ABSTRACT
Cell fate transitions involve rapid gene expression changes and global chromatin remodeling, yet the underlying regulatory pathways remain incompletely understood. Here, we identified the RNA-processing factor Nudt21 as a novel regulator of cell fate change using transcription-factor-induced reprogramming as a screening assay. Suppression of Nudt21 enhanced the generation of induced pluripotent stem cells, facilitated transdifferentiation into trophoblast stem cells, and impaired differentiation of myeloid precursors and embryonic stem cells, suggesting a broader role for Nudt21 in cell fate change. We show that Nudt21 directs differential polyadenylation of over 1,500 transcripts in cells acquiring pluripotency, although only a fraction changed protein levels. Remarkably, these proteins were strongly enriched for chromatin regulators, and their suppression neutralized the effect of Nudt21 during reprogramming. Collectively, our data uncover Nudt21 as a novel post-transcriptional regulator of cell fate and establish a direct, previously unappreciated link between alternative polyadenylation and chromatin signaling.
Subject(s)
Cellular Reprogramming , Chromatin Assembly and Disassembly , Cleavage And Polyadenylation Specificity Factor/metabolism , Polyadenylation , Signal Transduction , Animals , Cells, Cultured , Chromatin/genetics , Chromatin/metabolism , Cleavage And Polyadenylation Specificity Factor/genetics , Embryonic Stem Cells/cytology , Embryonic Stem Cells/metabolism , HEK293 Cells , Humans , MiceABSTRACT
The baobab trees (genus Adansonia) have attracted tremendous attention because of their striking shape and distinctive relationships with fauna1. These spectacular trees have also influenced human culture, inspiring innumerable arts, folklore and traditions. Here we sequenced genomes of all eight extant baobab species and argue that Madagascar should be considered the centre of origin for the extant lineages, a key issue in their evolutionary history2,3. Integrated genomic and ecological analyses revealed the reticulate evolution of baobabs, which eventually led to the species diversity seen today. Past population dynamics of Malagasy baobabs may have been influenced by both interspecific competition and the geological history of the island, especially changes in local sea levels. We propose that further attention should be paid to the conservation status of Malagasy baobabs, especially of Adansonia suarezensis and Adansonia grandidieri, and that intensive monitoring of populations of Adansonia za is required, given its propensity for negatively impacting the critically endangered Adansonia perrieri.
Subject(s)
Adansonia , Phylogeny , Adansonia/classification , Adansonia/genetics , Biodiversity , Conservation of Natural Resources , Ecology , Endangered Species , Evolution, Molecular , Genome, Plant/genetics , Madagascar , Population Dynamics , Sea Level RiseABSTRACT
Retinoic acid (RA) is the proposed mammalian 'meiosis inducing substance'. However, evidence for this role comes from studies in the fetal ovary, where germ cell differentiation and meiotic initiation are temporally inseparable. In the postnatal testis, these events are separated by more than 1â week. Exploiting this difference, we discovered that, although RA is required for spermatogonial differentiation, it is dispensable for the subsequent initiation, progression and completion of meiosis. Indeed, in the absence of RA, the meiotic transcriptome program in both differentiating spermatogonia and spermatocytes entering meiosis was largely unaffected. Instead, transcripts encoding factors required during spermiogenesis were aberrant during preleptonema, and the subsequent spermatid morphogenesis program was disrupted such that no sperm were produced. Taken together, these data reveal a RA-independent model for male meiotic initiation.
Subject(s)
Testis , Tretinoin , Animals , Female , Male , Tretinoin/pharmacology , Spermatogenesis/genetics , Spermatogonia , Spermatozoa , Meiosis/genetics , MammalsABSTRACT
Among breast cancers, triple-negative breast cancer (TNBC) is the most poorly understood and is refractory to current targeted therapies. Using a genetic screen, we identify the PTPN12 tyrosine phosphatase as a tumor suppressor in TNBC. PTPN12 potently suppresses mammary epithelial cell proliferation and transformation. PTPN12 is frequently compromised in human TNBCs, and we identify an upstream tumor-suppressor network that posttranscriptionally controls PTPN12. PTPN12 suppresses transformation by interacting with and inhibiting multiple oncogenic tyrosine kinases, including HER2 and EGFR. The tumorigenic and metastatic potential of PTPN12-deficient TNBC cells is severely impaired upon restoration of PTPN12 function or combined inhibition of PTPN12-regulated tyrosine kinases, suggesting that TNBCs are dependent on the proto-oncogenic tyrosine kinases constrained by PTPN12. Collectively, these data identify PTPN12 as a commonly inactivated tumor suppressor and provide a rationale for combinatorially targeting proto-oncogenic tyrosine kinases in TNBC and other cancers based on their profile of tyrosine-phosphatase activity.
