Biology subjects

Ma, C.

Publications and source records attributed to Ma, C..

215 records · Page 12Linked to original sources

bioRxiv2019

Detecting Transcriptomic Structural Variants in Heterogeneous Contexts via the Multiple Compatible Arrangements Problem

Transcriptomic structural variants (TSVs) -- structural variants that affect expressed regions -- are common, especially in cancer. Detecting TSVs is a challenging computational problem. Sample heterogeneity (including differences between alleles in diploid organisms) is a critical confounding factor when identifying TSVs. To improve TSV detection in heterogeneous RNA-seq samples, we introduce the MO_SCPCAPULTIPLEC_SCPCAP CO_SCPCAPOMPATIBLEC_SCPCAP AO_SCPCAPRRANGEMENTC_SCPCAP PO_SCPCAPROBLEMC_SCPCAP (MCAP), which seeks k genome rearrangements to maximize the number of reads that are concordant with at least one rearrangement. This directly models the situation of a heterogeneous or diploid sample. We prove that MCAP is NP-hard and provide a [Formula]-approximation algorithm for k = 1 and a [Formula]-approximation algorithm for the diploid case (k = 2) assuming an oracle for k = 1. Combining these, we obtain a [Formula]-approximation algorithm for MCAP when k = 2 (without an oracle). We also present an integer linear programming formulation for general k. We completely characterize the graph structures that require k > 1 to satisfy all edges and show such structures are prevalent in cancer samples. We evaluate our algorithms on 381 TCGA samples and 2 cancer cell lines and show improved performance compared to the state-of-the-art TSV-calling tool, SQUID.

bioinformatics↗

bioRxiv2019

A personal and population-based Egyptian genome reference

The human genome is composed of chromosomal DNA sequences consisting of bases A, C, G and T - the blueprint to implement the molecular functions that are the basis of every individuals life. Deciphering the first human genome was a consortium effort that took more than a decade and considerable cost. With the latest technological advances, determining an individuals entire personal genome with manageable cost and effort has come within reach. Although the benefits of the all-encompassing genetic information that entire genomes provide are manifold, only a small number of de novo assembled human genomes have been reported to date 1-3, and few have been complemented with population-based genetic variation 4, which is particularly important for North Africans who are not represented in current genome-wide data sets 5-7. Here, we combine long- and short-read whole-genome next-generation sequencing data with recent assembly approaches into the first de novo assembly of the genome of an Egyptian individual. The resulting assembly demonstrates well-balanced quality metrics and is complemented with high-quality variant phasing via linked reads into haploblocks, which we can associate with gene expression changes in blood. To construct an Egyptian genome reference, we further assayed genome-wide genetic variation occurring in the Egyptian population within a representative cohort of 110 Egyptian individuals. We show that differences in allele frequencies and linkage disequilibrium between Egyptians and Europeans may compromise the transferability of European ancestry-based genetic disease risk and polygenic scores, substantiating the need for multi-ethnic genetic studies and corresponding genome references. The Egyptian genome reference represents a comprehensive population data set based on a high-quality personal genome. It is a proof of concept to be considered by the many national and international genome initiatives underway. More importantly, we anticipate that the Egyptian genome reference will be a valuable resource for precision medicine targeting the Egyptian population and beyond.

genetics↗

bioRxiv2019

Detecting anomalies in RNA-seq quantification

Algorithms to infer isoform expression abundance from RNA-seq have been greatly improved in accuracy during the past ten years. However, due to incomplete reference transcriptomes, mapping errors, incomplete sequencing bias models, or mistakes made by the algorithm, the quantification model sometimes could not explain all aspects of the input read data, and misquantification can occur. Here, we develop a computational method to detect instances where a quantification model could not thoroughly explain the input. Specifically, our approach identifies transcripts where the read coverage has significant deviations from the expectation. We call these transcripts "expression anomalies", and they represent instances where the quantification estimates may be in doubt. We further develop a method to attribute the cause of anomalies to either the incompleteness of the reference transcriptome or the algorithmic mistakes, and we show that our method precisely detects misquantifications with both causes. By correcting the misquantifications that are labeled as algorithmic mistakes, the number of false predictions of differentially expressed transcripts can be reduced. Applying anomaly detection to 30 GEUVADIS and 16 Human Body Map samples, we detect 103 genes with potential unannotated isoforms. These genes tend to be longer than average, and contain a very long exon near 3' end that the unannotated isoform excludes. Anomaly detection is a new approach for investigating the expression quantification problem that may find wider use in other areas of genomics.

