Biology subjects

Ma, Q.

Publications and source records attributed to Ma, Q..

At least 19 recordsLinked to original sources

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗

bioRxiv2018

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

Identification of transcription factor binding sites (TFBSs) and cis-regulatory motifs (motifs for short) from genomics datasets, provides a powerful view of the rules governing the interactions between TFs and DNA. Existing motif prediction methods however, are limited by high false positive rates in TFBSs identification, contributions from non-sequence-specific binding, and complex and indirect binding mechanisms. High throughput next-generation sequencing data provides unprecedented opportunities to overcome these difficulties, as it provides multiple whole-genome scale measurements of TF binding information. Uncovering this information brings new computational and modeling challenges in high-dimensional data mining and heterogeneous data integration. To improve TFBS identification and novel motifs prediction accuracy in the human genome, we developed an advanced computational technique based on deep learning (DL) and high-performance computing, named DESSO. DESSO utilizes deep neural network and binomial distribution to optimize the motif prediction. Our results showed that DESSO outperformed existing tools in predicting distinct motifs from the 690 in vivo ENCODE ChIP-Sequencing (ChIP-Seq) datasets for 161 human TFs in 91 cell lines. We also found that protein-protein interactions (PPIs) are prevalent among human TFs, and a total of 61 potential tethering binding were identified among the 100 TFs in the K562 cell line. To further expand DESSOs deep-learning capabilities, we included DNA shape features and found that (i) shape information has a strong predictive power for TF-DNA binding specificity; and (ii) it aided in identification of the shape motifs recognized by human TFs which in turn contributed to the interpretation of TF-DNA binding in the absence of sequence recognition. DESSO and the analyses it enabled will continue to improve our understanding of how gene expression is controlled by TFs and the complexities of DNA binding. The source code and the predicted motifs and TFBSs from the 690 ENCODE TF ChIP-Seq datasets are freely available at the DESSO web server: http://bmbl.sdstate.edu/DESSO.

bioinformatics↗

bioRxiv2018

The novel lncRNA lnc-NR2F1 is pro-neurogenic and mutated in human neurodevelopmental disorders

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

neuroscience↗

bioRxiv2018

QUBIC2: A novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis

The combination of biclustering and large-scale gene expression data holds a promising potential for inference of the condition specific functional pathways/networks. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-sequencing (RNA-Seq) data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, e.g., the massive zeros or lowly expressed genes in the data, especially for single-cell RNA-Seq (scRNA-Seq) data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. Here we presented a novel biclustering algorithm namely QUBIC2, for the analysis of large-scale bulk RNA-Seq and scRNA-Seq data. Key novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression, (ii) adopted the mixture Gaussian distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes, (iii) utilized a Core-Dual strategy to identify biclusters and optimize relevant parameters, and (iv) developed a size-based P-value framework to evaluate the statistical significances of all the identified biclusters. Our method validation on comprehensive data sets of bulk and single cell RNA-seq data suggests that QUBIC2 had superior performance in functional modules detection and cell type classification compared with the other five widely-used biclustering tools. In addition, the applications of temporal and spatial data demonstrated that QUBIC2 can derive meaningful biological information from scRNA-Seq data. The source code for QUBIC2 can be freely accessed at https://github.com/maqin2001/qubic2.

bioinformatics↗

bioRxiv2018

Nitric oxide synthase-mediated early nitric oxide-burst alleviates drought-induced oxidative damage in ammonium supplied-rice roots

Ammonium (NH4+) can enhance rice drought tolerance in comparison to nitrate (NO3-). The mechanism underpinning this relationship was investigated based on the time-dependent nitric oxide (NO) production and its protective role in oxidative stress of NH4+-/NO3--supplied rice under drought. An early burst of NO was induced by drought 3h after root NH4+ treatment but not after NO3- treatment. Root oxidative damage induced by drought was significantly higher in NO3- than in NH4+-treatment due to its reactive oxygen species accumulation. Inducing NO production by applying NO donor 3h after NO3- treatment alleviated the oxidative damage, while inhibiting the early NO burst increased root oxidative damage in NH4+ treatment. Application of nitric oxide synthase (NOS) inhibitor N(G)-nitro-L-arginine methyl ester (L-NAME) completely suppressed NO synthesis in roots 3h after NH4+ treatment and aggravated drought-induced oxidative damage, indicating the aggravation of oxidative damage might have resulted from changes in NOS-mediated early NO burst. Drought also increased root antioxidant enzymes activities, which were further induced by NO donor but repressed by NO scavenger and NOS inhibitor in NH4+-treated roots. Thus, the NOS-mediated early NO burst plays an important role in alleviating oxidative damage induced by drought by enhancing antioxidant defenses in NH4+-supplied rice roots.\n\nHighlightNOS-mediated early NO burst plays an important role in alleviating oxidative damage induced by water stress, by enhancing the antioxidant defenses in roots supplemented with NH4+

