Biology subjects

Peng, H.

Publications and source records attributed to Peng, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

DyNDG: Identifying Leukemia-Related Genes based on the Time-Series Dynamical Network by Integrating Differential Genes

Leukemia is a malignant disease of progressive accumulation characterized by high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed DyNDG, a novel Dynamic Network-based model, which integrates Differentially Expressed Genes to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The experimental results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at https://github.com/CSUBioGroup/DyNDG.

bioinformatics↗

bioRxiv2024

Reprogramming the genome of M13 bacteriophage for all-in-one personalized cancer vaccine

Peptide-based vaccines face limitations in immunogenicity and stability, and challenges in co-delivering antigens and adjuvants effectively. Virus-based nanoparticles, particularly M13 bacteriophage, present a promising solution due to their genetic modifiability, intrinsic adjuvanticity, and efficient antigen presentation capabilities. Here we developed a programmable M13 phage-based personalized cancer vaccine enabling single-step antigen-adjuvant assembly. Specifically, we designed a reprogrammed (RP) phage platform that precisely regulates Toll-like receptor 9 activation by programming its genome sequence and modulates antigen density through genetic engineering. Vaccination studies with RP phages demonstrated that the immune response could be modulated by fine-tuning the adjuvanticity and antigen density, revealing an optimal antigen dose and adjuvanticity for maximum vaccine efficacy. The RP phage induced a remarkable 24-fold increase in neoantigen-specific CD8+ T cells and eradicated established MC-38 tumors when combined with anti-PD-1 therapy. These findings highlight the RP phages potential as a powerful nanovaccine platform for personalized cancer vaccines.

bioengineering↗

bioRxiv2024

Expression study of Krabbe Disease GALC missense variants -- Insights from quantification profiles of residual enzyme activity, secretion and psychosine levels

Krabbe disease (KD) is an autosomal recessive lysosomal storage disorder caused by loss-of-function mutations in the GALC gene, which encodes for the enzyme galactosylceramidase (GALC). GALC is crucial for myelin metabolism. Functional deficiency of GALC leads to toxic accumulation of psychosine, dysfunction and death of oligodendrocytes, and eventual brain demyelination. To date, 46 clinically-relevant, pathogenic GALC missense mutations (MMs) have been identified in KD patients. These MMs are present in [~]70% of KD cases reported over 8 published studies between 1996 - 2019. However, the mechanisms by which these MMs lead to GALC functional deficiency and their correlations with clinical phenotype remain poorly understood. To address this, we generated a GALC-knockout human oligodendrocytic cell line (MO3.13/ GALC-KO) using CRISPR-Cas9 method to assess GALC function and GALC secretion. We evaluated 5 polymorphic and 31 clinically-relevant MM variants (MMVs) using transient expression assays. Our results showed that 26 MMVs, including 10 co-variants with p.I562T, reduced GALC activity by 92% - 100% compared to wild-type GALC (WT-GALC). MMVs from infantile-onset KD patients produced < 2% of WT activity, whereas those associated with juvenile- and adult-onset cases retained up to 7% of WT activity. Residual GALC activity was correlated with mature, lysosomal GALC protein levels (Pearson r = 0.93, P<0.0001). Many low-activity MMVs did not correspondingly impair GALC secretion. Twenty-one of the 26 low-activity MMVs showed a 21% - 100% reduction in sec-GALC levels, indicating varying degrees of GALC mis-trafficking among these variants. Importantly, GALC activity among MMVs strongly correlates with clinical disease severity, based on the age of symptom onset in patients with either homozygous MM (Pearson r = 0.98, P<0.0001, n = 7) or compound heterozygous (Pearson r = 0.94, P<0.0001, n = 12) MM-null mutation genotypes. Thus, our data suggests that GALC activity could serve as a prognostic disease indicator under specific experimental conditions. We further investigated the impact of pathogenic MMVs on psychosine accumulation, a key biomarker for KD. Psychosine levels were 21-fold higher in mock control cells compared to WT-GALC transfected cells (mock = 0.349 pmol/mg, WT-GALC = 0.016 pmol/mg), but negatively correlated with GALC activity among pathogenic MMVs (Pearson r = -0.63, P < 0.01, n = 15). Although psychosine levels were higher in most MMVs associated with infantile-onset KD, no significant correlations with clinical onset were detected. Overall, our study provides a comprehensive quantitative analysis of the functional deficits and mis-trafficking associated with clinically-relevant GALC MMVs, enhancing our understanding of the molecular genetics and genotype-phenotype correlations of the GALC gene in Krabbe disease.