bioinformatics↗

bioRxiv2018

Characterization and Variation of the Rhizosphere Fungal Community Structure of Cultivated Tetraploid Cotton

Rhizosphere fungal communities exert important influential forces on plant growth and health. However, information on the dynamics of the rhizosphere fungal community structure of the worldwide economic crop, cotton (Gossypium spp.), is limited. Next-generation sequencing of nuclear ribosomal internal transcribed spacer-1 (ITS1) was used to characterize the rhizosphere fungal communities of worldwide cultivated tetraploid cotton using G. hirsutum cv. TM-1 (upland cotton) and G. barbadense cv. Hai 7124 (island cotton). Plants were grown in field soil (FS) that had been continuously cropped with cotton and nutrient-rich soil (NS) that had not been cropped. Fungal species richness, diversity, and community composition were analyzed and compared among soil resources, cotton genotypes, and developmental stages. We found that the fungal community structure between the rhizosphere and bulk soil of cotton were different and the rhizosphere fungal communities were significantly varied between FS and NS. These results suggest that cotton rhizosphere fungal community structure variation was primarily determined by the interaction of cotton roots with different soil resources. We also found that the community composition of cotton rhizosphere fungi varied significantly during different developmental stages, suggesting that developmental stages were also important factors in the dynamics of rhizosphere fungal communities for the varying dominant fungal genera of the rhizosphere. In addition, we also observed that fungal pathogens were clearly increased at certain developmental stages, suggesting a higher infection rate and a high incidence of corresponding soil-borne disease in each stage. This research illustrates the characteristics of cotton rhizosphere fungal communities and provides important information for understanding the potential influences of rhizosphere fungal communities on cotton growth and health.

ecology↗

bioRxiv2018

Sleep and Motor Control by a Basal Ganglia Circuit

From invertebrates to humans, a defining feature of sleep is behavioral immobility(Campbell and Tobler, 1984; Hendricks et al., 2000; Shaw et al., 2000). In mammals, diminished electromyographic (EMG) activity is a major criterion for both rapid eye movement (REM) and non-REM (NREM) sleep. However, the relationship between sleep and motor control at the neuronal level remains poorly understood. Here we show that regions of the basal ganglia long known to be essential for motor suppression also play a key role in sleep generation. Optogenetic or chemogenetic activation of GABAergic neurons in mouse substantia nigra pars reticulata (SNr) strongly increased both REM and NREM sleep, whereas their inactivation suppressed sleep and increased wakefulness. Analysis of natural home-cage behavior showed that mice transition sequentially through several behavioral states: locomotion, non-locomotor movement, quiet wakefulness, and sleep. Activation/inactivation of SNr neurons promoted/suppressed sleep by biasing the direction of progression through the natural behavioral sequence. Virus-mediated circuit tracing showed that SNr GABAergic neurons project to multiple wake-promoting monoaminergic cell groups in addition to the thalamus and mesencephalic locomotor region, and activating each projection promoted sleep. Within the thalamus, direct optogenetic inactivation of glutamatergic neurons is sufficient to enhance sleep, but the effect is largely restricted to the regions receiving SNr projection. Furthermore, a major source of excitatory inputs to the SNr is the subthalamic nucleus (STN), and activation of neurotensin-expressing glutamatergic neurons in the STN also promoted sleep. Together, these results demonstrate a key role of the STN-SNr basal ganglia pathway in sleep generation and reveal a novel circuit mechanism linking sleep and motor control.

neuroscience↗

bioRxiv2018

PIRD: Pan immune repertoire database

MotivationT and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database.\n\nResultsWe developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualisation and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD.\n\nAvailability and ImplementationPIRD can be freely accessed at https://db.cngb.org/pird.

immunology↗

bioRxiv2018

Mesenchymal stem cells protect retinal ganglion cells from degeneration via mitochondrial donation

