Publications by authors named "Jianguo Xia"

91 Publications

Application of Ultrasonography in the Diagnosis of Rhabdomyolysis.

Ultrasound Med Biol 2021 Sep 14. Epub 2021 Sep 14.

Department of Ultrasonography, Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. Electronic address:

Early diagnosis and treatment of rhabdomyolysis are very important, but difficult to achieve for some atypical cases. Our study was aimed at determining the diagnostic value of ultrasonography in rhabdomyolysis caused by different factors. The study enrolled 50 patients with rhabdomyolysis diagnosed in our hospital. Among the 50 cases (mean age = 38.4 y, 22 women) of rhabdomyolysis, 26 cases (mean age = 35.5 y, 8 women) were induced by exercise. During the patients' first visit, 5 cases (mean age = 30.6 y, 1 woman) were suspected of having rhabdomyolysis and were diagnosed by clinicians; 12 cases (mean age = 34.8 y, 5 women) were correctly diagnosed under ultrasound; and 9 cases (mean age = 39.2 y, 2 women) were misdiagnosed. Ultrasound did not play a critical role in 24 patients (mean age = 41.5 y, 14 women) with rhabdomyolysis caused by trauma, infection, crayfish consumption, drugs, alcohol and heat stroke. We then concluded that exercise-induced rhabdomyolysis is a common type of rhabdomyolysis. Ultrasonography plays an important role in the early diagnosis of exercise-induced rhabdomyolysis but has limited value in the diagnosis of rhabdomyolysis caused by other etiologies.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2021.08.012DOI Listing
September 2021

Construction of a Territorial Space Classification System Based on Spatiotemporal Heterogeneity of Land Use and Its Superior Territorial Space Functions and Their Dynamic Coupling: Case Study on Qionglai City of Sichuan Province, China.

Int J Environ Res Public Health 2021 Aug 27;18(17). Epub 2021 Aug 27.

College of Resources, Sichuan Agricultural University, Chengdu 611130, China.

Territorial space classification (TSC) provides the basis for establishing systems of national territory spatial planning (NTSP) and supervising their implementation in China, thus has important theoretical and application significance. Most of the current TSC research is related to land use/land cover classification, ignoring the connection of the NTSP policies and systems, failing to consider the spatiotemporal heterogeneity of land use superior territorial space functions (TSFs) and the dynamic coupling between land use and its superior TSFs on the result of TSC. In this study, we integrated the factors influencing the connection of NTSP policies and systems and established a theoretical framework system of TSC from the perspective of spatial form and functional use. By integrating the q-statistic method with spatiotemporal geographical analysis, we propose a method to construct a TSC system for Qionglai City of Sichuan Province in China based on the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs. It makes up for the deficiency of directly taking land use/land cover classification as TSC and solves the problems of ignoring the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs. Using this method, we found that the TSC of Qionglai City consists of 3, 7, and 14 first-, second-, and third-level space types, respectively. The key findings from this study are that land use superior TSFs show spatiotemporal heterogeneity in Qionglai, and coupling effects in spatial distribution were noted between land use types and their superior TSFs, as was temporal heterogeneity in the coupling degree and the structure of the TSFs corresponding to the land use types, which show obvious dynamics and non-stationarity of the functional structure. These findings confirm the necessity of considering the spatiotemporal heterogeneity of land use superior TSFs and the dynamic coupling between land use and its superior TSFs in TSC. This method of establishing a TSC system can be used to address a number of NTSP and management issues, and three examples are provided here: (a) zoning of urban, agricultural, and ecological space; (b) use planning of production, living and ecological space; (c) delimitation of urban development boundary, permanent basic farmland protection redline, and ecological protection redline.
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http://dx.doi.org/10.3390/ijerph18179052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431555PMC
August 2021

Using Transcriptomics and Metabolomics to Understand Species Differences in Sensitivity to Chlorpyrifos in Japanese Quail and Double-Crested Cormorant Embryos.

Environ Toxicol Chem 2021 Jul 22. Epub 2021 Jul 22.

Ecotoxicology and Wildlife Health Division, Environment Canada, National Wildlife Research Centre, Carleton University, Ottawa, Ontario, Canada.