cell biology↗

bioRxiv2024

Generating brain-wide connectome using synthetic axonal morphologies

Recent experimental advancements, including electron microscopy reconstructions, have produced detailed connectivity data for local brain regions. On the other hand, for inter-regional connectivity, large-scale imaging techniques such as MRI are best suited to provide insights. However, understanding the relationship between local and long-range connectivity is essential for studying both healthy and pathological conditions of the brain. Leveraging a novel dataset of whole-brain axonal reconstructions, we present a technique to predict whole-brain connectivity at single cell level by generating detailed whole-brain axonal morphologies from sparse experimental data. The computationally generated axons accurately reproduce the local and global morphological properties of experimental reconstructions. Furthermore, the computationally synthesized axons generate large-scale inter-regional connectivity, defining the projectome and the connectome of the brain, thereby enabling the in silico experimentation of large brain regions.

neuroscience↗

bioRxiv2024

Reconstruct a Connectome of Single Neurons in Mouse Brains by Cross-Validating Multi-Scale Multi-Modality Data

Brain networks, often referred to as connectomes, are crucial to findings in brain science and have inspired the development of numerous technologies at macro-, meso-, and micro-scales. With the growing prevalence of single-cell analysis, there is an urgency to generate connectomes of individual neurons that project to brain-wide regions. This work reports a scalable approach to build neuron connection networks of mouse brains by mapping whole-brain single-neuron connectivity using two complementary methods at sub-neuronal levels. We first generated an arbor-net by partitioning and probabilistically pairing dendritic and axonal arbors of 20,247 neurons registered to the Allen Brain Atlas. We also produced a bouton-net based on 2.57 million putative boutons detected along single axons of 1,877 fully reconstructed neurons and probabilistic pairing of these full-morphology datasets. Our cross-validation of both the arbor-net and bouton-net showed statistical consistency in the spatially and anatomically modular distributions of neuronal connections, which also corresponded to functional modules of the mouse brain. Our single-neuron connections were also validated by two existing independent connectomes of coarser resolution based on viral tracing of neuron populations and barcoding-and-sequencing, as well as an independent synaptome containing the relative density distribution of synapses. We further found that single neuronal connections correlated more strongly with gene co-expression at both the brain region and single-cell levels than the previous full-brain mesoscale connectome of a mouse brain. Our findings allow the assembly of a new whole-brain scale single-neuron resolution connectome for all brain regions, called SEU-net. We studied the properties of our connectomes in comparison with other potential brain architectures and found a series of non-random subnetwork patterns in the form of consistent triad motifs. Overall, our data indicate a rich granularity and strong modular diversity in the brain networks of mice.

neuroscience↗

bioRxiv2024

Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry

We introduce NeuroXiv (neuroxiv.org), a large-scale, AI-powered database that provides detailed 3D morphologies of individual neurons mapped to a standard brain atlas, designed to support a wide array of dynamic, interactive neuroscience applications. NeuroXiv offers a comprehensive collection of 175,149 atlas-oriented reconstructed morphologies of individual neurons derived from more than 518 mouse brains, classified into 292 distinct types and mapped into the Common Coordinate Framework Version 3 (CCFv3). Different from conventional static brain atlases that are often limited to data-browsing, NeuroXiv allows interactive analyses as well as uploading and databasing custom neuron morphologies, which are mapped to the brain atlas for objective comparisons. Powered by a cutting-edge AI engine (AIPOM), NeuroXiv enables dynamic, user-specific analysis and data mining. We specifically developed a mixture-of-experts algorithm to harness the capabilities of multiple large language models. We also developed a client program to achieve more than 10 times better performance compared to a typical server-side setup. We demonstrate NeuroXivs scalability, efficiency, flexibility, openness, and robustness through various applications.

bioinformatics↗

bioRxiv2024

Identification of Intrinsic Features for Cortical Separability of Human and Mouse Neurons