physiology↗

bioRxiv2018

Thylakoid-integrated recombinant Hcf106 participates in the chloroplast Twin Arginine Transport (cpTat) system

The chloroplast Twin arginine transport (cpTat) system distinguishes itself as a protein transport pathway by translocating fully-folded proteins, using the proton-motive force (PMF) as the sole source of energy. The cpTat pathway is evolutionarily conserved with the Tat pathway found in the plasma membrane of many prokaryotes. The cpTat (E. coli) system uses three proteins, Tha4 (TatA), Hcf106 (TatB), and cpTatC (TatC), to form a transient translocase allowing the passage of precursor proteins. Briefly, cpTatC and Hcf106, with Tha4, form the initial receptor complex responsible for precursor protein recognition and binding in an energy-independent manner, while a separate pool of Tha4 assembles with the precursor-bound receptor complex in the presence the PMF. Analysis by blue-native polyacrylamide gel electrophoresis (BN-PAGE) shows that the receptor complex, in the absence of precursor, migrates near 700 kDa and contains cpTatC and Hcf106 with little Tha4 remaining after detergent solubilization. To investigate the role that Hcf106 may play in receptor complex oligomerization and/or stability, systematic cysteine substitutions were made in positions from the N-terminal transmembrane domain to the end of the predicted amphipathic helix of the protein. BN-PAGE analysis allowed us to identify the locations of amino acids in Hcf106 that were critical for interacting with cpTatC. Oxidative cross-linking allowed us to map interactions of the transmembrane domain and amphipathic helix region of Hcf106. In addition, we showed that in vitro expressed, integrated Hcf106 can interact with the precursor signal peptide domain and imported cpTatC, strongly suggesting that a subpopulation of the integrated Hcf106 is participating in competent cpTat complexes.

plant biology↗

bioRxiv2018

Selective auxin agonists induce specific AUX/IAA protein degradation to modulate plant development

Auxin phytohormones control most aspects of plant development through a complex and interconnected signaling network. In the presence of auxin, AUXIN/INDOLE-3-ACETIC ACID (AUX/IAA) transcriptional repressors are targeted for degradation by the SKP1-CULLIN1-F-BOX (SCF) ubiquitin-protein ligases containing TRANSPORT INHIBITOR RESISTANT 1/AUXIN SIGNALING F-BOX (TIR1/AFB). CULLIN1-neddylation is required for SCFTIR1/AFB functionality as exemplified by mutants deficient in the NEDD8-activating enzyme subunit AUXIN-RESISTANT 1 (AXR1). Here, we report a chemical biology screen that identifies small molecules requiring AXR1 to modulate plant development. We selected four molecules of interest, RubNeddin1 to 4 (RN1 to 4), among which RN3 and RN4 trigger selective auxin responses at transcriptional, biochemical and morphological levels. This selective activity is explained by their ability to promote the interaction between TIR1 and a specific subset of AUX/IAA proteins, stimulating the degradation of particular AUX/IAA combinations. Finally, via a genetic screen using RN4, we revealed that the chromatin remodeling ATPase BRAHMA is implicated in auxin-mediated apical hook development. These results demonstrate the power of selective auxin agonists to dissect auxin perception for plant developmental functions.

plant biology↗

bioRxiv2018

Corilagin controls post-parasiticide schistosome egg-induced liver fibrosis by inhibiting Stat6 signalling pathway