Subject(s)
Breast Neoplasms/metabolism , Protein Tyrosine Phosphatase, Non-Receptor Type 12/genetics , Protein Tyrosine Phosphatase, Non-Receptor Type 12/metabolism , Tumor Suppressor Proteins/metabolism , Breast Neoplasms/drug therapy , Cell Line, Tumor , Cell Transformation, Neoplastic , ErbB Receptors/metabolism , Female , Gene Expression Regulation, Neoplastic , Humans , MAP Kinase Signaling System , MicroRNAs/metabolism , Mutation , Neoplasm Metastasis , Protein Processing, Post-TranslationalABSTRACT
Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.
Subject(s)
Proteins , Molecular Docking Simulation , Ligands , Proteins/chemistry , Protein Binding , Molecular ConformationABSTRACT
Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.
Subject(s)
Neural Networks, Computer , Protein Processing, Post-Translational , Proteome/metabolism , Signal Transduction , Protein DomainsABSTRACT
Chondrosarcoma(CS), a prevalent primary malignant bone tumor, frequently exhibits chemotherapy resistance attributed to upregulated anti-apoptosis pathways such as the Bcl-2 family. In this manuscript, a new strategy is presented to augment chemosensitivity and mitigate systemic toxicity by harnessing a nano-enabled drug delivery hydrogel platform. The platform utilizes "PLGA-PEG-PLGA", an amphiphilic triblock copolymer combining hydrophilic polyethylene glycol (PEG) and hydrophobic polylactide glycolide (PLGA) blocks, renowned for its properties conducive to crafting a biodegradable, temperature-sensitive hydrogel. This platform is tailored to encapsulate a ratiometrically designed dual-loaded liposomes containing a first-line chemo option for CS, Doxorubicin (Dox), plus a calculated amount of small molecule inhibitor for anti-apoptotic Bcl-2 pathway, ABT-737. In vitro and in vivo evaluations demonstrate successful Bcl-2 suppression, resulting in the restoration of Dox sensitivity, evident through impeded tumor growth and amplified necrosis rates at the tumor site. This delivery system showcases remarkable thermal responsiveness, injectability, and biodegradability, all finely aligned with the clinical demands of CS treatment. Collectively, this study introduces a transformative avenue for tackling drug resistance in CS chemotherapy, offering significant clinical potential.
Subject(s)
Chondrosarcoma , Doxorubicin , Drug Delivery Systems , Drug Resistance, Neoplasm , Hydrogels , Nanocomposites , Chondrosarcoma/drug therapy , Chondrosarcoma/pathology , Chondrosarcoma/metabolism , Nanocomposites/chemistry , Animals , Drug Resistance, Neoplasm/drug effects , Hydrogels/chemistry , Doxorubicin/pharmacology , Doxorubicin/chemistry , Doxorubicin/therapeutic use , Drug Delivery Systems/methods , Humans , Cell Line, Tumor , Temperature , Polyethylene Glycols/chemistry , MiceABSTRACT
A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial-mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.