Modern 21st-century toxicity testing makes use of omics technologies to address critical questions in toxicology and chemical management. Of interest are questions relating to chemical mechanisms of toxicity, differences in species sensitivity, and translation of molecular effects to observable apical endpoints. Our study addressed these questions by comparing apical outcomes and multiple omics responses in early-life stage exposure studies with Japanese quail (Coturnix japonica) and double-crested cormorant (Phalacrocorax auritus), representing a model and ecological species, respectively. Specifically, we investigated the dose-dependent response of apical outcomes as well as transcriptomics and metabolomics in the liver of each species exposed to chlorpyrifos, a widely used organophosphate pesticide. Our results revealed a clear pattern of dose-dependent disruption of gene expression and metabolic profiles in Japanese quail but not double-crested cormorant at similar chlorpyrifos exposure concentrations. The difference in sensitivity between species was likely due to higher metabolic transformation of chlorpyrifos in Japanese quail compared to double-crested cormorant. The most impacted biological pathways after chlorpyrifos exposure in Japanese quail included hepatic metabolism, oxidative stress, endocrine disruption (steroid and nonsteroid hormones), and metabolic disease (lipid and fatty acid metabolism). Importantly, we show consistent responses across biological scales, suggesting that significant disruption at the level of gene expression and metabolite profiles leads to observable apical responses at the organism level. Our study demonstrates the utility of evaluating effects at multiple biological levels of organization to understand how modern toxicity testing relates to outcomes of regulatory relevance, while also highlighting important, yet poorly understood, species differences in sensitivity to chemical exposure. Environ Toxicol Chem 2021;00:1-15. © 2021 SETAC.
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http://dx.doi.org/10.1002/etc.5174DOI Listing
July 2021

Assessing the Toxicity of 17α-Ethinylestradiol in Rainbow Trout Using a 4-Day Transcriptomics Benchmark Dose (BMD) Embryo Assay.

Environ Sci Technol 2021 08 22;55(15):10608-10618. Epub 2021 Jul 22.

Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5B3.

There is an urgent demand for more efficient and ethical approaches in ecological risk assessment. Using 17α-ethinylestradiol (EE2) as a model compound, this study established an embryo benchmark dose (BMD) assay for rainbow trout (RBT; ) to derive transcriptomic points-of-departure (tPODs) as an alternative to live-animal tests. Embryos were exposed to graded concentrations of EE2 (measured: 0, 1.13, 1.57, 6.22, 16.3, 55.1, and 169 ng/L) from hatch to 4 and up to 60 days post-hatch (dph) to assess molecular and apical responses, respectively. Whole proteome analyses of alevins did not show clear estrogenic effects. In contrast, transcriptomics revealed responses that were in agreement with apical effects, including excessive accumulation of intravascular and hepatic proteinaceous fluid and significant increases in mortality at 55.1 and 169 ng/L EE2 at later time points. Transcriptomic BMD analysis estimated the median of the 20th lowest geneBMD to be 0.18 ng/L, the most sensitive tPOD. Other estimates (0.78, 3.64, and 1.63 ng/L for the 10th percentile geneBMD, first peak geneBMD distribution, and median geneBMD of the most sensitive over-represented pathway, respectively) were within the same order of magnitude as empirically derived apical PODs for EE2 in the literature. This 4-day alternative RBT embryonic assay was effective in deriving tPODs that are protective of chronic effects of EE2.
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http://dx.doi.org/10.1021/acs.est.1c02401DOI Listing
August 2021

Identify the triple-negative and non-triple-negative breast cancer by using texture features of medicale ultrasonic image: A STROBE-compliant study.

Medicine (Baltimore) 2021 Jun;100(22):e25878

Department of Nuclear Medicine, Taizhou people's Hospital affiliated to Medical College of Yangzhou University Taizhou, China.

Abstract: The study aimed to explore the value of ultrasound (US) texture analysis in the differential diagnosis of triple-negative breast cancer (TNBC) and non-TNBC.Retrospective analysis was done on 93 patients with breast cancer (35 patients with TNBC and 38 patients with non-TNBC) who were admitted to Taizhou people's hospital from July 2015 to June 2019. All lesions were pathologically proven at surgery. US images of all patients were collected. Texture analysis of US images was performed using MaZda software package. The differences between textural features in TNBC and non-TNBC were assessed. Receiver operating characteristic curve analysis was used to compare the diagnostic performance of textural parameters showing significant difference.Five optimal texture feature parameters were extracted from gray level run-length matrix, including gray level non-uniformity (GLNU) in horizontal direction, vertical gray level non-uniformity, GLNU in the 45 degree direction, run length non-uniformity in 135 degree direction, GLNU in the 135 degree direction. All these texture parameters were statistically higher in TNBC than in non-TNBC (P <.05). Receiver operating characteristic curve analysis indicated that at a threshold of 268.9068, GLNU in horizontal direction exhibited best diagnostic performance for differentiating TNBC from non-TNBC. Logistic regression model established based on all these parameters showed a sensitivity of 69.3%, specificity of 91.4% and area under the curve of 0.834.US texture features were significantly different between TNBC and non-TNBC, US texture analysis can be used for preliminary differentiation of TNBC from non-TNBC.
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http://dx.doi.org/10.1097/MD.0000000000025878DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183753PMC
June 2021