This work introduces a novel framework for holistic comparative analysis of cortical regions in mouse and human brains at single-neuron resolution, with a primary focus on the morphological and molecular characteristics of neurons. To do so, we generated one of the largest dendritic reconstruction datasets of cortical neurons to date, comprising 2,363 human neurons and 16,011 mouse neurons from the frontal, parietal, and temporal lobes, followed by establishing a rigorous procedure to identify anatomically and functionally corresponding brain regions with minimal variability in brain mapping. Additionally, we leveraged single nucleus/cell transcriptomic data from independent groups to validate the molecular correspondence of the brain regions identified in this study. The significance of these anatomically, functionally, and molecularly corresponding mouse-human region pairs is highlighted by examining the intrinsic features of their respective cortical regions. Our findings reveal that human neuron branching patterns differ dramatically from those in mouse brains, particularly in terms of dendritic branching frequency and normalized dendritic branching intervals. This difference is pronounced in the frontal and temporal lobes, underscoring the distinct neuronal architectures between the two species. At the single-neuron level, we found that neurons from the human frontal and parietal lobes are six times more separable than those from the same regions in mouse brains. This heightened separability is also observed between the frontal and temporal lobes, as well as between the parietal and temporal lobes in humans. We thoroughly explored the entire morphological feature space, along with its characteristic subspaces, and consistently found this distinct separability. Remarkably, this neuronal separability can be partially recapitulated when examining the global functional states of these brain lobes--using newly acquired Electroencephalography (EEG) and Magnetoencephalography (MEG) signals as physiological measures--as well as their global metabolic states, molecular profiles, and cortical geometry. These findings suggest that our comparative analysis of single-neuron intrinsic features could serve as a valuable foundation for future comprehensive studies of cross-species brain structures and functions.

neuroscience↗

bioRxiv2024

Constructing a Mouse Brain Atlas of Dendritic Microenvironments Helps Discover Hidden Associations Between Anatomical Layout, Projection Targets and Transcriptomic Profiles of Neurons

Digital brain atlases have become essential anatomical references for understanding the spatial and functional organization of brains. For mice, typical resources include the Allen Reference Atlas, the Allen Common Coordinate Framework (CCF), and their variants, like CCFv3. However, previous whole-brain atlases were constructed based on limited neuronal features, such as cell body (soma) density or average maps from collections of registered brain images, without considering the spatial organization of neuronal arbors. This study introduces a microenvironment representation that incorporates the morphological features of neighboring neurons to better quantify brain modularity. We generated a large dataset containing dendrites from 101,136 neurons across 111 mouse brains, covering 91% of non-ventricular, non-fiber-tract CCF regions, and constructed a multidimensional microenvironment feature map of the whole brain. Our findings reveal that the spatial organization of these microenvironments outperforms the CCFv3 and a state-of-the-art spatial transcriptomic cell atlas by providing complementary subregions within established regions, nearly doubling the total number of brain regions compared to CCFv3. In this way, our atlas enables the identification of previously unobserved neuron groupings or "subtypes". Our results also demonstrate that this microenvironment atlas enhances local spatial homogeneity while maintaining spatial differentiation within established CCF brain regions. For example, we found that the microenvironments of hippocampal neurons are correlated with axonal projection targets and improve the specificity of projection mapping, which implies the potential characterization of long-range axonal projections of mammalian neurons based on only local dendritic organization. The sub-parcellation of the caudoputamen (CP) aligns well with previous studies on projections, connectivity, and transcriptomics, revealing diverse input and output wiring patterns among CP subregions.

neuroscience↗

bioRxiv2024

Tracing the genetic diversity of the bread wheat D genome

Bread wheat (Triticum aestivum) became a globally dominant crop after incorporating the D genome from donor species Aegilops tauschii, while evolutionary history shaping the D genome during this process remains elusive. Here, we proposed a renewed evolutionary model linking Ae. tauschii and hexaploid wheat D genome, based on an ancestral haplotype map covering a total of 762 Ae. tauschii and hexaploid wheat accessions. We dissected the evolutionary process of Ae. tauschii lineages and clarified L3 as the most ancient lineage. A few independent intermediate accessions were reported, demonstrating the low-frequent inter-sublineage geneflow enriched the diversity of Ae. tauschii. We discovered that the D genome of hexaploid wheat inherited from a unified ancestral template, but with a mosaic composition that is highly mixed by three Ae. tauschii L2 sublineages located in the Caspian coastal region, suggesting the early agricultural activities facilitate the innovation of D genome compositions that finalized the success of hexaploidization. We further found that the majority (65.6%) of polymorphisms were attributed to novel mutations absent during the spreading of bread wheat, and also identified large Ae. tauschii introgressions from wild Aegilops lineages, expanding the diversity of wheat D genome and introducing beneficial alleles. This work decoded the mystery of the wheat hexaploidization process and the evolutionary significance of the multi-layered origins of the genetic diversity of the bread wheat D genome.

