Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet.

Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging. Computational methods have been developed to predict surface protein expression using gene expression information such as from single-cell RNA sequencing (scRNA-seq) data. Existing methods however are computationally demanding and lack the interpretability to reveal underlying biological processes. We propose CrossmodalNet, an interpretable machine learning model, to predict surface protein expression from scRNA-seq data. Our model with a customized adaptive loss accurately predicts surface protein abundances. When samples from multiple time points are given, our model encodes temporal information into an easy-to-interpret time embedding to make prediction in a time-point-specific manner, and is able to uncover noise-free causal gene-protein relationships. Using three publicly available time-resolved CITE-seq data sets, we validate the performance of our model by comparing it with benchmarking methods and evaluate its interpretability. Together, we show that our method accurately and interpretably profiles surface protein expression using scRNA-seq data, thereby expanding the capacity of CITE-seq experiments for investigating molecular mechanisms involving surface proteins.

Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

Identification of a copper-responsive small molecule inhibitor of uropathogenic Escherichia coli.

Urinary tract infections (UTIs) are a major global health problem and are caused predominantly by uropathogenic Escherichia coli (UPEC). UTIs are a leading cause of prescription antimicrobial use. Incessant increase in antimicrobial resistance in UPEC and other uropathogens poses a serious threat to the current treatment practices. Copper is an effector of nutritional immunity that impedes the growth of pathogens during infection. We hypothesized that copper would augment the toxicity of select small molecules against bacterial pathogens. We conducted a small molecule screening campaign with a library of 51,098 molecules to detect hits that inhibit a UPEC ΔtolC mutant in a copper-dependent manner. A molecule, denoted as E. coli inhibitor or ECIN, was identified as a copper-responsive inhibitor of wild-type UPEC strains. Our gene expression and metal content analysis results demonstrate that ECIN works in concert with copper to exacerbate Cu toxicity in UPEC. ECIN has a broad spectrum of activity against pathogens of medical and veterinary significance including Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. Subinhibitory levels of ECIN eliminate UPEC biofilm formation. Transcriptome analysis of UPEC treated with ECIN reveals induction of multiple stress response systems. Furthermore, we demonstrate that L-cysteine rescues the growth of UPEC exposed to ECIN. In summary, we report the identification and characterization of a novel copper-responsive small molecule inhibitor of UPEC.IMPORTANCEUrinary tract infection (UTI) is a ubiquitous infectious condition affecting millions of people annually. Uropathogenic Escherichia coli (UPEC) is the predominant etiological agent of UTI. However, UTIs are becoming increasingly difficult to resolve with antimicrobials due to increased antimicrobial resistance in UPEC and other uropathogens. Here, we report the identification and characterization of a novel copper-responsive small molecule inhibitor of UPEC. In addition to E. coli, this small molecule also inhibits pathogens of medical and veterinary significance including Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus.

Hepatocyte Adenosine Kinase Promotes Excessive Fat Deposition and Liver Inflammation.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease is highly associated with obesity and progresses to nonalcoholic steatohepatitis when the liver develops overt inflammatory damage. While removing adenosine in the purine salvage pathway, adenosine kinase (ADK) regulates methylation reactions. We aimed to study whether hepatocyte ADK functions as an obesogenic gene/enzyme to promote excessive fat deposition and liver inflammation. METHODS: Liver sections of human subjects were examined for ADK expression using immunohistochemistry. Mice with hepatocyte-specific ADK disruption or overexpression were examined for hepatic fat deposition and inflammation. Liver lipidomics, hepatocyte RNA sequencing (RNA-seq), and single-cell RNA-seq for liver nonparenchymal cells were performed to analyze ADK regulation of hepatocyte metabolic responses and hepatocyte-nonparenchymal cells crosstalk. RESULTS: Whereas patients with nonalcoholic fatty liver disease had increased hepatic ADK levels, mice with hepatocyte-specific ADK disruption displayed decreased hepatic fat deposition on a chow diet and were protected from diet-induced excessive hepatic fat deposition and inflammation. In contrast, mice with hepatocyte-specific ADK overexpression displayed increased body weight and adiposity and elevated degrees of hepatic steatosis and inflammation compared with control mice. RNA-seq and epigenetic analyses indicated that ADK increased hepatic DNA methylation and decreased hepatic Ppara expression and fatty acid oxidation. Lipidomic and single-cell RNA-seq analyses indicated that ADK-driven hepatocyte factors, due to mitochondrial dysfunction, enhanced macrophage proinflammatory activation in manners involving increased expression of stimulator of interferon genes. CONCLUSIONS: Hepatocyte ADK functions to promote excessive fat deposition and liver inflammation through suppressing hepatocyte fatty acid oxidation and producing hepatocyte-derived proinflammatory mediators. Therefore, hepatocyte ADK is a therapeutic target for managing obesity and nonalcoholic fatty liver disease.