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Nucleic Acids Res 2021 07;49(W1):W388-W396

Institute of Parasitology, McGill University, Montreal, Quebec, Canada.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.
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http://dx.doi.org/10.1093/nar/gkab382DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265181PMC
July 2021

OmicsAnalyst: a comprehensive web-based platform for visual analytics of multi-omics data.

Nucleic Acids Res 2021 07;49(W1):W476-W482

Institute of Parasitology, McGill University, Montreal, Quebec, Canada.

Data analysis and interpretation remain a critical bottleneck in current multi-omics studies. Here, we introduce OmicsAnalyst, a user-friendly, web-based platform that allows users to perform a wide range of well-established data-driven approaches for multi-omics integration, and visually explore their results in a clear and meaningful manner. To help navigate complex landscapes of multi-omics analysis, these approaches are organized into three visual analytics tracks: (i) the correlation network analysis track, where users choose among univariate and multivariate methods to identify important features and explore their relationships in 2D or 3D networks; (ii) the cluster heatmap analysis track, where users apply several cutting-edge multi-view clustering algorithms and explore their results via interactive heatmaps; and (iii) the dimension reduction analysis track, where users choose among several recent multivariate techniques to reveal global data structures, and explore corresponding scores, loadings and biplots in interactive 3D scatter plots. The three visual analytics tracks are equipped with comprehensive options for parameter customization, view customization and targeted analysis. OmicsAnalyst lowers the access barriers to many well-established methods for multi-omics integration via novel visual analytics. It is freely available at https://www.omicsanalyst.ca.
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http://dx.doi.org/10.1093/nar/gkab394DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262745PMC
July 2021

The symbiotic relationship between Caenorhabditis elegans and members of its microbiome contributes to worm fitness and lifespan extension.

BMC Genomics 2021 May 19;22(1):364. Epub 2021 May 19.

Institute of Parasitology, McGill University, Montreal, Quebec, Canada.

Background: A healthy microbiome influences host physiology through a mutualistic relationship, which can be important for the host to cope with cellular stress by promoting fitness and survival. The mammalian microbiome is highly complex and attributing host phenotypes to a specific member of the microbiome can be difficult. The model organism Caenorhabditis elegans and its native microbiome, discovered recently, can serve as a more tractable, experimental model system to study host-microbiome interactions. In this study, we investigated whether certain members of C. elegans native microbiome would offer a benefit to their host and putative molecular mechanisms using a combination of phenotype screening, omics profiling and functional validation.

Results: A total of 16 members of C. elegans microbiome were screened under chemically-induced toxicity. Worms grown with Chryseobacterium sp. CHNTR56 MYb120 or Comamonas sp. 12022 MYb131, were most resistant to oxidative chemical stress (SiO nanoparticles and juglone), as measured by progeny output. Further investigation showed that Chryseobacterium sp. CHNTR56 positively influenced the worm's lifespan, whereas the combination of both isolates had a synergistic effect. RNAseq analysis of young adult worms, grown with either isolate, revealed the enrichment of cellular detoxification mechanisms (glutathione metabolism, drug metabolism and metabolism of xenobiotics) and signaling pathways (TGF-beta and Wnt signaling pathways). Upregulation of cysteine synthases (cysl genes) in the worms, associated with glutathione metabolism, was also observed. Nanopore sequencing uncovered that the genomes of the two isolates have evolved to favor the specific route of the de novo synthesis pathway of vitamin B6 (cofactor of cysl enzymes) through serC or pdxA2 homologs. Finally, co-culture with vitamin B6 extended worm lifespan.