evolutionary biology↗

bioRxiv2024

Efficient generation of CAR NK cells from human umbilical cord blood CD34+ stem and progenitors for democratizing affordable immunotherapy

Chimeric antigen receptor (CAR) natural killer cells (CAR NK) cells, leveraging safety and not requiring HLA match in adoptive infusion, have emerged as promising alternative cells to CAR-T cells for immunotherapies. High and multiple doses of CAR NK cell infusions are essential to maintain therapeutic efficacy in clinical trials. This requires efficient methods for generating large-scale CAR NK cells and significantly reducing CAR engineering costs. In this study, we develop a three-step strategy to generate highly high yields of induced NK (iNK) and CAR iNK cells from human umbilical cord blood CD34+ hematopoietic stem and progenitor cells (CD34+ HSPCs). Starting from a single umbilical cord blood CD34+ HSPC, our reliable method efficiently produces 14-83 million mature iNK cells or 7-32 million CAR iNK cells with high expression levels of CD16 and zero T cell contaminations. Introducing CAR expression elements at the HSPC level reduces the quantities of CAR pseudoviruses to 1 / 140.000 - 1 / 600,000 compared to engineering CARs in mature NK cells. The iNK and CAR iNK cells, including fresh cells and thawed cells from cryopreserved conditions, demonstrate remarkable tumoricidal activities against various human cancer cells and significantly prolong the survival of human tumor-bearing animals. The high yields of CAR NK cells and negligible costs of CAR engineering of our method support the broad applications of CAR NK cells for treating cancer patients.

cell biology↗

bioRxiv2024

VirID: Beyond Virus Discovery - An Integrated Platform for Comprehensive RNA Virus Characterization

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase (RdRP) database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identity RNA viruses of all type, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks.

bioinformatics↗

bioRxiv2024

pH-Controlled chemoselective rapid azo-coupling reaction (CRACR) enables global profiling of serotonylation proteome in cancer cells

Serotonylation has been identified as a novel protein post-translational modification for decades, where an isopeptide bond is formed between the glutamine residue and serotonin through transamination. Transglutaminase 2 (also known as TGM2 or TGase2) was proven to act as the main "writer" enzyme for this PTM and a number of key regulatory proteins (including small GTPases, fibronectin, fibrinogen, serotonin transporter, and histone H3) have been characterized as the substrates of serotonylation. However, due to the lack of pan-specific antibodies for serotonylated glutamine, the precise enrichment and proteomic profiling of serotonylation still remain challenging. In our previous research, we developed an aryldiazonium probe to specifically label protein serotonylation in a bioorthogonal manner, which depended on a pH-controlled chemoselective rapid azo-coupling reaction (CRACR). Here, we report the application of a photoactive aryldiazonium-biotin probe for the global profiling of serotonylation proteome in cancer cells. Thus, over 1,000 serotonylated proteins were identified from HCT 116 cells, many of which are highly related to carcinogenesis. Moreover, a number of modification sites of these serotonylated proteins were determined, attributed to the successful application of our chemical proteomic approach. Overall, these findings provided new insights into the significant association between cellular protein serotonylation and cancer development, further suggesting that to target TGM2-mediated monoaminylation may serve as a promising strategy for cancer therapeutics. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/593574v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@67e538org.highwire.dtl.DTLVardef@f9f722org.highwire.dtl.DTLVardef@cfe5fdorg.highwire.dtl.DTLVardef@104674f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Chemical proteomic profiling of protein dopaminylation in colorectal cancer cells