Single-nucleus transcriptomics of epicardial adipose tissue from female pigs reveals effects of exercise training on resident innate and adaptive immune cells.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death in women. Epicardial adipose tissue (EAT) secretes cytokines to modulate coronary artery function, and the release of fatty acids from EAT serves as a readily available energy source for cardiomyocytes. However, despite having beneficial functions, excessive amounts of EAT can cause the secretion of proinflammatory molecules that increase the instability of atherosclerotic plaques and contribute to CAD progression. Although exercise mitigates CAD, the mechanisms by which exercise impacts EAT are unknown. The Yucatan pig is an excellent translational model for the effects of exercise on cardiac function. Therefore, we sought to determine if chronic aerobic exercise promotes an anti-inflammatory microenvironment in EAT from female Yucatan pigs. METHODS: Sexually mature, female Yucatan pigs (n = 7 total) were assigned to sedentary (Sed, n = 3) or exercise (Ex, n = 4) treatments, and coronary arteries were occluded (O) with an ameroid to mimic CAD or remained non-occluded (N). EAT was collected for bulk (n = 7 total) and single nucleus transcriptomic sequencing (n = 2 total, 1 per exercise treatment). RESULTS: Based on the bulk transcriptomic analysis, exercise upregulated S100 family, G-protein coupled receptor, and CREB signaling in neurons canonical pathways in EAT. The top networks in EAT affected by exercise as measured by bulk RNA sequencing were SRC kinase family, fibroblast growth factor receptor, Jak-Stat, and vascular endothelial growth factor. Single nucleus transcriptomic analysis revealed that exercise increased the interaction between immune, endothelial, and mesenchymal cells in the insulin-like growth factor pathway and between endothelial and other cell types in the platelet endothelial cell adhesion molecule 1 pathway. Sub-clustering revealed nine cell types in EAT, with fibroblast and macrophage populations predominant in O-Ex EAT and T cell populations predominant in N-Ex EAT. Unlike the findings for exercise alone as a treatment, there were not increased interactions between endothelial and mesenchymal cells in O-Ex EAT. Coronary artery occlusion impacted the most genes in T cells and endothelial cells. Genes related to fatty acid metabolism were the most highly upregulated in non-immune cells from O-Ex EAT. Sub-clustering of endothelial cells revealed that N-Ex EAT separated from other treatments. CONCLUSIONS: According to bulk transcriptomics, exercise upregulated pathways and networks related to growth factors and immune cell communication. Based on single nucleus transcriptomics, aerobic exercise increased cell-to-cell interaction amongst immune, mesenchymal, and endothelial cells in female EAT. Yet, exercise was minimally effective at reversing alterations in gene expression in endothelial and mesenchymal cells in EAT surrounding occluded arteries. These findings lay the foundation for future work focused on the impact of exercise on cell types in EAT.

rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database.

MOTIVATION: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. RESULTS: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. AVAILABILITY AND IMPLEMENTATION: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

Site-specific and substrate-specific control of accurate mRNA editing by a helicase complex in trypanosomes.

Trypanosome U-insertion/deletion RNA editing in mitochondrial mRNAs involves guide RNAs (gRNAs) and the auxiliary RNA editing substrate binding complex (RESC) and RNA editing helicase 2 complex (REH2C). RESC and REH2C stably copurify with editing mRNAs but the functional interplay between these complexes remains unclear. Most steady-state mRNAs are partially edited and include misedited "junction" regions that match neither pre-mRNA nor fully edited transcripts. Editing specificity is central to mitochondrial RNA maturation and function, but its basic control mechanisms remain unclear. Here we applied a novel nucleotide-resolution RNA-seq approach to examine ribosomal protein subunit 12 (RPS12) and ATPase subunit 6 (A6) mRNA transcripts. We directly compared transcripts associated with RESC and REH2C to those found in total mitochondrial RNA. RESC-associated transcripts exhibited site-preferential enrichments in total and accurate edits. REH2C loss-of-function induced similar substrate-specific and site-specific editing effects in total and RESC-associated RNA. It decreased total editing primarily at RPS12 5' positions but increased total editing at examined A6 3' positions. REH2C loss-of-function caused site-preferential loss of accurate editing in both transcripts. However, changes in total or accurate edits did not necessarily involve common sites. A few 5' nucleotides of the initiating gRNA (gRNA-1) directed accurate editing in both transcripts. However, in RPS12, two conserved 3'-terminal adenines in gRNA-1 could direct a noncanonical 2U-insertion that causes major pausing in 3'-5' progression. In A6, a noncanonical sequence element that depends on REH2C in a region normally targeted by the 3' half of gRNA-1 may hinder early editing progression. Overall, we defined transcript-specific effects of REH2C loss.

Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control.

MOTIVATION: Quality control (QC) is a critical step in single-cell RNA-seq (scRNA-seq) data analysis. Low-quality cells are removed from the analysis during the QC process to avoid misinterpretation of the data. An important QC metric is the mitochondrial proportion (mtDNA%), which is used as a threshold to filter out low-quality cells. Early publications in the field established a threshold of 5% and since then, it has been used as a default in several software packages for scRNA-seq data analysis, and adopted as a standard in many scRNA-seq studies. However, the validity of using a uniform threshold across different species, single-cell technologies, tissues and cell types has not been adequately assessed. RESULTS: We systematically analyzed 5 530 106 cells reported in 1349 annotated datasets available in the PanglaoDB database and found that the average mtDNA% in scRNA-seq data across human tissues is significantly higher than in mouse tissues. This difference is not confounded by the platform used to generate the data. Based on this finding, we propose new reference values of the mtDNA% for 121 tissues of mouse and 44 tissues of humans. In general, for mouse tissues, the 5% threshold performs well to distinguish between healthy and low-quality cells. However, for human tissues, the 5% threshold should be reconsidered as it fails to accurately discriminate between healthy and low-quality cells in 29.5% (13 of 44) tissues analyzed. We conclude that omitting the mtDNA% QC filter or adopting a suboptimal mtDNA% threshold may lead to erroneous biological interpretations of scRNA-seq data. AVAILABILITYAND IMPLEMENTATION: The code used to download datasets, perform the analyzes and produce the figures is available at https://github.com/dosorio/mtProportion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gag-like proteins: Novel mediators of prenatal alcohol exposure in neural development.

BACKGROUND: We previously showed that ethanol did not kill fetal neural stem cells (NSCs), but that their numbers nevertheless are decreased due to aberrant maturation and loss of self-renewal. To identify mechanisms that mediate this loss of NSCs, we focused on a family of Gag-like proteins (GLPs), derived from retroviral gene remnants within mammalian genomes. GLPs are important for fetal development, though their role in brain development is virtually unexplored. Moreover, GLPs may be transferred between cells in extracellular vesicles (EVs) and thereby transfer environmental adaptations between cells. We hypothesized that GLPs may mediate some effects of ethanol in NSCs. METHODS: Sex-segregated male and female fetal murine cortical NSCs, cultured ex vivo as nonadherent neurospheres, were exposed to a dose range of ethanol and to mitogen-withdrawal-induced differentiation. We used siRNAs to assess the effects of NSC-expressed GLP knockdown on growth, survival, and maturation and in silico GLP knockout, in an in vivo single-cell RNA-sequencing dataset, to identify GLP-mediated developmental pathways that were also ethanol-sensitive. RESULTS: PEG10 isoform-1, isoform-2, and PNMA2 were identified as dominant GLP species in both NSCs and their EVs. Ethanol-exposed NSCs exhibited significantly elevated PEG10 isoform-2 and PNMA2 protein during differentiation. Both PEG10 and PNMA2 were mediated apoptosis resistance and additionally, PEG10 promoted neuronal and astrocyte lineage maturation. Neither GLP influenced metabolism nor cell cycle in NSCs. Virtual PEG10 and PNMA2 knockout identified gene transcription regulation and ubiquitin-ligation processes as candidate mediators of GLP-linked prenatal alcohol effects. CONCLUSIONS: Collectively, GLPs present in NSCs and their EVs may confer apoptosis resistance within the NSC niche and contribute to the abnormal maturation induced by ethanol.

Adipose tissue inflammation and systemic insulin resistance in mice with diet-induced obesity is possibly associated with disruption of PFKFB3 in hematopoietic cells.