This study aims to explore the effect of Corilagin (Cor) on post-parasiticide schistosome egg-induced hepatic fibrosis through the Stat6 signalling pathway in vitro and in vivo. Cellular and animal models were established and treated by Corilagin. The inhibitory effect of Corilagin was also confirmed in RAW264.7 cells in which Stat6 was overexpressed based on the GV367-Stat6-EGFP lentiviral vector system and in which Stat6 was knock-downed by gene specific siRNAs. As a result, Corilagin prevented increases in the protein level of Phospho-Stat6 (P-Stat6). Both the mRNA and protein levels of the downstream mediators SOCS1, KLF4, and PPAR{gamma}/{delta} were markedly suppressed after Corilagin treatment. Expression of ARG1 and FIZZ1/Retnla, Ym1, TGF-{beta} and PDGF in serum were also inhibited by Corilagin. The pathological changes, area of granulomas of liver sections, and degree of hepatic fibrosis were significantly alleviated in the Corilagin group. The areas of CD68- and CD206-positive cells stained by immunofluorescence were significantly decreased by Corilagin. In conclusion, Corilagin can suppress post-parasiticide schistosome egg-induced hepatic fibrosis by inhibiting the Stat6 signalling pathway and provide a new therapeutic strategy for schistosomiasis liver fibrosis.

pharmacology and toxicology↗

bioRxiv2018

The interplay between small RNA pathways shapes chromatin landscape in C. elegans

The nematode C. elegans contains several types of endogenous small interfering RNAs (endo-siRNAs) produced by RNA-dependent RNA polymerase (RdRP) complexes. Both \"silencing\" siRNAs bound by Worm-specific Argonautes (WAGO) and \"activating\" siRNAs bound by the CSR-1 Argonaute require the DRH-3 helicase, an RdRP component. Here we show that, in the drh-3(ne4253) mutant deficient in RdRP-produced secondary endo-siRNAs, the silencing histone mark H3K9me3 is largely depleted, whereas in the csr-1 partial loss-of-function mutant this mark is ectopically deposited on CSR-1 target genes. Moreover, we observe ectopic H3K9me3 at enhancer elements in both drh-3 and csr-1 partial loss-of-function mutants and describe small RNAs matching enhancers. Finally, we detect accumulation of H3K27me3 at highly expressed genes in the drh-3(ne4253) mutant, which correlates with their reduced transcription. Our study shows that when abundant RdRP-produced siRNAs are depleted, there is ectopic elevation of noncoding RNAs linked to increase in silencing chromatin marks. Moreover, our results suggest that enhancer small RNAs may guide local H3K9 methylation.

molecular biology↗

bioRxiv2018

NDUFAB1 Protects Heart by Coordinating Mitochondrial Respiratory Complex and Supercomplex Assembly

The impairment of mitochondrial bioenergetics, often coupled with exaggerated reactive oxygen species (ROS) production, is emerging as a common mechanism in diseases of organs with a high demand for energy, such as the heart. Building a more robust cellular powerhouse holds promise for protecting these organs in stressful conditions. Here, we demonstrate that NDUFAB1 (NADH:ubiquinone oxidoreductase subunit AB1), acts as a powerful cardio-protector by enhancing mitochondrial energy biogenesis. In particular, NDUFAB1 coordinates the assembly of respiratory complexes I, II, and III and supercomplexes, conferring greater capacity and efficiency of mitochondrial energy metabolism. Cardiac-specific deletion of Ndufab1 in mice caused progressive dilated cardiomyopathy associated with defective bioenergetics and elevated ROS levels, leading to heart failure and sudden death. In contrast, transgenic overexpression of Ndufab1 effectively enhanced mitochondrial bioenergetics and protected the heart against ischemia-reperfusion injury. Our findings identify NDUFAB1 as a central endogenous regulator of mitochondrial energy and ROS metabolism and thus provide a potential therapeutic target for the treatment of heart failure and other mitochondrial bioenergetics-centered diseases.

cell biology↗

bioRxiv2018

IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

MotivationNext-Generation Sequencing has made available much more large-scale genomic and transcriptomic data. Studies with RNA-sequencing (RNA-seq) data typically involve generation of gene expression profiles that can be further analyzed, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.\n\nResultsHere we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.\n\nAvailabilityIRIS-DGE is freely available at http://bmbl.sdstate.edu/IRIS/.\n\nContactqin.ma@sdstate.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

ViDGER: An R package for integrative interpretation of differential gene expression results of RNA-seq data

Differential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

bioinformatics↗

bioRxiv2018

GeneQC: A quality control tool for gene expression estimation based on RNA-sequencing reads mapping