Conclusions: In summary, our study indicates that certain colonizing members of C. elegans have genomic diversity in vitamin B6 synthesis and promote host fitness and lifespan extension. The regulation of host cellular detoxification genes (i.e. gst) along with cysl genes at the transcriptome level and the bacterium-specific vitamin B6 synthesis mechanism at the genome level are in an agreement with enhanced host glutathione-based cellular detoxification due to this interspecies relationship. C. elegans is therefore a promising alternative model to study host-microbiome interactions in host fitness and lifespan.
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http://dx.doi.org/10.1186/s12864-021-07695-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136213PMC
May 2021

Development of a Comprehensive Toxicity Pathway Model for 17α-Ethinylestradiol in Early Life Stage Fathead Minnows ().

Environ Sci Technol 2021 04 23;55(8):5024-5036. Epub 2021 Mar 23.

Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5B3, Canada.

There is increasing pressure to develop alternative ecotoxicological risk assessment approaches that do not rely on expensive, time-consuming, and ethically questionable live animal testing. This study aimed to develop a comprehensive early life stage toxicity pathway model for the exposure of fish to estrogenic chemicals that is rooted in mechanistic toxicology. Embryo-larval fathead minnows (FHM; ) were exposed to graded concentrations of 17α-ethinylestradiol (water control, 0.01% DMSO, 4, 20, and 100 ng/L) for 32 days. Fish were assessed for transcriptomic and proteomic responses at 4 days post-hatch (dph), and for histological and apical end points at 28 dph. Molecular analyses revealed core responses that were indicative of observed apical outcomes, including biological processes resulting in overproduction of vitellogenin and impairment of visual development. Histological observations indicated accumulation of proteinaceous fluid in liver and kidney tissues, energy depletion, and delayed or suppressed gonad development. Additionally, fish in the 100 ng/L treatment group were smaller than controls. Integration of omics data improved the interpretation of perturbations in early life stage FHM, providing evidence of conservation of toxicity pathways across levels of biological organization. Overall, the mechanism-based embryo-larval FHM model showed promise as a replacement for standard adult live animal tests.
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http://dx.doi.org/10.1021/acs.est.0c05942DOI Listing
April 2021

Ultrafast functional profiling of RNA-seq data for nonmodel organisms.

Genome Res 2021 Apr 17;31(4):713-720. Epub 2021 Mar 17.

Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.

Computational time and cost remain a major bottleneck for RNA-seq data analysis of nonmodel organisms without reference genomes. To address this challenge, we have developed Seq2Fun, a novel, all-in-one, ultrafast tool to directly perform functional quantification of RNA-seq reads without transcriptome de novo assembly. The pipeline starts with raw read quality control: sequencing error correction, removing poly(A) tails, and joining overlapped paired-end reads. It then conducts a DNA-to-protein search by translating each read into all possible amino acid fragments and subsequently identifies possible homologous sequences in a well-curated protein database. Finally, the pipeline generates several informative outputs including gene abundance tables, pathway and species hit tables, an HTML report to visualize the results, and an output of clean reads annotated with mapped genes ready for downstream analysis. Seq2Fun does not have any intermediate steps of file writing and loading, making I/O very efficient. Seq2Fun is written in C++ and can run on a personal computer with a limited number of CPUs and memory. It can process >2,000,000 reads/min and is >120 times faster than conventional workflows based on de novo assembly, while maintaining high accuracy in our various test data sets.
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http://dx.doi.org/10.1101/gr.269894.120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015844PMC
April 2021

A Practical Guide to Metabolomics Software Development.

Anal Chem 2021 02 19;93(4):1912-1923. Epub 2021 Jan 19.

Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States.

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.
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http://dx.doi.org/10.1021/acs.analchem.0c03581DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859930PMC
February 2021

Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.

Metabolites 2021 Jan 9;11(1). Epub 2021 Jan 9.

Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.
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http://dx.doi.org/10.3390/metabo11010044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827862PMC
January 2021

Preoperative Hepatic and Regional Arterial Chemotherapy in Patients Who Underwent Curative Colorectal Cancer Resection: A Prospective, Multi-center, Randomized Controlled Trial.

Ann Surg 2021 06;273(6):1066-1075

Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.

Objective: To evaluate the effects of the addition of preoperative hepatic and regional arterial chemotherapy (PHRAC) on prognosis of stage II and III colorectal cancer (CRC) in a multicenter setting.

Summary Of Background Data: Our previous single-center pilot trial suggested that PHRAC in combination with surgical resection could reduce the occurrence of liver metastasis (LM) and improve survival in CRC patients.