Obesity-associated inflammation in white adipose tissue (WAT) is a causal factor of systemic insulin resistance; however, precisely how immune cells regulate WAT inflammation in relation to systemic insulin resistance remains to be elucidated. The present study examined a role for 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) in hematopoietic cells in regulating WAT inflammation and systemic insulin sensitivity. Male C57BL/6J mice were fed a high-fat diet (HFD) or low-fat diet (LFD) for 12 weeks and examined for WAT inducible 6-phosphofructo-2-kinase (iPFK2) content, while additional HFD-fed mice were treated with rosiglitazone and examined for PFKFB3 mRNAs in WAT stromal vascular cells (SVC). Also, chimeric mice in which PFKFB3 was disrupted only in hematopoietic cells and control chimeric mice were also fed an HFD and examined for HFD-induced WAT inflammation and systemic insulin resistance. In vitro, adipocytes were co-cultured with bone marrow-derived macrophages and examined for adipocyte proinflammatory responses and insulin signaling. Compared with their respective levels in controls, WAT iPFK2 amount in HFD-fed mice and WAT SVC PFKFB3 mRNAs in rosiglitazone-treated mice were significantly increased. When the inflammatory responses were analyzed, peritoneal macrophages from PFKFB3-disrputed mice revealed increased proinflammatory activation and decreased anti-inflammatory activation compared with control macrophages. At the whole animal level, hematopoietic cell-specific PFKFB3 disruption enhanced the effects of HFD feeding on promoting WAT inflammation, impairing WAT insulin signaling, and increasing systemic insulin resistance. In vitro, adipocytes co-cultured with PFKFB3-disrupted macrophages revealed increased proinflammatory responses and decreased insulin signaling compared with adipocytes co-cultured with control macrophages. These results suggest that PFKFB3 disruption in hematopoietic cells only exacerbates HFD-induced WAT inflammation and systemic insulin resistance.

scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technology has revolutionized the way research is done in biomedical sciences. It provides an unprecedented level of resolution across individual cells for studying cell heterogeneity and gene expression variability. Analyzing scRNA-seq data is challenging though, due to the sparsity and high dimensionality of the data. RESULTS: I developed scGEAToolbox-a Matlab toolbox for scRNA-seq data analysis. It contains a comprehensive set of functions for data normalization, feature selection, batch correction, imputation, cell clustering, trajectory/pseudotime analysis, and network construction, which can be combined and integrated to building custom workflow. While most of the functions are implemented in native Matlab, wrapper functions are provided to allow users to call the "third-party" tools developed in Matlab or other languages. Furthermore, scGEAToolbox is equipped with sophisticated graphical user interfaces (GUIs) generated with App Designer, making it an easy-to-use application for quick data processing. AVAILABILITY: https://github.com/jamesjcai/scGEAToolbox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AEGS: identifying aberrantly expressed gene sets for differential variability analysis.

Motivation: In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g. the changes in expression variance) between groups may reveal underlying genetic heterogeneity and undetected interactions, which has great implications in many fields of biology. An easy-to-use tool for DV analysis is needed. Results: We develop AEGS for DV analysis, to identify aberrantly expressed gene sets in diseased cases but not in controls. AEGS can rank individual genes in an aberrantly expressed gene set by each gene's relative contribution to the total degree of aberrant expression, prioritizing top genes. AEGS can be used for discovering gene sets with disease-specific expression variability changes. Availability and implementation: AEGS web server is accessible at http://bmi.xmu.edu.cn:8003/AEGS, where a stand-alone AEGS application can also be downloaded. Contact: glji@xmu.edu.cn.

Simulating variance heterogeneity in quantitative genome wide association studies.

BACKGROUND: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. RESULTS: A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. CONCLUSIONS: We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action.

Increasing evidence shows that phenotypic variance is genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e. genomic loci associated with gene expression variance, in humans. We discovered that common genetic variants may contribute to increasing gene expression variance via two distinct modes of action­epistasis and destabilization. Specifically, epistasis explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of 'third-party' eQTLs that influence the gene expression mean in a fraction, rather than the entire set, of sampled individuals. On the other hand, the destabilization model explains the other three-quarters of evQTLs, caused by mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and estimated the stability of gene expression in single samples using repetitive qRT-PCR assays. The mutations that cause destabilizing evQTLs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common genetic variants work either interactively or independently to shape the variability of gene expression in humans. Our findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variance-centred analytic methods for quantitative trait mapping.

The lncRNA RZE1 Controls Cryptococcal Morphological Transition.