MotivationOne of the main benefits of using modern RNA-sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations on the reference genome with the same alignment scores, which occurs in plant, animal, and metagenome samples. Such a read is so-called a multiple-mapping read (MMR). The impact of these MMRs is reflected in gene expression estimation and all downstream analyses, including differential gene expression, functional enrichment, etc. Current analysis pipelines lack the tools to effectively test the reliability of gene expression estimations, thus are incapable of ensuring the validity of all downstream analyses.\n\nResultsOur investigation into 95 RNA-Seq datasets from seven species (totaling 1,951GB) indicates an average of roughly 22% of all reads are MMRs for plant and animal species. Here we present a tool called GeneQC (Gene expression Quality Control), which can accurately estimate the reliability of each genes expression level. The underlying algorithm is designed based on extracted genomic and transcriptomic features, which are then combined using elastic-net regularization and mixture model fitting to provide a clearer picture of mapping uncertainty for each gene. GeneQC allows researchers to determine reliable expression estimations and conduct further analysis on the gene expression that is of sufficient quality. This tool also enables researchers to investigate continued re-alignment methods to determine more accurate gene expression estimates for those with low reliability.\n\nAvailabilityGeneQC is freely available at http://bmbl.sdstate.edu/GeneQC/home.html.\n\nContactqin.ma@sdstate.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Computational elucidation of regulatory network responding to acid stress in Lactococcus lactis MG1363

Acid stress caused by lactate increment can lead to the growth inhibition of bacteria and yes has not been fully defined. Regulons, serve as co-regulated gene groups contribute to the transcriptional regulation of microbe genome, have the potential in understanding the underlying regulatory mechanism. Lactococcus lactis is one of the most important Gram-positive lactic acid-producing bacteria, widely used in food industry and has been proved to have advantages in oral delivery of drug and vaccine. In this study, we designed a novel computational pipeline, RECTA, for regulon prediction. The pipeline carried out differentially expressed gene prediction, gene co-expression analysis, cis-regulatory motif finding, and comparative genomic study to predict and validate regulons related to acid stress response in Lactococcus lactis MG1363. A total of 51 regulons were identified, and 14 of them have computational verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response with (i) known transcriptional factors in MEME suite database successfully mapped in Lactococcus lactis MG1363; and (ii) differentially expressed genes between pH values of 6.5 (control) and 5.1 (treatment). Validated by 36 literature confirmed acid stress response related proteins and genes, 33 genes in Lactococcus lactis MG1363 were found having orthologous genes using BLAST, associated to six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated to acid stress. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network based on regulon elucidation. The predicted resistance pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. It has a strong application power and can be effectively applied to other bacterial genomes, where the elucidation of the transcriptional regulation network is needed.

systems biology↗

bioRxiv2018

Gallic Acid Disrupts Aβ1-42 Aggregation and Rescues Cognitive Decline of APP/PS1 Transgenic Mouse

Alzheimers disease (AD) treatment represents one of the largest unmet medical needs. Developing drugs capable of preventing A{beta} aggregation is an excellent approach to prevent and treat AD. Here, we show that gallic acid (GA), a naturally occurring polyphenolic small molecule rich in grape seeds and fruits, has the capacity to alleviate cognitive decline of APP/PS1 transgenic mouse through reduction of A{beta}1-42 aggregation and neurotoxicity. Oral administration of GA not only improved the spatial reference memory and spatial working memory of early stage AD mice (4-month-old), but also significantly reduced the more severe deficits in spatial learning, reference memory, short-term recognition and spatial working memory of the late stage AD mice (9-month-old). The hippocampal long-term-potentiation (LTP) was also significantly elevated in the GA-treated late stage APP/PS1 AD mice. Atomic force microscopy (AFM), dynamic light scattering (DLS) and thioflavin T (ThT) fluorescence densitometry analyses showed that GA can reduce A{beta}1-42 aggregation from forming toxic oligomers and fibrils. Indeed, pre-incubating GA with oligomeric A{beta}1-42 reduced A{beta} 1-42-mediated intracellular calcium influx and neurotoxicity. Molecular docking studies identified that the 3,4,5-hydroxyle groups of GA were essential in noncovalently stabilizing GA binding to the Lys28-Ala42 salt bridge and the -COOH group is critical for disrupting the salt bridge of A{beta}1-42. The predicated covalent interaction through Schiff-base formation between the carbonyl group of the oxidized product and {varepsilon}-amino group of Lys16 is also critical for the disruption of A{beta}1-42 S-shaped triple-{beta}-motif and toxicity. Together, these studies demonstrated that GA can prevent and protect the AD brain through disrupting A{beta}1-42 aggregation.

