Biology subjects

Liu, D.

Publications and source records attributed to Liu, D..

At least 253 records · Page 14Linked to original sources

bioRxiv2023

Transcriptome mining of RNA viruses (family Totiviridae) in Eimeria necatrix and Eimeria stiedai

Coccidian protozoa from the genus Eimeria are widespread parasites of vertebrates, causing serious disease (coccidiosis) and economic loss most notably in poultry. Several species of Eimeria are themselves infected with small RNA viruses assigned to the family Totiviridae. In this study, the sequences of two such viruses were newly determined, one of which represents the first complete protein-coding sequence of a virus from E. necatrix, an important pathogen of chickens, and the other of which is from E. stiedai, an important pathogen of rabbits. Sequence features of the newly identified viruses, compared with those of ones reported previously, provide several insights. Phylogenetic analyses suggest that these eimerian viruses constitute a well-demarcated clade, probably deserving of recognition as a distinct genus.

microbiology↗

bioRxiv2023

A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis

Social grouping increases survival in many species, including humans1,2. By contrast, social isolation generates an aversive state (loneliness) that motivates social seeking and heightens social interaction upon reunion3-5. The observed rebound in social interaction triggered by isolation suggests a homeostatic process underlying the control of social drive, similar to that observed for physiological needs such as hunger, thirst or sleep3,6. In this study, we assessed social responses in multiple mouse strains and identified the FVB/NJ line as exquisitely sensitive to social isolation. Using FVB/NJ mice, we uncovered two previously uncharacterized neuronal populations in the hypothalamic preoptic nucleus that are activated during social isolation and social rebound and that orchestrate the behavior display of social need and social satiety, respectively. We identified direct connectivity between these two populations of opposite function and with brain areas associated with social behavior, emotional state, reward, and physiological needs, and showed that animals require touch to assess the presence of others and fulfill their social need, thus revealing a brain-wide neural system underlying social homeostasis. These findings offer mechanistic insight into the nature and function of circuits controlling instinctive social need and for the understanding of healthy and diseased brain states associated with social context.

neuroscience↗

bioRxiv2023

GraphCPLMQA: Assessing protein model quality based on deep graph coupled networks using protein language model

Model quality evaluation is crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain challenge. More importantly, the quality assessment of multimer models is a hot topic for structure predicton.In this work, we present GraphCPLMQA, a novel graph-coupled network that uses embeddings from protein language models to assess residue-level protein model quality. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality are inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared to the CASP15 interface evaluation method in 9108 models of CASP15 multimer test set. In CAMEO blind test (2022-05-20[~]2022-08-13), GraphCPLMQA ranked first compared to other servers. GraphCPLMQA also outperforms state-of-the-art methods on 19,035 models in CASP13 and CASP14 monomer test set. Finally, on AlphaFold2 datasets, GraphCPLMQA was superior to self-assessment of AlphaFold2 in MAE metric, and it was able to screen out better models than AlphaFold2.

bioinformatics↗

bioRxiv2023

Model Parameter identification using 2D vs 3D experimental data: a comparative analysis

Computational models are becoming an increasingly valuable tool in biomedical research. They enable the quantification of variables difficult to measure experimentally, an increase in the spatio-temporal resolution of the experiments and the testing of hypotheses. Parameter estimation from in-vitro data, remains a challenge, due to the limited availability of experimental datasets acquired in directly comparable conditions. While the use of computational models to supplement laboratory results contributes to this issue, a more extensive analysis of the effect of incomplete or inaccurate data on the parameter optimization process and its results is warranted. To this end, we compared the results obtained from the same in-silico model of ovarian cancer cell growth and metastasis, calibrated with datasets acquired from two different experimental settings: a traditional 2D monolayer, and 3D cell culture models. The differential behaviour of these models will inform the role and importance of experimental data in the calibration of computational models calibration. This work will also provide a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational models Author summaryParameter identification is a key step in the development of a computational model, that is used to establish a connection between the simulated and experimental results and verify the accuracy of the in-silico framework. The selection of the in-vitro data to be used in this phase is fundamental, but little attention has been paid to the role of the experimental model in this process. To bridge this gap we present a comparative analysis of the same computational model calibrated using experimental data acquired from cells cultured (i) in 2D monolayers, (ii) in 3D culture models and (iii) a combination of the two. Data acquired in different experimental settings induce changes in the optimal parameter sets and the corresponding computational models behaviour. This translates in a varying degree of accuracy during the validation procedure, when the simulated data are compared to experimental measurements not used during the calibration step. Overall, our work provides a workflow and a set of guidelines to select the most appropriate experimental setting for the calibration and validation of computational models.