Methods: A prospective multi-center randomized controlled trial was conducted from December 2008 to December 2012 at 5 hospitals in China. Eligible patients with clinical stage II or III CRC who underwent curative resection were randomized to receive PHRAC plus adjuvant therapy (PHRAC arm) or adjuvant therapy alone (control arm). The primary endpoint was DFS. Secondary endpoints were cumulative LM rates, overall survival (OS), and safety (NCT00643877).

Results: A total of 688 patients from 5 centers in China were randomly assigned (1:1) to each arm. The five-year DFS rate was 77% in the PHRAC arm and 65% in the control arm (HR = 0.61, 95% CI 0.46-0.81; P = 0.001). The 5-year LM rates were 7% and 16% in the PHRAC and control arms, respectively (HR = 0.37, 95% CI 0.22-0.63; P < 0.001). The 5-year OS rate was 84% in the PHRAC arm and 76% in the control arm (HR = 0.61, 95% CI 0.43-0.86; P = 0.005). There were no significant differences regarding treatment related morbidity or mortality between the two arms.

Conclusions: The addition of PHRAC could improve DFS in patients with stage II and III CRC. It reduced the incidence of LM and improved OS without compromising patient safety.

Trial Registration: ClinicalTrials.gov identifier: NCT00643877.
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http://dx.doi.org/10.1097/SLA.0000000000004558DOI Listing
June 2021

FastBMD: an online tool for rapid benchmark dose-response analysis of transcriptomics data.

Bioinformatics 2021 05;37(7):1035-1036

Department of Natural Resource Sciences, McGill University, Montreal, QC H9X 3V9, Canada.

Motivation: Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that follows this approach is computationally inefficient and must be locally installed.

Results: FastBMD is a web-based tool that implements standardized methods for transcriptomics benchmark dose-response analysis in R. It is >60 times faster than the current leading software, supports transcriptomics data from 13 species, and offers a comprehensive analytical pipeline that goes from processing and normalization of raw gene expression values to interactive exploration of pathway-level benchmark dose results.

Availability And Implementation: FastBMD is freely available at www.fastbmd.ca.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa700DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128449PMC
May 2021

miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology.

Nucleic Acids Res 2020 07;48(W1):W244-W251

Department of Human Genetics, McGill University, Montreal, Quebec, Canada.

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.
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http://dx.doi.org/10.1093/nar/gkaa467DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319552PMC
July 2020

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Metabolites 2020 May 7;10(5). Epub 2020 May 7.

Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, Quebec, H9X 3V9, Canada.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
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http://dx.doi.org/10.3390/metabo10050186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281575PMC
May 2020

High-resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China.

Int J Infect Dis 2020 Jun 6;95:106-112. Epub 2020 Apr 6.

Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou city, Jiangsu province, 215000 P.R. China; Institute of Medical Imaging, Soochow University, Suzhou city, Jiangsu province, 215000 P.R. China. Electronic address:

Background: A pneumonia associated with the coronavirus disease 2019 (COVID-19) recently emerged in China. It was recognized as a global health hazard.

Methods: 234 inpatients with COVID-19 were included. Detailed clinical data, chest HRCT basic performances and certain signs were recorded Ground-glass opacity (GGO), consolidation, fibrosis and air trapping were quantified. Both clinical types and CT stages were evaluated.

Results: Most patients (approximately 90%) were classified as common type and with epidemiologic history. Fever and cough were main symptoms. Chest CT showed abnormal attenuation in bilateral multiple lung lobes, distributed in the lower and/or periphery of the lungs (94.98%), with multiple shapes. GGO and vascular enhancement sign were most frequent seen, followed by interlobular septal thickening and air bronchus sign as well as consolidation, fibrosis and air trapping. There were significant differences in most of CT signs between different stage groups. The SpO2 and OI were decreased in stage IV, and the CT score of consolidation, fibrosis and air trapping was significantly lower in stage I (P<0.05). A weak relevance was between the fibrosis score and the value of PaO2 and SpO2 (P<0.05).

Conclusions: Clinical performances of patients with COVID-19, mostly with epidemiologic history and typical symptoms, were critical valuable in the diagnosis of the COVID-19. While chest HRCT provided the distribution, shape, attenuation and extent of lung lesions, as well as some typical CT signs of COVID-19 pneumonia.
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http://dx.doi.org/10.1016/j.ijid.2020.04.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136866PMC
June 2020

Comprehensive phenotyping and transcriptome profiling to study nanotoxicity in .