In the fungal pathogen Cryptococcus neoformans, the switch from yeast to hypha is an important morphological process preceding the meiotic events during sexual development. Morphotype is also known to be associated with cryptococcal virulence potential. Previous studies identified the regulator Znf2 as a key decision maker for hypha formation and as an anti-virulence factor. By a forward genetic screen, we discovered that a long non-coding RNA (lncRNA) RZE1 functions upstream of ZNF2 in regulating yeast-to-hypha transition. We demonstrate that RZE1 functions primarily in cis and less effectively in trans. Interestingly, RZE1's function is restricted to its native nucleus. Accordingly, RZE1 does not appear to directly affect Znf2 translation or the subcellular localization of Znf2 protein. Transcriptome analysis indicates that the loss of RZE1 reduces the transcript level of ZNF2 and Znf2's prominent downstream targets. In addition, microscopic examination using single molecule fluorescent in situ hybridization (smFISH) indicates that the loss of RZE1 increases the ratio of ZNF2 transcripts in the nucleus versus those in the cytoplasm. Taken together, this lncRNA controls Cryptococcus yeast-to-hypha transition through regulating the key morphogenesis regulator Znf2. This is the first functional characterization of a lncRNA in a human fungal pathogen. Given the potential large number of lncRNAs in the genomes of Cryptococcus and other fungal pathogens, the findings implicate lncRNAs as an additional layer of genetic regulation during fungal development that may well contribute to the complexity in these "simple" eukaryotes.

Aberrant gene expression in humans.

Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Signature gene expression reveals novel clues to the molecular mechanisms of dimorphic transition in Penicillium marneffei.

Systemic dimorphic fungi cause more than one million new infections each year, ranking them among the significant public health challenges currently encountered. Penicillium marneffei is a systemic dimorphic fungus endemic to Southeast Asia. The temperature-dependent dimorphic phase transition between mycelium and yeast is considered crucial for the pathogenicity and transmission of P. marneffei, but the underlying mechanisms are still poorly understood. Here, we re-sequenced P. marneffei strain PM1 using multiple sequencing platforms and assembled the genome using hybrid genome assembly. We determined gene expression levels using RNA sequencing at the mycelial and yeast phases of P. marneffei, as well as during phase transition. We classified 2,718 genes with variable expression across conditions into 14 distinct groups, each marked by a signature expression pattern implicated at a certain stage in the dimorphic life cycle. Genes with the same expression patterns tend to be clustered together on the genome, suggesting orchestrated regulations of the transcriptional activities of neighboring genes. Using qRT-PCR, we validated expression levels of all genes in one of clusters highly expressed during the yeast-to-mycelium transition. These included madsA, a gene encoding MADS-box transcription factor whose gene family is exclusively expanded in P. marneffei. Over-expression of madsA drove P. marneffei to undergo mycelial growth at 37°C, a condition that restricts the wild-type in the yeast phase. Furthermore, analyses of signature expression patterns suggested diverse roles of secreted proteins at different developmental stages and the potential importance of non-coding RNAs in mycelium-to-yeast transition. We also showed that RNA structural transition in response to temperature changes may be related to the control of thermal dimorphism. Together, our findings have revealed multiple molecular mechanisms that may underlie the dimorphic transition in P. marneffei, providing a powerful foundation for identifying molecular targets for mechanism-based interventions.

Genome-wide association study to identify potential genetic modifiers in a canine model for Duchenne muscular dystrophy.

BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive muscle degeneration, cardiomyopathy and respiratory failure in approximately 1/5,000 boys. Golden Retriever muscular dystrophy (GRMD) resembles DMD both clinically and pathologically. Like DMD, GRMD exhibits remarkable phenotypic variation among affected dogs, suggesting the influence of modifiers. Understanding the role(s) of genetic modifiers of GRMD may identify genes and pathways that also modify phenotypes in DMD and reveal novel therapies. Therefore, our objective in this study was to identify genetic modifiers that affect discrete GRMD phenotypes. RESULTS: We performed a linear mixed-model (LMM) analysis using 16 variably-affected dogs from our GRMD colony (8 dystrophic, 8 non-dystrophic). All of these dogs were either full or half-siblings, and phenotyped for 19 objective, quantitative biomarkers at ages 6 and 12 months. Each biomarker was individually assessed. Gene expression profiles of 59 possible candidate genes were generated for two muscle types: the cranial tibialis and medial head of the gastrocnemius. SNPs significantly associated with GRMD biomarkers were identified on multiple chromosomes (including the X chromosome). Gene expression levels for candidate genes located near these SNPs correlated with biomarker values, suggesting possible roles as GRMD modifiers. CONCLUSIONS: The results of this study enhance our understanding of GRMD pathology and represent a first step toward the characterization of GRMD modifiers that may be relevant to DMD pathology. Such modifiers are likely to be useful for DMD treatment development based on their relationships to GRMD phenotypes.