Biology subjects

Liang, L.

Publications and source records attributed to Liang, L..

9 recordsLinked to original sources

bioRxiv2018

Noninvasive prenatal test of methylmalonic academia cblC type through targeted sequencing of cell-free DNA in maternal plasma

Methylmalonic acidemia (MMA) cblC type is the most frequent inborn error of intracellular cobalamin metabolism which is caused by mutations of MMACHC gene. Non-invasive test of MMA for pregnant women facilitates safe and timely prenatal diagnosis of the disease. In our study, we aimed to design and validate a haplotype-based noninvasive prenatal test (NIPT) method for cblC type of MMA. Targeted capture sequencing using customized hybridization was performed utilizing gDNA (genomic DNA) of trios including parents and an affected proband to determine parental haplotypes associated with the mutant and wild allele. The fetal haplotype was inferred later based on the high depth sequencing data of maternal plasma as well as haplotype linkage analysis. The fetal genotypes deduced by NIPT were further validated by amniocentesis. Haplotype-based NIPT was successfully performed in 21 families. The results of NIPT of 21 families were all consistent with invasive prenatal diagnosis, which was interpreted in a blinded fashion. Three fetuses were identified as compound heterozygosity of MMACHC, 9 fetuses were carriers of MMACHC variant, and 9 fetuses were normal. These results indicated that the haplotype-based NIPT for MMA through small target capture region sequencing is technically accurate and feasible.

genetics↗

bioRxiv2018

Can we obtain in vivo transmural mean hoop stress of the aortic wall without knowing patient-specific material properties and residual deformations?

It is well known that residual deformations/stresses alter the mechanical behavior of arteries, e.g. the pressure-diameter curves. In an effort to enable personalized analysis of the aortic wall stress, approaches have been developed to incorporate experimentally-derived residual deformations into in vivo loaded geometries in finite element simulations using thick-walled models. Solid elements are typically used to account for \"bending-like\" residual deformations. Yet, the difficulty in obtaining patient-specific residual deformations and material properties has become one of the biggest challenges of these thick-walled models. In thin-walled models, fortunately, static determinacy offers an appealing prospect that allows for the calculation of the thin-walled membrane stress without patient-specific material properties. The membrane stress can be computed using forward analysis by enforcing an extremely stiff material property as penalty treatment, which is referred to as the forward penalty approach. However, thin-walled membrane elements, which have zero bending stiffness, are incompatible with the residual deformations, and therefore, it is often stated as a limitation of thin-walled models. In this paper, by comparing the predicted stresses from thin-walled models and thick-walled models, we demonstrate that the transmural mean hoop stress is the same for the two models and can be readily obtained from in vivo clinical images without knowing the patient-specific material properties and residual deformations. Computation of patient-specific mean hoop stress can be greatly simplified by using membrane model and the forward penalty approach, which may be clinically valuable.

bioengineering↗

bioRxiv2018

Estimation of in vivo constitutive parameters of the aortic wall: a machine learning approach

The patient-specific biomechanical analysis of the aorta demands the in vivo mechanical properties of individual patients. Current inverse approaches have shown the feasibility of estimating the nonlinear, anisotropic material parameters from in vivo image data using certain optimization schemes. However, since such inverse methods are dependent on iterative nonlinear optimization, these methods are highly computation-intensive, which may take weeks to complete for only a single patient, inhibiting rapid feedback for clinical use. Recently, machine learning (ML) techniques have led to revolutionary breakthroughs in many applications. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate ML algorithms to expedite the procedure of in vivo material parameter identification. In this paper, we developed a ML-based approach to identify the material parameters from three-dimensional aorta geometries obtained at two different blood pressure levels, namely systolic and diastolic geometries. The nonlinear relationship between the two loaded shapes and the constitutive parameters are established by a ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validation was used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.

bioengineering↗

bioRxiv2017

Annihilation of Methicillin-resistant Staphylococcus aureus via Photobleaching of Staphyloxanthin

