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Zheng, D.

Publications and source records attributed to Zheng, D..

At least 73 records · Page 4Linked to original sources

An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide conjugated antibodies

BackgroundSingle-cell technologies to analyze transcription and chromatin structure have been widely used in many research areas to reveal the functions and molecular properties of cells at single-cell resolution. Sample multiplexing techniques are valuable when performing single-cell analysis, reducing technical variation and permitting cost efficiencies. Several commercially available methods are available and have been used in many scRNA-seq studies. On the other hand, while several methods have been published, the multiplexing techniques for single nuclear Assay for Transposase-Accessible Chromatin (snATAC)-seq assays remain under development. We developed a simple nucleus hashing method using oligonucleotide conjugated antibodies recognizing nuclear pore complex proteins, NuHash, to perform snATAC-seq library preparations by multiplexing. ResultsWe performed multiplexing snATAC-seq analyses on the mixture of human and mouse cell samples (two samples, 2-plex, and four samples, 4-plex) using NuHash. The demultiplexing accuracy of NuHash was high, and only ten out of 9,144 nuclei (2-plex) and 150 of 12,208 nuclei (4-plex) had discordant classifications between NuHash demultiplexing and discrimination using reference genome alignments. We compared results between snATAC-seq and deeply sequenced bulk ATAC-seq on the same samples and found that most of the peaks detected in snATAC-seq were also detected in deeply sequenced bulk ATAC-seq. The bulk ATAC-seq signal intensity was positively correlated with the number of cell subtype clusters detected in snATAC-seq, but not the subset of peaks detected in all clusters. These subsets of snATAC-seq peaks showed different distributions over different genomic features, suggesting that the peak intensities of bulk ATAC-seq can be used to identify different types of functional loci. ConclusionsOur multiplexing method using oligo-conjugated anti-nuclear pore complex proteins, NuHash, permits high accuracy demultiplexing of samples. The NuHash protocol is straightforward, it works on frozen samples, and requires no modifications for snATAC-seq library preparation.

genomics↗

Ex vivo intestinal permeability assay (X-IPA) for tracking barrier function dynamics

The intestinal epithelial barrier facilitates homeostatic host-microbiota interactions and immunological tolerance. However, mechanistic dissections of barrier dynamics following luminal stimulation pose a substantial challenge. Here, we describe an ex-vivo intestinal permeability assay, X-IPA, for quantitative analysis of gut permeability dynamics at the whole-tissue level. We demonstrate that specific gut microbes and metabolites induce rapid, dose-dependent increases to gut permeability, thus providing a powerful approach for precise investigation of barrier functions.

microbiology↗

Junctophilin-2 promotes cardiomyocyte survival by blocking MURF1-mediated Junctin ubiquitination and proteasome-dependent degradation

AimsJunctophilin-2 is required for the development, maturation and integrity of the t-tubule system and the gating stability of RyR2 in cardiomyocytes. This study investigated whether and how junctophilin-2 maintained junctin, a scaffold protein stabilizing RyR2, to prevent cardiomyocyte death under stress. MethodsCardiomyocytes were exposed to conditions of stress including palmitate, doxorubicin, or hypoxia/re-oxygenation. Adenoviral vectors were employed to manipulate expression of junctophilin-2 and junctin in cardiomyocytes. Molecular/cellular/biochemical analyses were conducted. ResultsDifferent conditions of stress decreased junctophilin-2 expression through aberrant autophagy and concomitantly induced a reduction of junctin protein in cardiomyocytes. Over-expression of junctophilin-2 preserved the protein levels of junctin and attenuated cytosolic Ca2+ and apoptosis in cardiomyocytes under stress. Knockdown of junctophilin-2 reproduced the detrimental phenotypes of stress in cardiomyocytes. Notably, over-expression of junctin prevented cardiomyocyte death under stress whereas knockdown of junctin offset the protective effects conferred by junctophilin-2 over-expression. Mechanistically, junctophilin-2 blocked MURF1-junctin interaction thereby preventing junctin ubiquitination and proteasome-dependent degradation. Mass spectrometry analysis identified multiple ubiquitination sites on the junctin protein and the non-ubiquitinated junctin mutant (K8A/K102A/K107A/K140A) was resistant to degradation. ConclusionsThis study uncovers an unrecognized role of junctophilin-2 in preventing junctin ubiquitination and degradation in maintaining cytosolic Ca2+ homeostasis. Both junctophilin-2 and junctin represent two new survival factors of cardiomyocytes and thus, may be new therapeutic targets for cardiac protection.