Subject(s)
Benchmarking , Models, Theoretical , Single-Cell Analysis/methods , Algorithms , Animals , Cellular Microenvironment , Data Analysis , Decision Trees , Gene Expression Profiling/methods , Humans , SpermatogenesisABSTRACT
MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD
Subject(s)
Biomedical Research , Biomarkers , Databases, Factual , MultiomicsABSTRACT
Three new strains of dissimilatory perchlorate-reducing bacteria (DPRB), QD19-16, QD1-5, and P3-1, were isolated from an active sludge. Phylogenetic trees based on 16S rRNA genes indicated that QD19-16, QD1-5, and P3-1 belonged to Brucella, Acidovorax, and Citrobacter, respectively, expanding the distribution of DPRB in the Proteobacteria. The three strains were gram-negative and facultative anaerobes with rod-shaped cells without flagella, which were 1.0-1.6 µm long and 0.5-0.6 µm wide. The three DPRB strains utilized similar broad spectrum of electron donors and acceptors and demonstrated a similar capability to reduce perchlorate within 6 days. The enzyme activity of perchlorate reductase in QD19-16 toward chlorate was higher than that toward perchlorate. The high sequence similarity of the perchlorate reductase operon and chlorite dismutase genes in the perchlorate reduction genomic islands (PRI) of the three strains implied that they were monophyletic origin from a common ancestral PRI. Two transposase genes (tnp1 and tnp2) were found in the PRIs of strain QD19-16 and QD1-5, but were absent in the strain P3-1 PRI. The presence of fragments of IR sequences in the P3-1 PRI suggested that P3-1 PRI had previously contained these two tnp genes. Therefore, it is plausible to suggest that a common ancestral PRI transferred across the strains Brucella sp. QD19-16, Acidovorax sp. QD1-5, and Citrobacter sp. P3-1 through horizontal gene transfer, facilitated by transposases. These results provided a direct evidence of horizontal gene transfer of PRI that could jump across phylogenetically unrelated bacteria through transposase. KEY POINTS: ⢠Three new DPRB strains can effectively remove high concentration of perchlorate. ⢠The PRIs of three DPRB strains are acquired from a single ancestral PRI. ⢠PRIs are incorporated into different bacteria genome through HGT by transposase.
Subject(s)
Genomic Islands , Perchlorates , Phylogeny , Oxidation-Reduction , Gene Transfer, Horizontal , RNA, Ribosomal, 16S/genetics , Bacteria/genetics , Oxidoreductases/genetics , Ecosystem , Transposases/geneticsABSTRACT
Superenhancers (SEs) are large genomic regions with a high density of enhancer marks. In cancer, SEs are found near oncogenes and dictate cancer gene expression. However, how oncogenic SEs are regulated remains poorly understood. Here, we show that INO80, a chromatin remodeling complex, is required for SE-mediated oncogenic transcription and tumor growth in melanoma. The expression of Ino80, the SWI/SNF ATPase, is elevated in melanoma cells and patient melanomas compared with normal melanocytes and benign nevi. Furthermore, Ino80 silencing selectively inhibits melanoma cell proliferation, anchorage-independent growth, tumorigenesis, and tumor maintenance in mouse xenografts. Mechanistically, Ino80 occupies >90% of SEs, and its occupancy is dependent on transcription factors such as MITF and Sox9. Ino80 binding reduces nucleosome occupancy and facilitates Mediator recruitment, thus promoting oncogenic transcription. Consistently, genes co-occupied by Ino80 and Med1 are selectively expressed in melanomas compared with melanocytes. Together, our results reveal an essential role of INO80-dependent chromatin remodeling in SE function and suggest a novel strategy for disrupting SEs in cancer treatment.
Subject(s)
Carcinogenesis/genetics , Cell Cycle Proteins/metabolism , Enhancer Elements, Genetic/physiology , Gene Expression Regulation, Neoplastic/genetics , Melanoma/genetics , Melanoma/physiopathology , Nuclear Proteins/metabolism , Animals , Cell Cycle Proteins/genetics , Cell Proliferation/genetics , Chromatin Assembly and Disassembly/genetics , Gene Silencing , Heterografts , Humans , Mediator Complex Subunit 1/genetics , Melanocytes/metabolism , Melanoma/enzymology , Mice , Nuclear Proteins/genetics , Protein Binding , Transcription Factors/metabolismABSTRACT
Land plants first evolved from freshwater algae, and flowering plants returned to water as early as the Cretaceous and multiple times subsequently. Alismatales is the largest clade of aquatic angiosperms including all marine angiosperms, as well as terrestrial plants. We used Alismatales to explore plant adaptations to aquatic environments by analyzing a data set that included 95 samples (89 Alismatales species) covering four genomes and 91 transcriptomes (59 generated in this study). To provide a basis for investigating adaptations, we assessed phylogenetic conflict and whole-genome duplication (WGD) events in Alismatales. We recovered a relationship for the three main clades in Alismatales as (Tofieldiaceae, Araceae) + core Alismatids. We also found phylogenetic conflict among the three main clades that was best explained by incomplete lineage sorting and introgression. Overall, we identified 18 putative WGD events across Alismatales. One of them occurred at the most recent common ancestor of core Alismatids, and three occurred at seagrass lineages. We also found that lineage and life-form were both important for different evolutionary patterns for the genes related to freshwater and marine adaptation. For example, several light- or ethylene-related genes were lost in the seagrass Zosteraceae, but are present in other seagrasses and freshwater species. Stomata-related genes were lost in both submersed freshwater species and seagrasses. Nicotianamine synthase genes, which are important in iron intake, expanded in both submersed freshwater species and seagrasses. Our results advance the understanding of the adaptation to aquatic environments and WGDs using phylogenomics.
