Biology subjects

Liu, M.

Publications and source records attributed to Liu, M..

27 records · Page 2Linked to original sources

bioRxiv2018

Distinct value encoding in striatal direct and indirect pathways during adaptive learning

The striatum is thought to play a central role in action selection and reinforcement, and optogenetic experiments suggest differential roles for direct- and indirect-pathway medium spiny neurons (dMSNs and iMSNs). However, the encoding of value-related information in dMSNs and iMSNs during adaptive decision-making is not well understood. We trained mice on a dynamic foraging task where they had to learn the value of different options based on their recent history of choices and outcomes. Single-cell calcium imaging in dorsomedial striatum revealed that dMSNs and iMSNs were oppositely modulated by the updated value of the different options. Additionally, we found that iMSNs were more active as animals slowed between trials, likely reflecting ongoing changes in motivational state. Together, our results demonstrate that co-activation of dMSNs and iMSNs during action initiation does not simply encode action identity, but instead reflects pathway-specific encoding of movement, motivation, and value information necessary for adaptive decision-making.

neuroscience↗

bioRxiv2018

Electron transfer dictates metabolic reprogramming in proliferating cells under hypoxia

Metabolic reprogramming extensively occurs in proliferating cancer cells. This phenomenon occurs highly heterogeneously, but its origin has remained unclear. Here we use a physicochemical concept of free electron potential (FEP) and its equation of state to profile metabolites. We demonstrate that FEP change between substrates and products exactly reflects electrons dissipated in a metabolic transformation. Based on the law of conservation of electron in chemical reactions, a function of FEP change for central metabolism in proliferating cells are further derived, and it can accurately predict metabolic behaviors under hypoxia by maximizing the cellular FEP change to consume electrons. Therefore, enabling electron transfer dictates metabolic reprogramming in hypoxic cells, which underlies the major findings in cancer metabolism and is supported by our experiments. Importantly, our model established on FEP helps to reveal a combination of promising targets to inhibit tumor growth under hypoxia by blocking electron consumption, and could also guide future studies on cancer metabolism under hypoxia.

biochemistry↗

bioRxiv2017

Brain-Computer Interfaces for Post-Stroke Motor Rehabilitation: A Meta-Analysis

ObjectiveBrain-computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here we provide a first meta-analysis evaluating the clinical effectiveness of BCI-based post-stroke motor rehabilitation.\n\nMethodsTrials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post-stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random-effects inverse variance method was used to calculate the summary effect size.\n\nResultsWe initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post-stroke survivors fulfilling the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta-analysis. Motor improvements, mostly quantified by the upper limb Fugl-Meyer Assessment (FMA-UE), exceeded the minimal clinical important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference (SMD) of 0.79 (95% CI: 0.37 to 1.20) in FMA-UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI-induced functional and structural neuroplasticity at a sub-clinical level.\n\nInterpretationWe found a medium to large effect size of BCI therapy compared to controls. This suggests that BCI technology might be an effective intervention for post-stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

neuroscience↗

bioRxiv2017

Proper Conditional Analysis in the Presence of Missing Data Identified Novel Independently Associated Low Frequency Variants in Nicotine Dependence Genes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values. In practice, genotype imputation is not always effective, e.g. when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Naive extensions of existing methods either replace missing summary statistics with 0 or discard studies with missing data. These approaches can bias genetic effect estimates and lead to seriously inflated type-I or II errors in conditional analysis, which is a critical tool for identifying independently associated variants.\n\nTo address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amount of missing values. Based on this estimator, we propose a score statistic we call PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to analyze the CHRNA5-CHRNB4-CHRNA3 locus in a large-scale meta-analysis for cigarettes-per-day. Using the new method, we identified three novel variants, independent of known association signals, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants is .46%, improving that of previously reported associations by 17%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.\n\nAUTHOR SUMMARYIt is of great interest to estimate the joint and conditional effects of multiple correlated variants from large scale meta-analysis, in order to fine map causal variants and understand the genetic architecture for complex traits. The contributed summary statistics from participating studies in a meta-analysis often contain missing values, as the imputation methods are not often effective, especially when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing meta-analysis methods do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly biased estimates of joint effects and highly inflated type-I errors for conditional analysis, which will in turn result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics in the meta-analysis contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

genetics↗

bioRxiv2017

Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data

MotivationThere is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, [~]10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results.\n\nResultsWe propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of [~]18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches.\n\nAvailabilitySoftware packages implementing these methods are available at (https://github.com/zhanxw/rvtests http://genome.sph.umich.edu/wiki/RareMETAL).\n\nContactxiaowei.zhan@utsouthwestem.edu; dajiang.liu@psu.edu

bioinformatics↗

bioRxiv2017

Porcine commensal Escherichia coli: A reservoir for class 1 integrons associated with IS26