molecular biology↗

Single cell transcriptomics uncovers a non-autonomous Tbx1-dependent genetic program controlling cardiac neural crest cell deployment and progression

Disruption of cardiac neural crest cells (CNCCs) results in congenital heart disease, yet we do not understand the cell fate dynamics as these cells differentiate to vascular smooth muscle cells. Here we utilized single-cell RNA-sequencing of NCCs from the pharyngeal apparatus with heart in control mouse embryos and when Tbx1, the gene for 22q11.2 deletion syndrome, is inactivated. We uncovered three dynamic transitions of pharyngeal NCCs expressing Tbx2 and Tbx3 through differentiated CNCCs expressing cardiac transcription factors with smooth muscle genes, and that these transitions are altered non-autonomously by loss of Tbx1. Further, inactivation of Tbx2 and Tbx3 in early CNCCs resulted in aortic arch branching defects due to failed smooth muscle differentiation. Loss of Tbx1 interrupted mesoderm to CNCC cell-cell communication with upregulation of BMP signaling with reduced MAPK signaling and failed dynamic transitions of CNCCs leading to disruption of aortic arch artery formation and cardiac outflow tract septation.

developmental biology↗

New genomic resources provide insights into the recent evolutionary adaptation of Antirrhinum lineage and multiomics investigation of S-locus

The multi-allelic S-locus, containing a pistil S-RNase and dozens of S-locus F-box (SLF), underlies genetic control of self-incompatibility (SI) in Antirrhinum hispanicum. The genus Antirrhinum, harboring such a SI system has been used as a model to study self-incompatibility extensively. However, there have been limited studies on the genomic organization of the S-locus supergene due to a lack of high-quality genomic data. Here, we present the chromosome-level reference and haplotype-resolved genome assemblies of a self-incompatible Antirrhinum hispanicum line, AhS7S8. Alongside with the draft genome of Misopates orontium, comparative genomics reveals that A.hispanicum diverged from its self-compatible cousin 12.3 million years ago (Mya). Expanded gene families enriched in distinct functional terms implied different evolutionary trajectories of outcrossing and selfing species. For the first time, two complete A.hispanicum S-haplotypes spanning [~]1.2Mb and containing 32 SLFs were reconstructed, while most of the SLFs derived from retroelement-mediated proximal or tandem duplication approximately 122 Mya. Moreover, we detected a candidate cis-transcription factor associated with regulating SLFs expression, and two miRNAs may control the expression of this transcription factor. Inter-specific S-locus and intra-specific S-haplotype comparisons revealed the dynamic nature and polymorphism of the S-locus supergene mediated by continuous gene duplication, segmental translocation or loss, and TE-involved transposition events. Our data provides an excellent resource for future research on the evolutionary studies on S-RNase-based self-incompatibility system.

genomics↗

A Novel Selective ERK1/2 Inhibitor, Laxiflorin B, Targets EGFR Mutation Subtypes in Non-small-cell Lung Cancer

Extracellular regulated protein kinases 1/2 (ERK1/2) are key members of multiple signaling pathways including the ErbB axis. ERK1/2 ectopic activation is responsible for various types of cancer, especially drug resistance to inhibitors of RTK, RAF and MEK, but specific ERK1/2 inhibitors are scarce. In this study, we identified a potential novel ERK covalent inhibitor, Laxiflorin B, a herbal compound with anticancer activity. However, Laxiflorin B is present at low levels in herbs; therefore, we adopted a semi-synthetic process for the efficient production of Laxiflorin B to improve the yield. Laxiflorin B induced mitochondria-mediated apoptosis via BAD activation in non-small-cell lung cancer (NSCLC) cells, especially in EGFR mutant subtypes. Transcriptomic analysis suggested that Laxiflorin B inhibits amphiregulin (AREG) and epiregulin (EREG) expression through ERK inhibition, and suppressed the activation of their receptors, ErbBs, via a positive feedback loop. More importantly, mass spectrometry combined with computer simulation analysis revealed that Laxiflorin B binds covalently to Cys-183 in the ATP-binding pocket of ERK1 through D-ring, and Cys-178 of ERK1 though non-inhibitory binding of A-ring, respectively. Laxiflorin B also exhibited strong tumor suppressive effects with low toxicity in a NSCLC tumor xenograft model in nude mice, and AREG and EREG were identified as biomarkers of Laxiflorin B efficacy. Finally, Laxiflorin B-4, a C-6 modification of Laxiflorin B, exhibited higher affinity for ERK1/2 and stronger tumor suppression. These findings provide a new approach to tumor inhibition using natural anticancer compounds.