Subject(s)
Alismatales , Magnoliopsida , Adaptation, Physiological/genetics , Alismatales/genetics , Biological Evolution , Magnoliopsida/genetics , Phylogeny , PlantsABSTRACT
BACKGROUND: Acute myeloid leukemia (AML) treatment remains challenging. CD70 was reported as a promising AML-specific antigen. Preclinically, CAR T-cell with single-chain-variable fragment (scFv) or truncated CD27 targeting CD70 has been reported to treat AML. However, various disadvantages including spontaneous exhaustion, proteinase-mediated loss of functional receptors, and high immunogenicity, limited its further application to clinical settings. Alternatively, the single-variable domain on heavy chain (VHH), also known as nanobodies, with comparable binding ability and specificity, provides an optional solution. METHOD: We generated CD70 knocked-out novel nanobody-based anti-CD70-CAR T-cells (nb70CAR-T) with two different VHHs for antigen detection. Next, we detected the CD70 expression on primary AML blasts by flow cytometry and associated the efficacy of nb70CAR-T with the target antigen density. Finally, epigenetic modulators were investigated to regulate the CD70 expression on AML cells to promote the functionality of nb70CAR-T. RESULTS: Our nb70CAR-T exhibited expected tumoricidal functionality against CD70-expressed cell lines and primary AML blasts. However, CD70 expression in primary AML blasts was not consistently high and nb70CAR-T potently respond to an estimated 40.4% of AML patients when the CD70 expression level was over a threshold of 1.6 (MFI ratio). Epigenetic modulators, Decitabine and Chidamide can up-regulate CD70 expression on AML cells, enhancing the treatment efficacy of nb70CAR-T. CONCLUSION: CD70 expression in AML blasts was not fully supportive of its role in AML targeted therapy as reported. The combinational use of Chidamide and Decitabine with nb70CAR-T could provide a new potential for the treatment of AML.
Subject(s)
Leukemia, Myeloid, Acute , Humans , Decitabine/pharmacology , Aminopyridines/metabolism , Immunotherapy, Adoptive , T-LymphocytesABSTRACT
BACKGROUND: Iris lactea var. chinensis, a perennial herbaceous species, is widely distributed and has good drought tolerance traits. However, there is little information in public databases concerning this herb, so it is difficult to understand the mechanism underlying its drought tolerance. RESULTS: In this study, we used Illumina sequencing technology to conduct an RNA sequencing (RNA-seq) analysis of I. lactea var. chinensis plants under water-stressed conditions and rehydration to explore the potential mechanisms involved in plant drought tolerance. The resulting de novo assembled transcriptome revealed 126,979 unigenes, of which 44,247 were successfully annotated. Among these, 1187 differentially expressed genes (DEGs) were identified from a comparison of the water-stressed treatment and the control (CK) treatment (T/CK); there were 481 upregulated genes and 706 downregulated genes. Additionally, 275 DEGs were identified in the comparison of the rehydration treatment and the water-stressed treatment (R/T). Based on Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) analysis, the expression levels of eight randomly selected unigenes were consistent with the transcriptomic data under water-stressed and rehydration treatment, as well as in the CK. According to Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, proline metabolism-related DEGs, including those involved in the 'proline catabolic process', the 'proline metabolic process', and 'arginine and proline metabolism', may play important roles in plant drought tolerance. Additionally, these DEGs encoded 43 transcription factors (TFs), 46 transporters, and 22 reactive oxygen species (ROS)-scavenging system-related proteins. Biochemical analysis and histochemical detection showed that proline and ROS were accumulated under water-stressed conditions, which is consistent with the result of the transcriptomic analysis. CONCLUSIONS: In summary, our transcriptomic data revealed that the drought tolerance of I. lactea var. chinensis depends on proline metabolism, the action of TFs and transporters, and a strong ROS-scavenging system. The related genes found in this study could help us understand the mechanisms underlying the drought tolerance of I. lactea var. chinensis.