Histone dopaminylation is a newly identified epigenetic mark that plays a role in the regulation of gene transcription, where an isopeptide bond is formed between the fifth amino acid residue of H3 (i.e., glutamine) and dopamine. In our previous studies, we discovered that the dynamics of this post-translational modification (including installation, removal, and replacement) were regulated by a single enzyme, transglutaminase 2 (TGM2), through reversible transamination. Recently, we developed a chemical probe to specifically label and enrich histone dopaminylation via bioorthogonal chemistry. Given this powerful tool, we found that histone H3 glutamine 5 dopaminylation (H3Q5dop) was highly enriched in colorectal tumors, which could be attributed to the high expression level of TGM2 in colon cancer cells. Due to the enzyme promiscuity of TGM2, non-histone proteins have also been identified as targets of dopaminylation on glutamine residues, however, the dopaminylated proteome in cancer cells still remains elusive. Here, we utilized our chemical probe to enrich dopaminylated proteins from colorectal cancer cells in a bioorthogonal manner and performed the chemical proteomics analysis. Therefore, 425 dopaminylated proteins were identified, many of which are involved in nucleic acid metabolism and transcription pathways. More importantly, a number of modification sites of these dopaminylated proteins were identified, attributed to the successful application of our chemical probe. Overall, these findings shed light on the significant association between cellular protein dopaminylation and cancer development, further suggesting that to block the installation of protein dopaminylation may become a promising anti-cancer strategy. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/591460v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1a3e9dorg.highwire.dtl.DTLVardef@104ecddorg.highwire.dtl.DTLVardef@5ab948org.highwire.dtl.DTLVardef@3a5d8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Turn `noise' to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances

Although the per-base erring rate of NGS is very low at 0.1% to 0.5%, the percentage/probability of erroneous reads in a short-read sequencing dataset can be as high as 10% to 15% or in the number of millions. Correction of these wrongly sequenced reads to retrieve their huge missing value will improve many downstream applications. As current methods correct only some of the errors at the cost of introducing many new errors, we solve this problem by turning erroneous reads into their original states, without bringing up any non-existing reads to keep the data integrity. The novelty of our method is originated in a computable rule translated from PCR erring mechanism that: a rare read is erroneous if it has a neighbouring read of high abundance. With this principle, we construct a graph to link every pair of reads of tiny edit distances to detect a solid part of erroneous reads; then we consider them as training data to learn the erring mechanisms to identify possibly remaining hard-case errors between pairs of high-abundance reads. Compared with state-of-the-art methods on tens of datasets of UMI-based ground truth, our method has made a remarkably better performance under 19 metrics including two entropy metrics that measure noise levels in a dataset. Case studies found that our method can make substantial impact on genome abundance quantification, isoform identification, SNP profiling, and genome editing efficiency estimation. For example, the abundance level of the reference genome of SARS-CoV-2 can be increased by 12% and that of Monkeypox can be boosted by 52.12% after error correction. Moreover, the number of distinct isomiRs is decreased by 31.56%, unveiling there are so many previously identified isomiRs that are actually sequencing errors. Author summaryDetecting short-read sequencing errors and correcting the related erroneous reads is a long-standing problem in bioinformatics. Current error correction algorithms correct only small parts of the errors but simultaneously introduce thousands of non-existing sequences. We present a new method to rectify erroneous reads under 300 bp produced by PCR-involved miRNA-sequencing, small RNA sequencing, or paired-end RNA sequencing, regardless of platform or sample type. Our method is the first kind considering the PCR erring mechanism and machine learning technique to improve sequencing data quality by turning millions of erroneous short reads into their original state without bringing up any non-existing sequences into the read set. Our error correction method can make a significant impact on a wide range of cutting-edge downstream applications. The observations and advantages in the case studies lay down strong evidence to question the accuracies of current downstream research outcomes and open new avenues to conduct downstream analysis whenever short-read data are adopted.

bioinformatics↗

bioRxiv2024

Bioorthogonal labeling and enrichment of histone monoaminylation reveal its accumulation and regulatory function in cancer cell chromatin

Histone monoaminylation (i.e., serotonylation and dopaminylation) is an emerging category of epigenetic mark occurring on the fifth glutamine (Q5) residue of H3 N-terminal tail, which plays significant roles in gene transcription. Current analysis of histone monoaminylation is mainly based on site-specific antibodies and mass spectrometry, which either lacks high resolution or is time-consuming. In this study, we report the development of chemical probes for bioorthogonal labeling and enrichment of histone serotonylation and dopaminylation. These probes were successfully applied for the monoaminylation analysis of in vitro biochemical assays, cells, and tissue samples. The enrichment of monoaminylated histones by the probes further confirmed the crosstalk between H3Q5 monoaminylation and H3K4 methylation. Finally, combining the ex vivo and in vitro analyses based on the developed probes, we have shown that both histone serotonylation and dopaminylation are highly enriched in tumor tissues that overexpress transglutaminase 2 (TGM2) and regulate the three-dimensional architecture of cellular chromatin. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/586010v3_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1bee3f7org.highwire.dtl.DTLVardef@1687013org.highwire.dtl.DTLVardef@1960bd0org.highwire.dtl.DTLVardef@1e19a04_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Early cytokine-driven adaptation of survival pathways in lymphoid cells during targeted therapies