pharmacology and toxicology↗

PRC2 Inactivating Mutations in Cancer are Synthetic Lethal with DNMT1 Targeted Therapy via Enhanced Viral Mimicry

Polycomb Repressive Complex 2 (PRC2) establishes and maintains di- and tri-methylation at histone 3 at lysine 27 (H3K27me2/3) in the genome and plays oncogenic and tumor suppressor roles in context-dependent cancer pathogenesis. While there is clinical success of therapeutically targeting PRC2 core component, EZH2, in PRC2-dependent cancers (e.g., follicular lymphoma, epithelioid sarcoma), it remains an unmet therapeutic bottleneck in PRC2-inactivated cancer. Biallelic inactivating mutations in PRC2 core components are a hallmark feature of high-grade malignant peripheral nerve sheath tumor (MPNST), an aggressive subtype of sarcoma with poor prognosis and no effective targeted therapeutics. Using a custom RNAi-based drop out screen, we observed that PRC2-inactivation is synthetic lethal with DNA methyltransferase 1 (DNMT1) downregulation; we further observed that small molecule DNMT inhibitors (DNMTis) resulted in enhanced cytotoxicity and antitumor response in PRC2-loss cancer context in vitro and in vivo. Mechanistically, DNMTi-mediated de-repression of retrotransposons (e.g., endogenous retroviral elements (ERVs)/LTR, LINE, SINE) and gene targets is partly restricted by PRC2, which potentially contributes to limited therapeutic activity in PRC2-wild-type (wt) cancer context. In contrast, DNMTi treatment synergizes with PRC2 inactivation and cooperatively amplifies the expression of retrotransposons (e.g., ERV/LTR, LINE, SINE), and subsequent viral mimicry response that promotes robust cell death in part through PKR-dependent double stranded-RNA (dsRNA) sensing. Collectively, our observations posit DNA methylation as a safeguard against anti-tumorigenic cell fate decisions in the context of PRC2-inactivation to promote cancer pathogenesis. Further, they identified a novel targeted therapeutic strategy in PRC2-inactivated MPNST and delineated the PRC2-inactivated cancer context for future preclinical exploration and clinical investigation of DNMT1-targeted therapies in cancer. SIGNIFICANCEPRC2-inactivation drives oncogenesis in various cancers but therapeutically targeting PRC2-loss has remained challenging. Here we show that PRC2 inactivating mutations sets up a tumor context-specific liability for synthetic lethal interaction with genetic and therapeutic inhibition of DNMT1. DNMT1 inhibitor-induced cytotoxicity in PRC2-loss cancer context is accompanied by innate immune signaling signature through PKR-mediated sensing of endogenous retrotransposons. These observations posit a therapeutic window via direct anti-tumor effect by DNMT1 inhibitors in PRC2-loss cancers, and point to potentials to be combined with innovative immunotherapeutic strategies to capitalize on innate immune signaling activation.

cancer biology↗

Tumor-intrinsic PRC2 inactivation drives a context-dependent immune-desert tumor microenvironment and confers resistance to immunotherapy