Subject(s)
Iris Plant , Transcription Factors , Transcription Factors/genetics , Transcription Factors/metabolism , Iris Plant/genetics , Iris Plant/metabolism , Reactive Oxygen Species/metabolism , Drought Resistance , Stress, Physiological/genetics , Transcriptome , Gene Expression Profiling , Dehydration/genetics , High-Throughput Nucleotide Sequencing , Water/metabolism , Gene Expression Regulation, Plant , DroughtsABSTRACT
BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.
Subject(s)
MicroRNAs , Stomach Neoplasms , Humans , Stomach Neoplasms/genetics , MicroRNAs/genetics , Biomarkers, Tumor/genetics , Computational Biology , Artificial IntelligenceABSTRACT
BACKGROUND: Extramedullary disease usually implies a dismal outcome in relapsed/refractory multiple myeloma patients, and requires novel treatment approaches. We designed a trial using Selinexor, a nuclear export protein 1 inhibitor, together with anti-B cell maturation antigen (BCMA) chimeric antigen receptor (CAR)-T cell product CT103A to treat these patients, and describe the first two cases in this report. METHODS: Selinexor was administered with a novel two-step schedule in bridging therapy and in maintenance. The clinical responses and adverse events were recorded after CAR-T infusion and Selinexor administration. In vitro analysis of the influence of Selinexor on CAR-T cell function was performed using myeloma cell lines. RESULTS: After infusion, both patients achieved stringent complete remission (sCR), and were maintained in sCR at data-cutoff, with survival over 13 and 10 months, respectively. Neither immune effector cell-associated neurotoxicity syndrome nor over grade 2 cytokine release syndrome was observed. Meanwhile, the patients showed good tolerance to the combination. In addition, we demonstrated that low dose of Selinexor could upregulate the expression of BCMA on plasma cell lines and subsequently enhance the function of CAR-T cell in vitro. CONCLUSIONS: The combination of Selinexor and CT103A exerts preliminary synergistic effect, and can be developed as a promising strategy for relapsed/refractory extramedullary myeloma.
Subject(s)
Multiple Myeloma , Receptors, Chimeric Antigen , Humans , Multiple Myeloma/drug therapy , Receptors, Chimeric Antigen/therapeutic use , Receptors, Chimeric Antigen/metabolism , B-Cell Maturation Antigen/metabolism , Antibodies/therapeutic use , Plasma Cells , Immunotherapy, AdoptiveABSTRACT
Apocynaceae are one of the ten species-richest angiosperm families. However, the backbone phylogeny of the family is yet less well supported, and the evolution of plastome structure has not been thoroughly studied for the whole family. Herein, a total of 101 complete plastomes including 35 newly sequenced, 24 reassembled from public raw data and the rest from the NCBI GenBank database, representing 26 of 27 tribes of Apocynaceae, were used for comparative plastome analysis. Phylogenetic analyses were conducted using a combined plastid data matrix of 77 protein-coding genes from 162 taxa, encompassing all tribes and 41 of 49 subtribes of Apocynaceae. Plastome lengths ranged from 150,897 bp in Apocynum venetum to 178,616 bp in Hoya exilis. Six types of boundaries between the inverted repeat (IR) regions and single copy (SC) regions were identified. Different sizes of IR expansion were found in three lineages, including Alyxieae, Ceropegieae and Marsdenieae, suggesting multiple expansion events of the IRs over the SC regions in Apocynaceae. The IR regions of Marsdenieae evolved in two ways: expansion towards the large single copy (LSC) region in Lygisma + Stephanotis + Ruehssia + Gymnema (Cosmopolitan clade), and expansion towards both LSC and small single copy (SSC) region in Dischidia-Hoya alliance