bioengineering↗

bioRxiv2023

Population genomics reveals extensive inbreeding and purging of mutational load in wild Amur tigers

The inbreeding is a big threat for the persistence of genetic diversity in small and isolated populations of endangered species. The homozygous genome could exacerbate inbreeding depression by introducing homozygous deleterious alleles in the population. However, purging of inbreeding loads as they become homozygotes in small populations could alleviate the depression. The Amur tiger (Panthera tigris altaica) is typically exists in small population living in forests in Northeast Asia and is among the most endangered animals on the planet with great symbolic significance of conservation. By comparing with captive individuals, we revealed substantially higher and more extensive inbreeding in the wild Amur tiger population (FROH=0.51) than in captive Amur tigers (FROH=0.26). We further found much less mutational loads in wild populations when compared with captive Amur tigers. However, the frequency of loss of function and deleterious nonsynonymous mutations inside ROH regions are much lower than that in non-ROH regions in both wild and captive Amur tigers, indicating the purging may had occurred in both populations but much effective in the wild population. In addition, we found the average frequency of deleterious alleles was much lower than that of neutral alleles in the wild population, indicating that the purifying selection contributed to the purging of mutational loads in the wild Amur tigers. These findings provide valuable genome-wide evidence to support the making of future conservation plans of wild Amur tigers.

genomics↗

bioRxiv2023

Auranofin Inhibition of Thioredoxin Reductase in a Preclinical Model of Small Cell Lung Cancer

Thioredoxin Reductase (TrxR) is a key enzyme in hydroperoxide detoxification through peroxiredoxin enzymes and in thiol-mediated redox regulation of cell signaling. Because cancer cells produce increased steady-state levels of reactive oxygen species (ROS; i.e., superoxide and hydrogen peroxide), TrxR is currently being targeted in clinical trials using the anti-rheumatic drug, auranofin (AF). AF treatment decreased TrxR activity and clonogenic survival in small cell lung cancer (SCLC) cell lines (DMS273 and DMS53) as well as the lung atypical (neuroendocrine tumor) NET cell line H727. AF treatment also significantly sensitized DMS273 and H727 cell lines in vitro to sorafenib, a multi-kinase inhibitor that was shown to decrease intracellular glutathione. The pharmacokinetic and pharmacodynamic properties of AF treatment in a mouse SCLC xenograft model was examined to maximize inhibition of TrxR activity without causing toxicity. AF was administered intraperitoneally at 2 mg/kg or 4 mg/kg (IP) once (QD) or twice daily (BID) for 1 to 5 days in mice with DMS273 xenografts. Plasma levels of AF were 10-20 M (determined by mass spectrometry of gold) and the optimal inhibition of TrxR (50 %) was obtained at 4 mg/kg once daily, with no effect on glutathione peroxidase 1 activity. When this daily AF treatment was extended for 14 days a significant prolongation of median survival from 19 to 23 days (p=0.04, N=30 controls, 28 AF) was observed without causing changes in animal bodyweight, CBCs, bone marrow toxicity, blood urea nitrogen, or creatinine. These results show that AF is an effective inhibitor of TrxR both in vitro and in vivo in SCLC, capable of sensitizing NETs and SCLC to sorafenib, and supports the hypothesis that AF could be used as an adjuvant therapy with agents known to induce disruptions in thiol metabolism to enhance therapeutic efficacy.

cancer biology↗

bioRxiv2023

Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 11.8% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly generates models with acceptable quality (DockQ [≥] 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

bioinformatics↗

bioRxiv2023

Protein Engineering for Thermostability through Deep Evolution

Protein engineering for increased thermostability through iterative mutagenesis and high throughput screening is labor-intensive, expensive and inefficient. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein thermostability through global sequence generation and selection using deep learning models. We firstly constructed a thermostability selector based on a protein language model to extract thermostability-related features in high-dimensional latent spaces of protein sequences with high temperature tolerance. Subsequently, we constructed a variant generator based on a generative adversarial network to create protein sequences containing the desirable function with more than 50% accuracy. Finally, the generator and selector were utilized to iteratively improve the performance of DeepEvo on the model protein glyceraldehyde-3-phosphate dehydrogenase (G3PDH), whereby 8 highly thermostable variants were obtained from only 30 generated sequences, demonstrating the high efficiency of DeepEvo for the engineering of protein thermostability.