Immune checkpoint blockade (ICB) has demonstrated clinical success in "inflamed" tumors with significant T-cell infiltrates, but tumors with an immune-desert tumor microenvironment (TME) fail to benefit. The tumor cell-intrinsic molecular mechanisms of the immune-desert phenotype remain poorly understood. Here, we demonstrate that inactivation of the Polycomb-repressive complex 2 (PRC2) core components, EED or SUZ12, a prevalent genetic event in malignant peripheral nerve sheath tumor (MPNST) and sporadically in other cancer types, drives a context-dependent immune-desert TME. PRC2 inactivation reprograms the chromatin landscape that leads to a cell-autonomous shift from primed baseline signaling-dependent cellular responses (e.g., interferon {gamma}) to PRC2-regulated development and cellular differentiation transcriptional programs. Further, PRC2 inactivation reprograms the TME, leads to diminished tumor immune infiltrates and immune evasion through reduced chemokine production and impaired antigen presentation and T-cell priming, and confers ICB primary resistance through blunted T-cell recruitment in vivo. We demonstrate that strategies that enhancing innate immunity via intratumoral delivery of inactivated modified vaccinia virus Ankara (MVA) leads to increased tumor immune infiltrates and sensitizes PRC2-loss tumors to ICB. Our results provide novel molecular mechanisms of context-dependent dysfunctional epigenetic reprogramming that underline the immune-desert phenotype in MPNST and other cancers with PRC2 inactivation. Importantly, our findings highlight genetic-inactivation of PRC2 as a novel context-dependent ICB therapeutic resistance biomarker in cancer, and caution that therapeutic strategies that non-selectively target PRC2 in the host may lead to undesirable context-dependent immune evasion and ICB resistance in tumors. Our studies also point to intratumoral delivery of immunogenic therapeutic viruses as an initial strategy to modulate the immune-desert TME and capitalize on the clinical benefit of ICB.

cancer biology↗

Induction of Anxiety-Like Phenotypes by Knockout of Cannabinoid Type-1 Receptor in Amygdala of Marmosets

The amygdala is an important hub for the regulation of emotions, which is crucial for elucidating cellular and molecular mechanisms of many mental diseases. In the central nervous system, the endocannabinoid system plays a key role in the regulation of emotions and mainly functions through the cannabinoid type-1 receptor (CB1R), which is encoded by the Cnr1 gene. Although CB1R is highly expressed in the amygdala of non-human primates, little is known about its function. Here, we investigated the function of CB1R by knocking out the CB1R in the amygdala of adult marmosets through regional delivery of AAV-SaCas9-gRNA. We found that CB1R knockout in the amygdala of marmosets induced anxiety-like behaviors, including disrupted night sleep, agitated psychomotor activity in new environments, and reduced social desire, but had no effect on hedonic state and fear response. Moreover, CB1R-knockout marmosets exhibited up-regulated plasma cortisol levels, suggesting increased stress. These results showed that knockout of CB1R in the amygdala induced anxiety-like phenotypes in marmosets and shed new light on the mechanisms underlying the regulation of anxiety by CB1R in the amygdala of non-human primates.

neuroscience↗

Functional Contribution of Cancer-Associated Fibroblasts in Glioblastoma

The abundance and biological contribution of cancer associated fibroblasts (CAFs) in glioblastoma are poorly understood. Here, we applied single-cell RNA sequencing and spatial transcriptomics analyses to identify and characterize CAFs in human glioblastoma tumors and then performed functional enrichment analysis and in vitro assays to investigate their interactions with malignant glioblastoma cells. We found that CAF abundance was significantly correlated with tumor grade, poor clinical outcome, and activation of extracellular matrix remodeling, using three large databases containing bulk RNA-sequencing data and clinical information. Proteomic analysis of the CAFs and their secretome revealed fibronectin (FN1) as a strong candidate mediating CAF functions. This was validated using in vitro cellular models, which demonstrated that CAF conditioned media and recombinant FN1 could facilitate the migration and invasion of glioblastoma cells. In addition, we showed that CAFs were more abundant in the mesenchymal-like state (or subtype) than in other states of glioblastomas, while cell lines resembling the proneural-state responded to the CAF signaling better in terms of the migratory and invasive phenotypes. Investigating the in-situ expression of gene markers specifically associated with CAFs and mesenchymal malignant cells further indicated that CAFs were enriched in the perinecrotic and pseudopalisading zones of human tumors, where mesenchymal-like glioblastoma cells co-resided and thus likely interacted. Overall, this study characterized the molecular features and functional impacts of CAFs in glioblastoma, alluding to a novel cell-to-cell interaction axis mediated by CAFs in the glioblastoma microenvironment.