PeerJ 2020 27;8:e8684. Epub 2020 Feb 27.

Institute of Parasitology, McGill University, Montreal, Canada.

Engineered nanoparticles are used at an increasing rate in both industry and medicine without fully understanding their impact on health and environment. The nematode is a suitable model to study the toxic effects of nanoparticles as it is amenable to comprehensive phenotyping, such as locomotion, growth, neurotoxicity and reproduction. In this study, we systematically evaluated the effects of silver (Ag) and five metal oxide nanoparticles: SiO, CeO, CuO, AlO and TiO. The results showed that Ag and SiO exposures had the most toxic effects on locomotion velocity, growth and reproduction, whereas CeO, AlO and CuO exposures were mostly neurotoxic. We further performed RNAseq to compare the gene expression profiles underlying Ag and SiOtoxicities. Gene set enrichment analyses revealed that exposures to Ag and SiOconsistently downregulated several biological processes (regulations in locomotion, reproductive process and cell growth) and pathways (neuroactive ligand-receptor interaction, wnt and MAPK signaling, etc.), with opposite effects on genes involved in innate immunity. Our results contribute to mechanistic insights into toxicity of Ag and SiO nanoparticles and demonstrated that as a valuable model for nanotoxicity assessment.
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http://dx.doi.org/10.7717/peerj.8684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7049462PMC
February 2020

EcoToxModules: Custom Gene Sets to Organize and Analyze Toxicogenomics Data from Ecological Species.

Environ Sci Technol 2020 04 10;54(7):4376-4387. Epub 2020 Mar 10.

Faculty of Agricultural and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue H9X 3V9, Canada.

Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here, we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow () to (1) re-cast predefined molecular pathways into a toxicological framework and (2) provide a data-driven, unsupervised grouping of genes impacted by exposure to environmental contaminants. The functional EcoToxModules were identified by re-organizing KEGG pathways into biological processes that are more relevant to ecotoxicology based on the input from expert scientists and regulators. The statistical EcoToxModules were identified using co-expression analysis of publicly available microarray data ( = 303 profiles) measured in livers of fathead minnows after exposure to 38 different conditions. Potential applications of the EcoToxModules were demonstrated with two case studies that represent exposure to a pure chemical and to environmental wastewater samples. In comparisons to differential expression and gene set analysis, we found that EcoToxModule responses were consistent with these traditional results. Additionally, they were easier to visualize and quantitatively compare across different conditions, which facilitated drawing conclusions about the relative toxicity of the exposures within each case study.
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http://dx.doi.org/10.1021/acs.est.9b06607DOI Listing
April 2020

Rapid localization of ureteral calculi in patients with renal colic by "ultrasonic ureteral crossing sign".

Sci Rep 2020 02 5;10(1):1927. Epub 2020 Feb 5.

Wuhan Fourth Hospital, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

In this study, the term "ultrasonic ureteral crossing sign" is defined, and the diagnostic accuracy of this sign in the rapid localization of ureteral calculi is assessed. Between January 2017 and June 2018, 535 patients underwent ultrasound examination for suspected ureteral calculi. The "ultrasonic ureteral crossing sign" was classified as either positive or negative and correlated with the location of ureteral calculi. Of the 451 patients who were ultimately diagnosed with ureteral calculi, 263 patients had a positive sign, of which 258 patients had distal ureteral calculi, and 188 patients had a negative sign, of which 164 patients had proximal ureteral calculi. Eighteen stones were located in the ureter across the iliac vessels. For patients with a positive "ultrasonic ureteral crossing sign", we observed a 91% sensitivity, 97% specificity, 98% PPV, 87% NPV, and AUC of 0.94 for distal ureteral calculi. For patients with a negative "ultrasonic ureteral crossing sign", we observed a 97% sensitivity, 91% specificity, 87% PPV, 98% NPV, and AUC of 0.94 for proximal ureteral calculi. The "ultrasonic ureteral crossing sign" was found to accurately predict the location of ureteral calculi, significantly improve the efficiency of ultrasound examination, and provide a useful basis for follow-up treatment.
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http://dx.doi.org/10.1038/s41598-020-58805-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002372PMC
February 2020

Network-Based Approaches for Multi-omics Integration.

Methods Mol Biol 2020 ;2104:469-487

Institute of Parasitology, McGill University, Montreal, QC, Canada.