bioinformatics↗

bioRxiv2023

Parallel genome-scale CRISPR screens distinguish pluripotency and self-renewal

Pluripotent stem cells are defined by their self-renewal capacity, which is the ability of the stem cells to proliferate indefinitely while maintaining the pluripotent identity essential for their ability to differentiate into any somatic cell lineage. However, understanding the mechanisms that control stem cell fitness versus the pluripotent cell identity is challenging. To investigate the interplay between these two aspects of pluripotency, we performed four parallel genome-scale CRISPR-Cas9 loss-of-function screens interrogating stem cell fitness in hPSC self-renewal conditions, and the dissolution of the primed pluripotency identity during early differentiation. Comparative analyses led to the discovery of genes with distinct roles in pluripotency regulation, including mitochondrial and metabolism regulators crucial for stem cell fitness, and chromatin regulators that control pluripotent identity during early differentiation. We further discovered a core set of factors that control both stem cell fitness and pluripotent identity, including a network of chromatin factors that safeguard pluripotency. Our unbiased and systematic screening and comparative analyses disentangle two interconnected aspects of pluripotency, provide rich datasets for exploring pluripotent cell identity versus cell fitness, and offer a valuable model for categorizing gene function in broad biological contexts.

developmental biology↗

bioRxiv2023

DeepUMQA3: a web server for model quality assessment of protein complexes

Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, model quality assessment methods that can provide accurate evaluation of complex structures are urgently required. Here, we present DeepUMQA3, a web server for evaluating protein complex structures using deep neural network. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and a improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman and AUC of 0.564, 0.535 and 0.755 under the lDDT measurement, which are 18.5%, 23.6% and 10.9% higher than the second-best method, respectively. DeepUMQA3 can also accurately assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues/models. The websever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.

bioinformatics↗

bioRxiv2023

Progesterone is an Inducement of Heritable Pulmonary Arterial Hypertension with BMPR2 Mutation

BackgroundBone morphogenetic protein type II receptor (BMPR2) gene mutation accounts for 80% of patients with heritable pulmonary artery hypertension (HPAH), and female mutation carriers have significantly higher penetrance rate than males. The inducement of HPAH penetrance and the mechanism of sex differential penetrance are still elusive. MethodsWe infected or transfected pulmonary artery smooth muscle cells (PASMCs) with shBMPR2 lentivirus or siBMPR2 to simulate the pathologic condition of BMPR2 heterozygous mutation and treated them with progesterone. The HPAH patient-derived induced pluripotent stem cells (iPSCs) were induced into vascular smooth muscle cells (VSMCs) to further verify the cellular phenotype. Wild-type flox+/- female mice and SM22-cre BMPR2 flox+/- female mice (CKO mice) were administered with 1-month progesterone, and their phenotype of PAH was evaluated by right heart catheterization and histopathological examination. ResultsProgesterone promoted the proliferation of PASMCs with BMPR2-knockdownby activating ERK pathway via progesterone receptor (PGR). Activated ERK not only upregulated the phosphorylation and elevation of cMYC, but also induced the transcription of endothelin (EDN1) by promoting the nuclear entry of c-JUN and combination on the its promoter region. Similar results were confirmed by iPSCs-VSMCs experiment. CKO mice developed PAH spontaneously and had increased expression of EDN1, which was further aggravated by exogenous progesterone. ConclusionsProgesterone might be an inducement of HPAH penetrance caused by BMPR2 mutation, accounting for sex differential penetrance.

molecular biology↗

bioRxiv2023

DCBLD1 modulates angiogenesis by regulating of the VEGFR-2 endocytosis in endothelial cells

ObjectiveUnwanted angiogenesis is involved in the progression of various malignant tumors and cardiovascular diseases, and the factors that regulate angiogenesis are potential therapeutic targets. We tested the hypothesis that DCBLD1 (Discoidin, CUB, and LCCL domain-containing protein 1) is a co-receptor of VEGFR-2 and modulates angiogenesis in endothelial cells(ECs). Approach and ResultsA carotid artery ligation model and retinal angiogenesis assay were used to study angiogenesis using globe knockout or EC-specific conditional DCBLD1 knockout mice in vivo. Immunoblotting, immunofluorescence staining, plasma-membrane subfraction isolation, Co-immunoprecipitation and mass-spectrum assay were performed to clarify the molecular mechanisms.Loss of DCBLD1 impaired VEGF response and inhibited VEGF-induced EC proliferation and migration. DCBLD1 deletion interfered with adult and developmental angiogenesis. Mechanistically, DCBLD1 bound to VEGFR-2 and regulated the formation of VEGFR-2 complex with negative regulators: protein tyrosine phosphatases, E3 ubiquitin ligases(Nedd4 and c-Cbl), and also DCBLD1 knockdown promoted lysosome-mediated VEGFR-2 degradation in ECs. ConclusionsThese findings demonstrated the essential role of endothelial DCBLD1 in regulating VEGF signaling and provided evidence that DCBLD1 promotes VEGF-induced angiogenesis by limiting the dephosphorylation, ubiquitination, and lysosome degradation after VEGFR-2 endocytosis. We proposed that endothelial DCBLD1 is a potential therapeutic target for ischemic cardiovascular diseases by the modulation of angiogenesis through regulating of the VEGFR-2 endocytosis.