cancer biology↗

Keystone species determine the productivity of synthetic microbial biofilm communities

Microbes typically reside in multi-species communities, whose interactions have considerable impacts on the robustness and functionality of such communities. To manage microbial communities, it is essential to understand the factors driving their assemblage and maintenance. Even though the community composition could be easily assessed, interspecies interactions during community establishment remain poorly understood. Here, we combined co-occurrence network analysis with quantitative PCR to examine the importance of each species within synthetic communities (SynComs) of pellicle biofilms. Genome-scale metabolic models and in vitro experiments indicated that the biomass of SynComs was primarily affected by keystone species that are acting either as metabolic facilitators or as competitors. Our study sets an example of how to construct a model SynCom and investigate interspecies interactions.

microbiology↗

Kinetic and data-driven modeling of pancreatic β-cell central carbon metabolism and insulin secretion

Pancreatic {beta}-cells respond to increased extracellular glucose levels by initiating a metabolic shift. That change in metabolism is part of the process of glucose-stimulated insulin secretion and is of particular interest in the context of diabetes. However, we do not fully understand how the coordinated changes in metabolic pathways and metabolite products influence insulin secretion. In this work, we apply systems biology approaches to develop a detailed kinetic model of the intracellular central carbon metabolic pathways in pancreatic {beta}-cells upon stimulation with high levels of glucose. The model is calibrated to published metabolomics datasets for the INS1 823/13 cell line, accurately capturing the measured metabolite fold-changes. We first employed the calibrated mechanistic model to estimate the stimulated cells fluxome. We then used the predicted network fluxes in a data-driven approach to build a partial least squares regression model. By developing the combined kinetic and data-driven modeling framework, we gain insights into the link between {beta}-cell metabolism and glucose-stimulated insulin secretion. The combined modeling framework was used to predict the effects of common anti-diabetic pharmacological interventions on metabolite levels, flux through the metabolic network, and insulin secretion. Our simulations reveal targets that can be modulated to enhance insulin secretion. The model is a promising tool to contextualize and extend the usefulness of metabolomics data and to predict dynamics and metabolite levels that are difficult to measure in vitro. In addition, the modeling framework can be applied to identify, explain, and assess novel and clinically-relevant interventions that may be particularly valuable in diabetes treatment. Author SummaryDiabetes is among the most common chronic illnesses, occurring when the {beta}-cells in the pancreas are unable to produce enough insulin to properly manage the bodys blood sugar levels. {beta}-cells metabolize nutrients to produce energy needed for insulin secretion in response to high glucose, and there is a potential to harness {beta}-cell metabolism for treating diabetes. However, {beta}-cell metabolism is not fully characterized. We have developed a computational modeling framework to better understand the relationship between cellular metabolism and insulin production in the pancreatic {beta}-cell. With this modeling framework, we are able to simulate metabolic perturbations, such as the knockdown of the activity of a metabolic enzyme, and predict the effect on the metabolic network and on insulin production. This work can therefore be applied to investigate, in a time- and cost-efficient manner, {beta}-cell metabolism and predict effective therapies that target the cells metabolic network.

systems biology↗

Characterization of Cell-cell Communication in Autistic Brains with Single Cell Transcriptomes