Venetoclax, a first-in-class BH3 mimetic drug targeting BCL-2, has improved outcomes for patients with chronic lymphocytic leukemia (CLL). Early measurements of the depth of the venetoclax treatment response, assessed by minimal residual disease, are strong predictors of long-term clinical outcomes. Yet, there are limited data concerning the early changes induced by venetoclax treatment that might inform strategies to improve responses. To address this gap, we conducted longitudinal mass cytometric profiling of blood cells from patients with CLL during the first two months of venetoclax monotherapy. At baseline, we resolved CLL heterogeneity at the single-cell level to define multiple subpopulations in all patients distinguished by proliferative, metabolic and cell survival proteins. Venetoclax induced significant reduction in all CLL subpopulations coincident with rapid upregulation of pro-survival BCL-2, BCL-XL and MCL-1 proteins in surviving cells, which had reduced sensitivity to the drug. Mouse models recapitulated the venetoclax-induced elevation of survival proteins in B cells and CLL-like cells that persisted in vivo, with genetic models demonstrating that extensive apoptosis and access to the B cell cytokine, BAFF, were essential. Accordingly, analysis of patients with CLL that were treated with a different targeted therapy, the anti-CD20 antibody obinutuzumab, also exhibited marked elevation of BAFF and increased pro-survival proteins in leukemic cells that persisted. Overall, these data highlight the rapid adaptation of CLL cells to targeted therapies via homeostatic factors and support co-targeting of cytokine signals to achieve deeper and more durable long-term responses. Key pointsO_LILeukaemic cells rapidly adapt to targeted therapy by elevating pro-survival protein expression. C_LIO_LICell attrition and increased bioavailability of homeostatic cytokines drive this heightened survival, highlighting avenues for more potent combination therapies. C_LI

cancer biology↗

bioRxiv2024

Systematic annotation of Helitron-like elements in eukaryote genomes using HELIANO

Helitron-like elements (HLEs) are widespread eukaryotic DNA transposons employing a rolling-circle transposition mechanism. Despite their prevalence in fungi, animals, and plant genomes, identifying Helitrons remains challenging. We introduce HELIANO, a software for annotating and classifying autonomous and non-autonomous Helitron and Helentron sequences from whole genomes. HELIANO outperforms existing tools in speed and accuracy, demonstrated through benchmarking and its application to complex genomes (Xenopus tropicalis, Xenopus laevis, Oryza sativa), revealing numerous newly identified Helitrons and Helentrons. In a comprehensive analysis of 404 eukaryote genomes, we found HLEs widely distributed across phyla, with exceptions in specific taxa. Helentrons were identified in numerous land plant species, and 20 protein domains were discovered integrated within specific autonomous HLE families. A global phylogenetic analysis confirmed the classification into main clades Helentron and Helitron, revealing nine subgroups, some enriched in particular taxa. The future use of HELIANO will contribute to the global analysis of TEs across genomes and enhance our understanding of this transposon superfamily.

evolutionary biology↗

bioRxiv2024

Comparative Metagenomics Provide Mechanistic Insights into the Biodegradation of the Non-hydrolysable Plastic Polyvinyl Chloride in Gut Microbiota of Insect Larvae

Using microbiomes to mitigate global plastic pollution is of paramount importance. Insect microbiomes have garnered emerging interest for their ability to biodegrade non-hydrolysable plastic polymers. The larvae of Spodoptera frugiperda, a globally prevalent migratory crop pest, are accidentally discovered to consume polyvinyl chloride (PVC) plastic films, highlighting the role of their gut microbiome. Following the migration of S. frugiperd in China, this study displays a comprehensive geographical profile of its larval gut microbiota and a significant shift after PVC feeding. Using comparative metagenomics we revealed the functional redundancy within the larval gut microbiomes of two distinct insects after PVC ingestion, we discovered a surprisingly potent PVC-dechlorinating activity of an NADH peroxidase (6.48 mg/L chlorine produced in 96 hours with NADH as a cofactor) encoded by Enterococcus casseliflavus EMBL-3. These findings open a new avenue for understanding plastic biodegradation mechanism and enable the development of biotechnologies to mitigate global plastic pollution.

microbiology↗