molecular biology↗

bioRxiv2023

Prediction of virus-host association using protein language models and multiple instance learning

Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 8.6%, 12.3%, and 4.1% in prokaryotic hosts, and 0.5%, 1.8% and 3% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction task and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together, highlighting the ability of EvoMIL to capture key proteins in virus-host specificity. Author summaryBeing able to predict which viruses can infect which host species, and identifying the specific proteins that are involved in these interactions, are fundamental tasks in virology. Traditional methods for predicting these interactions rely on common manual features among proteins, overlooking the structure of the protein "language" encoded in individual proteins. We have developed a novel method that combines a protein language model and multiple instance learning to allow host prediction directly from protein sequences, without the need to extract manual features. This method significantly improved prediction accuracy and revealed key proteins involved in virus-host interactions.

bioinformatics↗

bioRxiv2023

MRE11A: a novel negative regulator of human DNA mismatch repair

DNA mismatch repair (MMR) is a highly conserved pathway that corrects DNA replication errors. Although well characterized, MMR factors remain to be identified. As a 3-5 exonuclease and endonuclease, meiotic recombination 11 homolog A (MRE11A) is implicated in multiple DNA repair pathways. However, the role of MRE11A in MMR is unclear. Here, we show that MRE11A deficiency increased the sensitivity of HeLa cells to N-methyl-N nitro-N nitrosoguanidine (MNNG) treatment, implying a potential role of MRE11 in MMR. Moreover, we found MRE11A was largely recruited to chromatin and negatively regulated the DNA damage signals within the first cell cycle after MNNG treatment. We also showed that knockdown of MRE11A increased, while overexpressing MRE11A decreased, MMR activity in HeLa cells, suggesting that MRE11A negatively regulates MMR activity. Furthermore, we show that the recruitment of MRE11A to chromatin requires MLH1 and that MRE11A competes with PMS2 for binding to MLH1. This decreases PMS2 levels in whole cell and on chromatin, and consequently comprises MMR activity. Collectively, our findings reveal that MRE11A is a negative regulator of human MMR.

molecular biology↗

bioRxiv2023

GenBank genomics highlight the genomic features, genetic diversity and regulation of morphological, metabolic and disease-resistance traits in Nicotiana tabacum

Nicotiana tabacum is a model organism in plant molecular and pathogenic research and has significant potential in the production of biofuels and active pharmaceutical compounds in synthetic biology. Because of the large allotetraploid genome of tobacco, its genomic features, genetic diversity and genetic regulation of many complex traits remain unknown. In this study, we present a nearly complete chromosome-scale assembly of N. tabacum and provide evidence that homoeologous exchange between subgenomes and epigenetic remodelling are likely mechanisms of genome stabilization and subgenome coordination following polyploidization. By leveraging GenBank-scale sequencing and phenotyping data from 5196 lines, geography at the continent scale, rather than types assigned on the basis of curing crop practices, was found to be the most important correlate of genetic structure. Using 178 marker{square}trait associations detected in genome-wide association analysis, a reference genotype-to-phenotype map was built for 39 morphological, developmental, and disease-resistance traits. A novel gene, auxin response factor 9 (Arf9), associated with wider leaves after being knocked out, was fine-mapped to a single nucleotide polymorphism (SNP). This point mutation alters the translated amino acid from Ala203 to Pro203, likely preventing homodimer formation during DNA binding. Our analysis also revealed signatures of positive and polygenic selection for multiple traits during the process of selective breeding. Overall, this study demonstrated the power of leveraging GenBank genomics to gain insights into the genomic features, genetic diversity, and regulation of complex traits in N. tabacum, laying a foundation for future research on plant functional genomics, crop breeding, and the production of biopharmaceuticals and biofuels.