Retinal ganglion cell (RGC) degeneration is extremely hard to repair or regenerate and is often coupled with mitochondrial dysfunction. Mesenchymal stem cells (MSCs)-based treatment has been demonstrated beneficial for RGC against degeneration. However, underlying mechanisms of MSC-provided RGC protection are largely unknown other than neuropectective paracrine actions. In this study, we sought to investigate whether mitochondrial donation can preserve RGC functions, in a mitochondrial Ndufs4 deficient mouse model of RGC degeneration. The results revealed intravitreal transplanted by induced pluripotent stem cell derived-MSCs (iPSC-MSC) could donate their mitochondria through crossing inner limited membrane to host RGCs. Furthermore, the donated mitochondria effectively protected against RGC death and largely preserved retinal function in Ndufs4-KO mice. Importantly, the protective effects of mitochondrial donation from MSCs were associated with management of pro-inflammatory cytokines. Our data identified a novel role of MSCs-mitochondrial donation in protection of RGC from degeneration, and highlight a viable therapeutic strategy by manipulating stem cell mitochondrial donation for the treatment of retina degeneration in future.

cell biology↗

bioRxiv2018

Disentangling the contribution of childhood and adulthood circumstances and genetics to phenotypic aging: prospective cohort study

ObjectivesTo evaluate the extent to which childhood and adulthood circumstances and genetics contribute to phenotypic aging, using a multi-system-based signature of aging that has been shown to capture mortality and morbidity risk.\n\nDesignProspective population-based cohort study.\n\nSettingUnited States (U.S.).\n\nParticipants2,339 adults (aged 51+ years) from U.S. Health and Retirement Study, who participated in the Core Survey, the 2016 Venous Blood Study, the 2015 Life History Mail Survey, the Enhanced Face-To-Face interview (2006-2016), and were part of the genetic sample.\n\nMain outcomes measurePhenotypic Age, a validated aging measure based on a linear combination of chronological age and nine multi-system biomarkers. For most analyses, we examined \"PhenoAgeAccel\", which represents phenotypic aging after accounting for chronological age (i.e. whether a person appears older [positive value] or younger [negative value] than expected, physiologically).\n\nResultsThe Shapley Value Decomposition approach revealed that together all 11 domains (four childhood and adulthood circumstances domains, five polygenic scores [PGSs] domains, demographics, and behaviors domains) accounted for about 30% of variance in PhenoAgeAccel. Among the four circumstances domains, adulthood adversity was the largest contributor (9%), while adulthood socioeconomic status (SES), childhood adversity, and childhood SES accounted for 2.8%, 2.1%, 0.7%, respectively. Collectively, all PGSs contributed 3.8% of variance in PhenoAgeAccel. Further, six subpopulations/clusters--identified using a hierarchical cluster analysis based on childhood and adulthood SES and adversity--showed differences in average levels of phenotypic aging. Finally, there was a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the most apparently disadvantaged subpopulation/cluster--suggesting a multiplicative effect of adverse environment coupled with genetic risk on phenotypic aging.\n\nConclusionsSocioenvironmental circumstances during both childhood and adulthood account for a sizable proportion of the difference in phenotypic aging among U.S. older adults. The detrimental effects may further be exacerbated among persons with a genetic predisposition to coronary artery disease.

epidemiology↗

bioRxiv2018

RNA-Seq analysis of compatible and incompatible styles of Pyrus species at the beginning of pollination

In Rosaceae, incompatible pollen can penetrate into the style during the gametophytic self-incompatibility response. It is therefore considered a stylar event rather than a stigmatic event. In this study, we explored the differences in gene expression between compatibility and incompatibility in the early stage of pollination. The self-compatible pear variety \"Jinzhuili\" is a naturally occurring bud mutant from \"Yali\", a leading Chinese native cultivar exhibiting typical gametophytic self-incompatibility. We collected the styles of Yali and Jinzhuili at 0.5 and 2 h after self-pollination and then performed high-throughput sequencing. According to the pathway enrichment analysis of the differentially expressed genes, \"plant-pathogen interaction\" was the most represented pathway. Quantitative PCR was used to validate these differential genes. The expression levels of genes related to pollen growth and disease inhibition, such as LRR (LEUCINE-RICH REPEAT EXTENSIN), resistance, and defensin, differed significantly between compatible and incompatible pollination. Interestingly, at 0.5 h, most of these genes were upregulated in the compatible pollination system compared with the incompatible pollination system. Calcium ion transport, which requires ATPase, also demonstrated upregulated expression. In summary, the self-incompatibility reaction was initiated when the pollen came into contact with the stigma.