Porcine faecal waste is a serious environmental pollutant. Carriage of antimicrobial resistance and virulence-associated genes (VAGs) and the zoonotic potential of commensal Escherichia coli from swine is largely unknown. Furthermore, little is known about the role of commensal E. coli as contributors to the mobilisation of antimicrobial resistance genes between food animals and the environment. Here, we report whole genome sequence analysis of 141 E. coli from the faeces of healthy pigs. Most strains belonged to phylogroups A and B1 and carried i) a class 1 integron; ii) VAGs linked with extraintestinal infection in humans; iii) antimicrobial resistance genes blaTEM, aphAl, cmlA, strAB, tet(A)A, dfrA12, dfrA5, sul1, sul2, sul3; iv) IS26; and v) heavy metal resistance genes (merA, cusA, terA). Carriage of the sulphonamide resistance gene sul3 was notable in this study. The 141 strains belonged to 42 multilocus sequence types, but clonal complex 10 featured prominently. Structurally diverse class 1 integrons that were frequently associated with IS26 carried unique genetic features that were also identified in extraintestinal pathogenic E. coli (ExPEC) from humans. This study provides the first detailed genomic analysis and point of reference for commensal E. coli of porcine origin, facilitating tracking of specific lineages and the mobile resistance genes they carry.\n\nConflict of Interest StatementNone to declare.

genomics↗

bioRxiv2017

The pomegranate (Punica granatum L.) genome provides insights into fruit quality and ovule developmental biology

Pomegranate (Punica granatum L.) with an uncertain taxonomic status has an ancient cultivation history, and has become an emerging fruit due to its attractive features such as the bright red appearance and the high abundance of medicinally valuable ellagitannin-based compounds in its peel and aril. However, the absence of genomic resources has restricted further elucidating genetics and evolution of these interesting traits. Here we report a 274-Mb high-quality draft pomegranate genome sequence, which covers approximately 81.5% of the estimated 336 Mb genome, consists of 2,177 scaffolds with an N50 size of 1.7 Mb, and contains 30,903 genes. Phylogenomic analysis supported that pomegranate belongs to the Lythraceae family rather than the monogeneric Punicaceae family, and comparative analyses showed that pomegranate and Eucalyptus grandis shares the paleotetraploidy event. Integrated genomic and transcriptomic analyses provided insights into the molecular mechanisms underlying the biosynthesis of ellagitannin-based compounds, the color formation in both peels and arils during pomegranate fruit development, and the unique ovule development processes that are characteristic of pomegranate. This genome sequence represents the first reference in Lythraceae, providing an important resource to expand our understanding of some unique biological processes and to facilitate both comparative biology studies and crop breeding.

genomics↗

bioRxiv2017

A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images

Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.

bioengineering↗

bioRxiv2017

Evaluation of ddRADseq for reduced representation metagenome sequencing

Background Who is doing what is the ultimate open question in microbiome study. Shotgun metagenomics is often applied to gain knowledge of functional roles for bacteria in microbial communities, where the data can be used to predict protein encoding genes and enzymatic pathways present in the community, sometimes leading to testable hypotheses for microbial function. We describe a method and basic analysis for a metagenomic adaptation of the double digest restriction site associated DNA sequencing (ddRADseq) protocol for reduced representation metagenome profiling. This technique takes advantage of the sequence specificity of restriction endonucleases to construct an Illumina-compatible sequencing library containing DNA fragments that are between a pair of restriction sites located within close proximity. This results in a reduced sequencing library with coverage breadth that can be tuned by size selection.\n\nResultsWe assessed the performance of the metagenomic ddRADseq approach by applying the method to human stool samples and generating sequence data. We evaluate the extent to which ddRADseq data provides an unbiased reduced representation for microbiome profiling.\n\nConclusionAlthough ddRADseq does introduce some bias in taxonomic representation, the bias is likely to be small relative to DNA extraction bias. ddRADseq appears feasible and could have value as a tool for metagenome-wide association studies.

genomics↗