and Marsdenia (Asia-Pacific clade). Six coding genes and five non-coding regions were identified as highly variable, including accD, ccsA-ndhD, clpP, matK, ndhF, ndhG-ndhI, trnG(GCC)-trnfM(CAU), trnH(GUG)-psbA, trnY(GUA)-trnE(UUC), ycf1, and ycf2. Maximum likelihood and Bayesian phylogenetic analyses resulted in nearly identical tree topologies and produced a well-resolved backbone comprising 15 consecutive dichotomies that subdivided Apocynaceae into 15 clades. The subfamily Periplocoideae were embedded in the Apocynoid grade and were sister to the Echiteae-Odontadenieae-Mesechiteae clade with high support values. Three tribes (Melodineae, Vinceae, and Willughbeieae), the subtribe Amphineuriinae, and four genera (Beaumontia, Ceropegia, Hoya, and Stephanotis) were not resolved as monophyletic. Our work sheds light on the backbone phylogenetic relationships in the family Apocynaceae and offers insights into the evolution of Apocynaceae plastomes using the most densely sampled plastome dataset to date.
Subject(s)
Apocynaceae , Magnoliopsida , Humans , Phylogeny , Apocynaceae/genetics , Bayes Theorem , Evolution, Molecular , Magnoliopsida/geneticsABSTRACT
B-cell maturation antigen (BCMA)-specific chimeric antigen receptor (CAR) T-cell therapies have shown efficacy in relapsed/refractory multiple myeloma (RRMM). Because the non-human originated antigen-targeting domain may limit clinical efficacy, we developed a fully human BCMA-specific CAR, CT103A, and report its safety and efficacy in a phase 1 trial. Eighteen consecutive patients with RRMM, including 4 with prior murine BCMA CAR exposures, were enrolled. CT103A was administered at 1, 3, and 6 × 106 CAR-positive T cells/kg in the dose-escalation phase, and 1 × 106 CAR-positive T cells/kg in the expansion cohort. The overall response rate was 100%, with 72.2% of the patients achieving complete response or stringent complete response. For the 4 murine BCMA CAR-exposed patients, 3 achieved stringent complete response, and 1 achieved a very good partial response. At 1 year, the progression-free survival rate was 58.3% for all cohorts and 79.1% for the patients without extramedullary myeloma. Hematologic toxicities were the most common adverse events; 70.6% of the patients experienced grade 1 or 2 cytokine release syndromes. No immune effector cell-associated neurotoxicity syndrome was observed. To the cutoff date, CAR transgenes were detectable in 77.8% of the patients. The median CAR transgene persistence was 307.5 days. Only 1 patient was positive for the anti-drug antibody. Altogether, CT103A is safe and highly active in patients with RRMM and can be developed as a promising therapy for RRMM. Patients who relapsed from prior murine BCMA CAR T-cell therapy may still benefit from CT103A. This trial was registered at http://www.chictr.org.cn as #ChiCTR1800018137.
Subject(s)
B-Cell Maturation Antigen/antagonists & inhibitors , Immunotherapy, Adoptive , Multiple Myeloma/therapy , Receptors, Chimeric Antigen/therapeutic use , Single-Chain Antibodies/therapeutic use , Adult , Afibrinogenemia/etiology , Aged , Animals , Antibodies, Anti-Idiotypic/biosynthesis , Antineoplastic Agents/therapeutic use , B-Cell Maturation Antigen/immunology , Combined Modality Therapy , Drug Resistance, Neoplasm , Female , Hematologic Diseases/etiology , Humans , Immunity, Humoral , Immunotherapy, Adoptive/adverse effects , Leukemia, Plasma Cell/etiology , Leukemia, Plasma Cell/therapy , Male , Mice , Middle Aged , Multiple Myeloma/drug therapy , Receptors, Chimeric Antigen/administration & dosage , Receptors, Chimeric Antigen/immunology , Remission Induction , Single-Chain Antibodies/immunology , TransgenesABSTRACT
Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.