genetics↗

bioRxiv2023

RelB represses miR-193a-5p expression to promote the phenotypic transformation of vascular smooth muscle cells in aortic aneurysm

Aortic aneurysm (AA) is a potentially fatal disease with the possibility of rupture, causing high mortality rates with no effective drugs for the treatment of AA. The mechanism of AA, as well as its therapeutic potential to inhibit aneurysm expansion, has been minimally explored. Small non-coding RNA (miRNAs and miRs) is emerging as a new fundamental regulator of gene expression. This study aimed to explore the role and mechanism of miR-193a-5p in abdominal aortic aneurysms (AAA). In AAA vascular tissue and Angiotensin II (Ang II)-treated vascular smooth muscle cells (VSMCs), the expression of miR-193a-5 was determined using real-time quantitative PCR (RT-qPCR). Western blotting was used to detect the effects of miR-193a-5p on PCNA, CCND1, CCNE1, and CXCR4. To detect the effect of miR-193a-5p on the proliferation and migration of VSMCs, CCK-8, and EdU immunostaining, flow cytometry, wound healing, and Transwell Chamber analysis were performed. In vitro results suggest that overexpression of miR-193a-5p inhibited the proliferation and migration of VSMCs, and its inhibition aggravated their proliferation and migration. In VSMCs, miR-193a-5p mediated proliferation by regulating CCNE1 and CCND1 genes and migration by regulating CXCR4. Further, in the Ang II-induced abdominal aorta of mice, the expression of miR-193a-5p was reduced and significantly downregulated in the serum of patients with aortic aneurysm (AA). In vitro studies confirmed that Ang II-induced downregulation of miR-193a-5p in VSMCs by upregulation of the expression of the transcriptional repressor RelB in the promoter region. This study may provide new intervention targets for the prevention and treatment of AA.

cell biology↗

bioRxiv2023

Alteration of functional connectivity in the cortex and major brain networks of non-human primates following focused ultrasound exposure

Focused ultrasound (FUS) is a non-invasive neuromodulation technology that is being investigated for potential treatment of neurological and psychiatric disorders. Focused ultrasound combined with microbubbles can temporarily open the intact blood-brain barrier (BBB) of animals and humans, and facilitate drug delivery. FUS exposure, either with or without microbubbles, has been demonstrated to alter the behavior of non-human primates, and previous work has demonstrated transient and long-term effects of FUS neuromodulation on functional connectivity using resting state functional MRI. However, it is unknown whether opening the BBB affects functional connectivity differently than FUS alone. Thus we applied FUS alone (neuromodulation) and FUS with microbubbles (BBB opening) in the dorsal striatum of lightly anesthetized non-human primates, and compared changes in functional connectivity in major brain networks. We found different alteration patterns between FUS neuromodulation and FUS-mediated BBB opening in several cortical areas, and we also found that applying FUS to a deep brain structure can alter functional connectivity in the default mode network and frontotemporal network.

neuroscience↗

bioRxiv2023

Exploring the consequences of redirecting an exocytic Rab onto endocytic vesicles.

Bidirectional vesicular traffic links compartments along the exocytic and endocytic pathways. Rab GTPases have been implicated in specifying the direction of vesicular transport because anterograde vesicles are marked with a different Rab than retrograde vesicles. To explore this proposal, we sought to redirect an exocytic Rab, Sec4, onto endocytic vesicles by fusing the catalytic domain of the Sec4 GEF, Sec2, onto the CUE localization domain of Vps9, a GEF for the endocytic Rab, Ypt51. The Sec2GEF-GFP-CUE construct was found to localize to bright puncta predominantly near sites of polarized growth and this localization was strongly dependent upon the ability of the CUE domain to bind to the ubiquitin moieties added to the cytoplasmic tails of proteins destined for endocytic internalization. Sec4 and Sec4 effectors were recruited to these puncta with varying efficiency. The puncta appeared to consist of clusters of 80 nm vesicles and although the puncta are largely static, FRAP analysis suggests that traffic into and out of these clusters continues. Cells expressing Sec2GEF-GFP-CUE grew surprisingly well and secreted protein at near normal efficiency, implying that Golgi derived secretory vesicles were delivered to polarized sites of cell growth, where they tethered and fused with the plasma membrane despite the misdirection of Sec4 and its effectors. In total, the results suggest that while Rabs play a critical role in regulating vesicular transport, cells are remarkably tolerant of Rab misdirection.

cell biology↗