Autism spectrum disorder is a neurodevelopmental disorder, affecting 1-2% of children. Studies have revealed genetic and cellular abnormalities in the brains of affected individuals, leading to both regional and distal cell communication deficits. Recent application of single cell technologies, especially single cell transcriptomics, has significantly expanded our understanding of brain cell heterogeneity and further demonstrated that multiple cell types and brain layers or regions are perturbed in autism. The underlying high-dimensional single cell data provides opportunities for multi-level computational analysis that collectively can better deconvolute the molecular and cellular events altered in autism. Here, we apply advanced computation and pattern recognition approaches on single cell RNA-seq data to infer and compare inter-cell-type signaling communications in autism brains and controls. Our results indicate that at a global level there are cell-cell communication differences in autism in comparison to controls, largely involving neurons as both signaling senders and receivers, but glia also contribute to the communication disruption. Although the magnitude of change is moderate, we find that excitatory and inhibitor neurons are involved in multiple intercellular signaling that exhibit increased strengths in autism, such as NRXN and CNTN signaling. Not all genes in the intercellular signaling pathways are differentially expressed, but genes in the pathways are enriched for axon guidance, synapse organization, neuron migration, and other critical cellular functions. Furthermore, those genes are highly connected to and enriched for genes previously associated with autism risks. Overall, our proof-of-principle computational study using single cell data uncovers key intercellular signaling pathways that are potentially disrupted in the autism brains, suggesting that more studies examining cross-cell type affects can be valuable for understanding autism pathogenesis.

bioinformatics↗

An ultrapotent neutralizing bispecific antibody with broad spectrum against SARS-CoV-2 variants

In spite of the successful development of effective countermeasures against Covid-19, variants have and will continue to emerge that could compromise the efficacy of currently approved neutralizing antibodies and vaccines. Consequently, novel and more efficacious agents are urgently needed. We have developed a bispecific antibody, 2022, consisting of two antibodies, 2F8 and VHH18. 2F8 was isolated from our proprietary fully synthetic human IDEAL (Intelligently Designed and Engineered Antibody Library)-VH/VL library and VHH18 is a single domain antibody isolated from IDEAL-nanobody library. 2022 was constructed by attaching VHH18 to the C-terminal of Fc of 2F8. 2022 binds two non-overlapping epitopes simultaneously on the RBD of the SARS-CoV-2 spike protein and blocks the binding of RBD to human angiotensin-converting enzyme 2 (ACE2). 2022 potently neutralizes SARS-CoV-2 and all of the variants tested in both pseudovirus and live virus assays, including variants carrying mutations known to resist neutralizing antibodies approved under EUA and that reduce the protection efficiency of current effective vaccines. The half-maximum inhibitory concentration (IC50) of 2022 is 270 pM, 30 pM, 20 pM, and 1 pM, for wild-type, alpha, beta, and delta pseudovirus, respectively. In the live virus assay, 2022 has an IC50 of 26.4 pM, 13.3 pM, and 88.6 pM, for wild-type, beta, and delta live virus, respectively. In a mouse model of SARS-CoV-2, 2022 showed strong prophylactic and therapeutic effects. A single administration of 2022 intranasal (i.n.) or intraperitoneal (i.p.) 24 hours before virus challenge completely protected all mice from bodyweight loss, as compared with up to 20% loss of bodyweight in placebo treated mice. In addition, the lung viral titers were undetectable (FRNT assay) in all mice treated with 2022 either prophylactically or therapeutically, as compared with around 1x105 pfu/g lung tissue in placebo treated mice. In summary, bispecific antibody 2022 showed potent binding and neutralizing activity across a variety of SARS-CoV-2 variants and could be an attractive weapon to combat the ongoing waves of the COVID-19 pandemic propagated mainly by variants, especially, the much more contagious delta variant.

microbiology↗

Chasing the metabolism of novel syntrophic acetate-oxidizing bacteria in thermophilic methanogenic chemostats

BackgroundAcetate is the major intermediate of anaerobic digestion of organic waste to CH4. In anaerobic methanogenic systems, acetate degradation is carried out by either acetoclastic methanogenesis or a syntrophic degradation by a syntrophy of acetate oxidizers and hydrogenotrophic methanogens. Due to challenges in isolation of syntrophic acetate-oxidizing bacteria (SAOB), the diversity and metabolism of SAOB, as well as the mechanisms of their interactions with methanogenic partners remain poorly understood. ResultsIn this study, we successfully enriched previously unknown SAOB by operating continuous thermophilic anaerobic chemostats fed with acetate, propionate, butyrate, or isovalerate as the sole carbon and energy source. They represent novel clades belonging to Clostridia, Thermoanaerobacteraceae, Anaerolineae, and Gemmatimonadetes. In these SAOB, acetate is degraded through reverse Wood-Ljungdahl pathway or an alternative pathway mediated by the glycine cleavage system, while the SAOB possessing the latter pathway dominated the bacterial community. Moreover, H2 is the major product of the acetate degradation by these SAOB, which is mediated by [FeFe]-type electron-confurcating hydrogenases, formate dehydrogenases, and NADPH reoxidation complexes. We also identified the methanogen partner of these SAOB in acetate-fed chemostat, Methanosarcina thermophila, which highly expressed genes for CO2-reducing methanogenesis and hydrogenases to supportively consuming H2 at transcriptional level. Finally, our bioinformatical analyses further suggested that these previously unknown syntrophic lineages were prevalent and might play critical roles in thermophilic methanogenic reactors. ConclusionThis study expands our understanding on the phylogenetic diversity and in situ biological functions of uncultured syntrophic acetate degraders, and presents novel insights on how they interact with their methanogens partner. These knowledges strengthen our awareness on the important role of SAO in thermophilic methanogenesis and may be applied to manage microbial community to improve the performance and efficiency of anaerobic digestion.