Given that the dearth of new antibiotic development loads an existential burden on successful infectious disease therapy1, health organizations are calling for alternative approaches to combat methicillin-resistant Staphylococcus aureus (MRSA) infections. Here, we report a drug-free photonic approach to eliminate MRSA through photobleaching of staphyloxanthin, an indispensable membrane-bound antioxidant of S. aureus2-5. The photobleaching process, uncovered through a transient absorption imaging study and quantitated by absorption spectroscopy and mass spectrometry, decomposes staphyloxanthin and sensitizes MRSA to reactive oxygen species attack. Consequently, staphyloxanthin bleaching by low-level blue light eradicates MRSA synergistically with external or internal reactive oxygen species. The effectiveness of this synergistic therapy is validated in MRSA culture, MRSA-infected macrophage cells, S. aureus biofilms, and a mouse wound infection model. Collectively, these findings highlight broad applications of staphyloxanthin photobleaching for treatment of MRSA infections.

microbiology↗

bioRxiv2017

Molecular evolution, diversity and adaptation of H7N9 influenza A viruses in China

A novel H7N9 avian influenza virus has caused five human epidemics in China since 2013. The substantial increase in prevalence and the emergence of antigenically divergent or highly pathogenic (HP) H7N9 strains during the current outbreak raises concerns about the epizootic-potential of these viruses. Here, we investigate the evolution and adaptation of H7N9 by combining publicly available data with newly generated virus sequences isolated in Guangdong between 2015-2017. Phylogenetic analyses show that currently-circulating H7N9 viruses belong to distinct lineages with differing spatial distributions. Using ancestral sequence reconstruction and structural modelling we have identified parallel amino-acid changes on multiple separate lineages. Furthermore, we infer mutations in HA primarily occur at sites involved in receptor-recognition and/or antigenicity. We also identify seven new HP strains, which likely emerged from viruses circulating in eastern Guangdong around March 2016 and is further associated with a high rate of adaptive molecular evolution.

evolutionary biology↗

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

Free-living human cells reconfigure their chromosomes in the evolution back to uni-cellularity

Cells of multi-cellular organisms evolve toward uni-cellularity in the form of cancer and, if humans intervene, continue to evolve in cell culture. During this process, gene dosage relationships may evolve in novel ways to cope with the new environment and may regress back to the ancestral unicellular state. In this context, the evolution of sex chromosomes vis-a-vis autosomes is of particular interest. Here, we report the chromosomal evolution in ~600 cancer cell lines. Many of them jettisoned either Y or the inactive X; thus, free-living male and female cells converge by becoming \"de-sexualized\". Surprisingly, the active X often doubled, accompanied by the addition of one haploid complement of autosomes, leading to an X:A ratio of 2:3 from the extant ratio of 1:2. Theoretical modeling of the frequency distribution of X:A karyotypes suggests that the 2:3 ratio confers a higher fitness and may reflect aspects of sex chromosome evolution.

genomics↗

bioRxiv2017

Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank

Clinical and epidemiological data suggest that asthma and allergic diseases are associated. And may share a common genetic etiology. We analyzed genome-wide single-nucleotide polymorphism (SNP) data for asthma and allergic diseases in 35,783 cases and 76,768 controls of European ancestry from the UK Biobank. Two publicly available independent genome wide association studies (GWAS) were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84x10-62). Cross trait analysis identified 38 genome-wide significant loci, including novel loci such as D2HGDH and GAL2ST2. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance our understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.

genetics↗

bioRxiv2016

A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts

The Nurses Health Study (NHS), Nurses Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)).\n\nWe observed the strongest BMI association for the FTO SNP rs55872725 ({beta}=0.45, p=3.48x10-22), and using a significance level of p=0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR=2.17, 95% CI: 1.79-2.63, p=2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.

epidemiology↗