plant biology↗

bioRxiv2018

The effector of Hippo signaling, Taz, is required for formation of the micropyle and fertilization in zebrafish

The mechanisms that ensure fertilization of eggs by a single sperm are not fully understood. In all teleosts, a channel called the micropyle is the only route of entry for sperm to enter and fertilize the egg. The micropyle forms by penetration of the developing vitelline envelope by a single specialized follicle cell, the micropylar cell, which subsequently degenerates. The mechanisms underlying micropylar cell specification and micropyle formation are poorly understood. Here, we show that an effector of the Hippo signaling pathway, the Transcriptional co-activator with a PDZ-binding domain (Taz), plays crucial roles in micropyle formation and fertilization in zebrafish. Genome editing mutants affecting taz can grow to adults, however, eggs from homozygous taz females are not fertilized even though oocytes in mutant females are histologically normal with intact animal-vegetal polarity, complete meiosis and proper ovulation. However, taz mutant eggs have no micropyle. We show that Taz protein is specifically enriched from mid-oogenesis onwards in two follicle cells located at the animal pole of the oocyte, and co-localizes with the actin and tubulin cytoskeleton. Taz protein and micropylar cell are not detected in taz mutant ovaries. Our work identifies a novel role for the Hippo/Taz pathway in micropylar cell specification in zebrafish, and uncovers the molecular basis of micropyle formation in teleosts.

developmental biology↗

bioRxiv2018

Early candidate urine biomarkers for detecting Alzheimer’s disease before beta amyloid plaque deposition in an APP (swe)/PSEN1dE9 transgenic mouse model

Alzheimers disease (AD) is an incurable age-associated neurodegenerative disorder that is characterized by irreversible progressive cognitive deficits and extensive brain damage. The identification of candidate biomarkers before beta amyloid plaque deposition occurs is therefore of great importance for the early intervention of AD. Urine, which is not regulated by homeostatic mechanisms, theoretically accumulates changes associated with AD earlier than cerebrospinal fluid and blood. In this study, an APP (swe)/PSEN1dE9 transgenic mouse model was used to identify candidate biomarkers for early AD. Urine samples were collected from 4-, 6-, and 8-month-old transgenic mouse models, and the urinary proteomes were profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The levels of 33 proteins differed significantly between wild-type and 4-month-old mice, which had not started to deposit beta amyloid plaque. Among these proteins, 16 have been associated with the mechanisms of AD, while 9 have been suggested as AD biomarkers. Our results indicated that urine proteins enable detecting AD before beta amyloid plaque deposition, which may present an opportunity for intervention.

molecular biology↗

bioRxiv2017

DeepGS: Predicting phenotypes from genotypes using Deep Learning

MotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.\n\nResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.\n\nAvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

bioinformatics↗

bioRxiv2017

PEA: an integrated R toolkit for plant epitranscriptome analysis

MotivationThe epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.\n\nResultsHere, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation, and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.\n\nAvailabilityPEA is implemented using R and available at https://github.com/cma2015/PEA.

bioinformatics↗

bioRxiv2017

Influence of APOA5 locus on the treatment efficacy of three statins: evidence from a randomized pilot study in Chinese subjects

