways. Together, these findings identify SALL4B as the therapeutically relevant isoform in SALL4-dependent cancers and establish isoform-aware targeted degradation as a strategy to overcome the limitation of IMiDs in solid tumors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/548071v2_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@99670dorg.highwire.dtl.DTLVardef@13ca58forg.highwire.dtl.DTLVardef@998e7eorg.highwire.dtl.DTLVardef@15a1875_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract | Identification of QE: a non-IMiDs degrader capable of degrading both SALL4A and SALL4B, triggers anti-cancer effects beyond IMiDs, and Impacts of QE on Key Validated SALL4B Targets and Pathways C_FIG

cancer biology↗

bioRxiv2023

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pre-training objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that 1) xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. The model also facilitates an atomic-resolution view of protein structures, leading to an advanced 3D structural prediction model that surpasses existing language model-based tools. 2) xTrimoPGLM not only can generate de novo protein sequences following the principles of natural ones, but also can perform programmable generation after supervised fine-tuning (SFT) on curated sequences. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences, contributing to the evolving landscape of foundation models in protein science. Trained weight for the xTrimoPGLM model, and downstream datasets are available at https://huggingface.co/proteinglm.

synthetic biology↗

bioRxiv2023

Aged male and female Panx3 KO mice develop severe osteoarthritis independent of forced mechanical use.

Osteoarthritis (OA) is a multi-factorial disease associated with aging. As the molecular mechanisms underpinning the pathogenesis of this disease are unclear, there are no disease-modifying drugs to combat OA. Pannexin 3 (PANX3) has been shown to promote cartilage loss during posttraumatic OA. In contrast, the ablation of Panx3 in male mice results in spontaneous full-thickness cartilage lesions at 24 months of age. While protected from traumatic intervertebral disc (IVD) degeneration, Panx3 knockout (KO) mice show signs of IVD disease with altered disc mechanics. Whether the deleterious effects of ablating Panx3 in aging is the result from accumulated mechanical damage is unknown. We used male and female wildtype (WT) and global Panx3 KO C57Bl6 mice aged to 18 months of age. Mice were then randomized to sedentary (SED) or forced treadmill running (FEX) for 6 weeks. Knee joint tissues including patellar tendon, quadriceps and distal patellar enthesis, and synovium were analyzed histologically and through micro-CT, along with lumbar spine IVDs. Half of male and female sedentary Panx3 KO mice developed full-thickness cartilage lesions, severe synovitis, and ectopic fibrocartilage deposition and calcification of the knee joints in comparison to all other conditions. Panx3 KO mice with severe OA show signs of quadriceps and patellar enthesitis, characterized by bone and marrow formation. Forced treadmill running did not seem to exacerbate these phenotypes in male or female Panx3 KO mice; however, it may have contributed to the development of lateral compartment OA. The IVDs of aged Panx3 KO mice displayed no apparent differences to control mice, and forced treadmill running had no further effects in either genotype. We conclude that aged Panx3 KO mice show features of late-stage primary OA including full-thickness cartilage erosion, severe synovitis, and enthesitis. These data suggest that the deletion of Panx3 is deleterious to synovial joint health in aging. LAY SUMMARYOsteoarthritis is a common joint disease, and its incidence can increase with aging. As such, no drugs are available to treat its progression. Recently, we discovered that cellular channels composed of Pannexin 3 (PANX3) proteins may contribute to the health of joint tissues. In this study, we propose a new model for age-related primary OA, using histology, microCT, and pathology assessments under sedentary and forced exercise conditions. We found that aged mice lacking the channel protein PANX3 developed late-stage OA, including severe cartilage loss, joint inflammation, and tendon attachment damage. Half of the sedentary PANX3-deficient mice showed these changes, along with abnormal bone growth. Treadmill running did not make the condition worse but may have caused additional joint damage in some areas. The spine, however, remained unaffected. These findings suggest that deleting Panx3 harms joint health with aging. Importantly, this mouse model mimics human primary OA, even without added factors like diet changes or forced exercise.

physiology↗

bioRxiv2023

CRISPR-Cas12a-Based Rapid and Sensitive Detection of rpoBL378R in Mycobacterium tuberculosis

Rifampin is the most effective drug in the treatment of tuberculosis, whose major pathogen is Mycobacterium tuberculosis (MTB), whereas there are still certain MTB strains resistant to the therapy of rifampin. The rpoB mutations play a central role in MTB resistance to the rifampin therapy, so it is crucial to identify these mutations in order to discover novel therapeutic approaches to these drug-resistant MTB strains. Here we show that a CRISPR-Cas12a-based detection platform with recombinase polymerase amplification and fluorescence reporter can be utilized to detect and visualize an MTB drug-resistant point mutation (rpoBL378R) from its rpoB wild type. Notably, this detection system is highly specific because it did not cross-react with contrived reference samples containing the genomes of MTB H37Rv, Mycobacterium smegmatis (M. smegmatis), Mycobacterium aureus (M. aureus), and Escherichia coli (E. coli). Collectively, this strategy based on CRISPR-Cas12a that we show in this report is simple, sensitive as well as specific for detection of the rifampin-resistant MTB H37Rv with the rpoBL378R mutation, indicating that this CRISPR-Cas12a-based detection platform has high potential to be exploited for clinic application to identify MTB mutations.

microbiology↗

bioRxiv2023

Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression

What genes and regulatory sequences critically differentiate modern humans from apes and archaic humans, which share highly similar genomes but show distinct phenotypes, has puzzled researchers for decades. Previous studies examined species-specific protein-coding genes and related regulatory sequences, revealing that birth, loss, and changes in these genes and sequences can drive speciation and evolution. However, investigations of species-specific lncRNA genes and related regulatory sequences, which regulate substantial genes, remain limited. We identified human-specific (HS) lncRNAs from GENCODE-annotated human lncRNAs, predicted their DNA binding domains (DBDs) and binding sites (DBSs), analyzed DBS sequences in modern humans (CEU, CHB, and YRI), archaic humans (Altai Neanderthals, Denisovans, and Vindija Neanderthals), and chimpanzees, and investigated how HS lncRNAs and their DBSs have influenced gene expression in archaic and modern humans. Our results suggest that these lncRNAs and DBSs have substantially reshaped gene expression. This reshaping has evolved continuously from archaic to modern humans, enabling humans to adapt to new environments and lifestyles, promoting brain evolution, and resulting in cross-population differences. The parallel analysis of gene expression in GTEx tissues by HS TFs and their DBSs indicates that HS lncRNAs have reshaped gene expression in the brain more significantly than HS TFs.

genomics↗