neuroscience↗

bioRxiv2017

Identification of methylation and hydroxymethylation haplotype blocks as distal regulatory elements aids in deconvolution of heterogeneous brain tissues

5-Hydroxymethylcytosine (5hmC) is an oxidation product of 5-methylcytosine (5mC), and adjacent CpG sites in the mammalian genome can be co-methylated and co-hydroxymethylated due to the processivity of DNMT and TET enzymes. We applied TAB-seq and oxBS-seq to selectively detect 5hmC and 5mC, respectively, at base resolution in the mouse cortex, olfactory bulb and cerebellum tissues. We found that majority of the called 5hmC CpG sites frequently had 5mC modification simultaneously and enriched in gene body regions of neuron development related genes in brain tissues. These results supported a prominent role of oxidizing 5mC to 5hmC as new epigenetic mark. Strikingly, by a systematic search of regions that show highly coordinated methylation and hydroxymethylation (MHBs and hMHBs), we found that MHBs significantly overlapped with hMHBs in gene body regions which further supported that oxidized 5mC to 5hmC co-ordinately in a subset of cells within heterogeneous brain tissues. Consistently, using a metric called methylation haplotype load, we defined a subset of 1,361 tissue-specific MHBs and 3,818 shared MHBs which were predominantly regulatory elements, and aids in deconvolution of heterogeneous brain tissues. Our results provide new insights into the role of co-ordinately oxidized 5mC to 5hmC as distal regulatory elements may involve in regulating tissue identity.

genetics↗

bioRxiv2017

Dynamic protein aggregation regulates bacterial dormancy depth critical for antibiotic tolerance

The ability of some bacteria within a population to tolerate antibiotic treatment is often attributed to prolonged bacterial infection1-3. Unlike antibiotic resistance, which generally results from genetic mutations or plasmid transfer4,5, antibiotic tolerance usually refers to the phenomenon that a subgroup of cells can survive high dose antibiotic treatment as a result of phenotypic heterogeneity6,7. Previous studies mainly associate antibiotic tolerance with cell dormancy, by hypothesizing that the lethal effects of antibiotics are disabled due to the extremely slow metabolic and proliferation rates in dormant bacteria 8,9. However, less is known about how surviving bacteria subsequently escape from the dormant state and resuscitate, which is equally important for disease recurrence. Here we monitored the process of bacterial antibiotic tolerance and regrowth at the single-cell level, and found that each individual survival cell shows different dormancy depth, which in return regulates whether and when it can resume growth after removal of antibiotic. The persister cells are considered to be in shallow dormancy depth, while the viable but non-culturable cells (VBNC cells) are in deep dormancy depth. We further implemented time-lapse fluorescent imaging and biochemical analysis to establish that dynamic endogenous protein aggregation is an important indicator of bacterial dormancy depth. For cells to leave the dormant state and resuscitate, clearance of cellular protein aggregates and recovery of proteostasis are required. Through additional mutagenesis studies, we found the ability to recruit functional DnaK-ClpB machineries, which facilitate protein disaggregation in an ATP-dependent manner, determines the timeline (whether and when) for bacterial regrowth. Better understanding of the key factors regulating bacterial regrowth after surviving antibiotic attack could lead to new therapeutic strategies for combating bacterial antibiotic tolerance.

microbiology↗

bioRxiv2017

Genetic load and mutational meltdown in cancer cell populations

ABSRACTLarge and non-recombining genomes are prone to accumulating deleterious mutations faster than natural selection can purge (Mullers ratchet). A possible consequence would then be the extinction of small populations. Relative to most single-cell organisms, cancer cells, with large and non-recombining genomes, could be particularly susceptible to such \"mutational meltdown\". Curiously, deleterious mutations in cancer cells are rarely noticed despite the strong signals in cancer genome sequences. Here, by monitoring single-cell clones from HeLa cell lines, we characterize deleterious mutations that retard cell proliferation. The main mutational events are copy number variations (CNVs), which happen at an extraordinarily high rate of 0.29 events per cell division. The average fitness reduction, estimated to be 18% per mutation, is also very high. HeLa cell populations therefore have very substantial genetic load and, at this level, natural population would likely experience mutational meltdown. We suspect that HeLa cell populations may avoid extinction only after the population size becomes large. Because CNVs are common in most cell lines and cancer tissues, the observations hint at cancer cells vulnerability, which could be exploited by therapeutic strategies.

evolutionary biology↗