microbiology↗

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic {beta}-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic {beta}-Cell Consortium. Significance StatementCells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic {beta}-Cell Consortium.

cell biology↗

MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads

Most eukaryotic genes harbor multiple cleavage and polyadenylation sites (PASs), leading to expression of alternative polyadenylation (APA) isoforms. APA regulation has been implicated in a diverse array of physiological and pathological conditions. While RNA sequencing tools that generate reads containing the PAS, named onSite reads, have been instrumental in identifying PASs, they have not been widely used. By contrast, a growing number of methods generate reads that are close to the PAS, named nearSite reads, including the 3 end counting strategy commonly used in single cell analysis. How these nearSite reads can be used for APA analysis, however, is poorly studied. Here, we present a computational method, named model-based analysis of alternative polyadenylation using 3 end-linked reads (MAAPER), to examine APA using nearSite reads. MAAPER uses a probabilistic model to predict PASs for nearSite reads with high accuracy and sensitivity, and examines different types of APA events, including those in 3UTRs and introns, with robust statistics. We show MAAPERs accuracy with data from both bulk and single cell RNA samples and its applicability in unpaired or paired experimental designs. Our result also highlights the importance of using well annotated PASs for nearSite read analysis.

bioinformatics↗

Butyryl/Caproyl-CoA:Acetate CoA-Transferase: Cloning, Expression and Characterization of the Key Enzyme Involved in Medium-Chain Fatty Acid Biosynthesis

Coenzyme A transferases (CoATs) are important enzymes involved in carbon chain elongation contributing to medium-chain fatty acid (MCFA) biosynthesis. For example, butyryl-CoA:acetate CoA transferase (BCoAT) is responsible for the final step of butyrate synthesis from butyryl-CoA. However, little is known about caproyl-CoA:acetate CoA-transferase (CCoAT), which is responsible for the final step of caproate synthesis from caproyl-CoA. In this study, two CoAT genes from Ruminococcaceae bacterium CPB6 and Clostridium tyrobutyricum BEY8 were identified by gene cloning and expression analysis. The enzyme assays and kinetic studies were carried out using butyryl-CoA or caproyl-CoA as the substrate. CPB6-CoAT can catalyze the conversion of both butyryl-CoA to butyrate and caproyl-CoA to caproate, but its catalytic efficiency with caproyl-CoA as the substrate was 3.8 times higher than that with butyryl-CoA. In contrast, BEY8-CoAT had only BCoAT activity, not CCoAT activity. This demonstrated the existence of a specific CCoAT involved in chain elongation via the reverse {beta}-oxidation pathway. Comparative bioinformatics analysis showed the presence of a highly conserved motif (GGQXDFXXGAXX) in CoATs, which is predicted to be the active center of CoATs. Single point mutations in the conserved motif of CPB6-CoAT (Asp346 and Ala351) led to marked decreases in the activity for butyryl-CoA and caproyl-CoA, indicating that the conserved motif is the active center of CPB6-CoAT, and sites Asp346 and Ala351 were critical residues that affect enzymatic activity. This work provides insight into the function of CCoAT in caproic acid biosynthesis and improves the understanding of the chain elongation pathway for MCFA production.

biochemistry↗