Pharmacogenetics or pharmacogenomics approaches are important for addressing the individual variabilities of drug efficacy especially in the era of precision medicine. One particular interesting gene to investigate is APOA5 which has been repeatedly linked with the inter-individual variations of serum triglycerides. Here, we explored APOA5-statin interactions in 195 Chinese subjects randomized to rosuvastatin (5-10 mg/day), atorvastatin (10-20 mg/day), or simvastatin (40 mg/day) for 12 weeks by performing a targeted genotyping analysis of the APOA5 promoter SNP rs662799 (-1131T>C). There were no significant differences between the treatment arms for any of the statin-induced changes in clinical biomarkers. Reductions in LDL cholesterol were influenced by the APOA5 genotype in all three treatment groups. By contrast, changes in HDL cholesterol and triglycerides were only affected by the APOA5 genotype in the atorvastatin and simvastatin groups and not in the rosuvastatin group. Our results support earlier findings indicating that rosuvastatin is a better treatment option and that future studies should consider stratifying subjects not only by genetic background but also by statin type.\n\nAbbreviations

clinical trials↗

bioRxiv2017

IRF4 haploinsufficiency in a family with Whipples disease

The pathogenesis of Whipples disease (WD) remains largely unknown, as WD strikes only a very small minority of the individuals infected with Tropheryma whipplei (Tw). Asymptomatic carriage of Tw is less rare. We studied a large multiplex French kindred, containing four otherwise healthy WD patients (mean age: 76.7 years) and five healthy carriers of Tw (mean age: 55 years). We used a strategy combining genome-wide linkage analysis and whole-exome sequencing to test the hypothesis that WD is inherited in an autosomal dominant (AD) manner, with age-dependent incomplete penetrance. WD was linked to 12 genomic regions covering 27 megabases in the four patients. These regions contained only one very rare non-synonymous variation: the R98W variant of IRF4. The five Tw carriers were heterozygous for R98W. Interferon regulatory factor 4 (IRF4) is a transcription factor with pleiotropic roles in immunity. We showed that R98W was a loss-of-function allele, like only five other exceedingly rare IRF4 alleles of a total of 39 rare and common non-synonymous alleles tested. Furthermore, heterozygosity for R98W led to a distinctive pattern of transcription in leukocytes following stimulation with BCG or Tw. Finally, we found that IRF4 had evolved under purifying selection and that R98W was not dominant-negative, suggesting that the IRF4 deficiency in this kindred was due to haploinsufficiency. Overall, haploinsufficiency at the IRF4 locus selectively underlies WD in this multiplex kindred. This deficiency displays AD inheritance with incomplete penetrance, and chronic carriage probably precedes WD by several decades in Tw-infected heterozygotes.

immunology↗

bioRxiv2017

SQUID: Transcriptomic Structural Variation Detection from RNA-seq

Transcripts are frequently modified by structural variations, which leads to a fused transcript of either multiple genes (known as a fusion gene) or a gene and a previously non-transcribing sequence. Detecting these modifications (called transcriptomic structural variations, or TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to accurately predict both fusion-gene and non-fusion-gene TSVs from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model, and doubles the accuracy on simulation data compared to other approaches. With SQUID, we identified novel non-fusion-gene TSVs on TCGA samples.

bioinformatics↗

bioRxiv2017

Identification of drug candidates that enhance pyrazinamide activity from a clinical drug library

Tuberculosis (TB) remains a leading cause of morbidity and mortality globally despite the availability of the TB therapy. 1 The current TB therapy is lengthy and suboptimal, requiring a treatment time of at least 6 months for drug susceptible TB and 9-12 months (shorter Bangladesh regimen) or 18-24 months (regular regimen) for multi-drug-resistant tuberculosis (MDR-TB). 1 The lengthy therapy makes patient compliance difficult, which frequently leads to emergence of drug-resistant strains. The requirement for the prolonged treatment is thought to be due to dormant persister bacteria which are not effectively killed by the current TB drugs, except rifampin and pyrazinamide (PZA) which have higher activity against persisters. 2, 3 Therefore new therapies should address the problem of insufficient efficacy against M. tuberculosis persisters, which could cause relapse of clinical disease. 4 PZA is a critical frontline TB drug that kills persister bacteria 5 and shortens the TB treatment from 9-12 months to 6 months. 6, 7 Although several new TB drugs are showing promise in clinical studies, none can replace PZA as they all have to be used together with PZA. 7 Because of the essentiality of PZA and the high cost of developing new drugs, in this study, we explored the idea of identifying drugs that enhance the anti-persister activity of PZA as an economic alternative approach to developing new drugs for improved treatment by screening an clinical drug library against old M. tuberculosis cultures enriched with persisters.

microbiology↗