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.
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http://dx.doi.org/10.1007/978-1-0716-0239-3_23DOI Listing
January 2021

Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data.

Methods Mol Biol 2020 ;2104:337-360

Institute of Parasitology, McGill University, Montreal, QC, Canada.

MetaboAnalyst ( www.metaboanalyst.ca ) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.
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http://dx.doi.org/10.1007/978-1-0716-0239-3_17DOI Listing
January 2021

Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.

Nat Protoc 2020 03 15;15(3):799-821. Epub 2020 Jan 15.

Institute of Parasitology, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.

MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min.
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http://dx.doi.org/10.1038/s41596-019-0264-1DOI Listing
March 2020

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.

Curr Protoc Bioinformatics 2019 12;68(1):e86

Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada.

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.
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http://dx.doi.org/10.1002/cpbi.86DOI Listing
December 2019

T1000: a reduced gene set prioritized for toxicogenomic studies.

PeerJ 2019 29;7:e7975. Epub 2019 Oct 29.

Institute of Parasitology, McGill University, Montreal, Canada.

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., and , dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.
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http://dx.doi.org/10.7717/peerj.7975DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824333PMC
October 2019

NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis.

Nucleic Acids Res 2019 07;47(W1):W234-W241

Institute of Parasitology, McGill University, Montreal, Quebec, Canada.

The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.
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http://dx.doi.org/10.1093/nar/gkz240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602507PMC
July 2019

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights.

Metabolites 2019 Mar 22;9(3). Epub 2019 Mar 22.

Institute of Parasitology, McGill University, Montreal, QC H3A 0G4, Canada.

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
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http://dx.doi.org/10.3390/metabo9030057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468840PMC
March 2019

Intestinal dysbiosis compromises alveolar macrophage immunity to Mycobacterium tuberculosis.

Mucosal Immunol 2019 05 19;12(3):772-783. Epub 2019 Feb 19.

Meakins-Christie Laboratories, Department of Medicine, Department of Pathology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada.

Current treatments for tuberculosis (TB) are effective in controlling Mycobacterium tuberculosis (Mtb) growth, yet have significant side effects and do not prevent reinfection. Therefore, it is critical to understand why our host defense system is unable to generate permanent immunity to Mtb despite prolonged anti-tuberculosis therapy (ATT). Here, we demonstrate that treatment of mice with the most widely used anti-TB drugs, rifampicin (RIF) or isoniazid (INH) and pyrazinamide (PYZ), significantly altered the composition of the gut microbiota. Unexpectedly, treatment of mice with the pro-Mtb drugs INH and PYZ, but not RIF, prior to Mtb infection resulted in an increased bacterial burden, an effect that was reversible by fecal transplantation from untreated animals. Mechanistically, susceptibility of INH/PYZ-treated mice was associated with impaired metabolism of alveolar macrophages and defective bactericidal activity. Collectively, these data indicate that dysbiosis induced by ATT administered to millions of individuals worldwide may have adverse effects on the anti-Mtb response of alveolar macrophages.
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http://dx.doi.org/10.1038/s41385-019-0147-3DOI Listing
May 2019

EcoToxChip: A next-generation toxicogenomics tool for chemical prioritization and environmental management.

Environ Toxicol Chem 2019 02;38(2):279-288

Toxicology Center, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

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http://dx.doi.org/10.1002/etc.4309DOI Listing
February 2019

Using OmicsNet for Network Integration and 3D Visualization.

Curr Protoc Bioinformatics 2019 03 17;65(1):e69. Epub 2018 Dec 17.

Institute of Parasitology, McGill University, Sainte Anne de Bellevue, Quebec, Canada.

OmicsNet is a novel web-based tool for creating and visualizing complex biological networks in 3D space. By coupling a comprehensive knowledgebase with the powerful WebGL technology, OmicsNet allows researchers to intuitively explore molecular interactions and regulatory relationships among genes, transcription factors, microRNAs, and metabolites. OmicsNet fills an important gap by facilitating multi-omics integration and systems biology. This article contains three basic protocols covering the key features of OmicsNet, including how to create biological networks from a single or multiple list(s) of molecules, how to integrate or enrich different types of networks, and how to navigate the 3D visualization system to obtain biological insights. The OmicsNet web server is freely available at https://www.omicsnet.ca. © 2018 by John Wiley & Sons, Inc.
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http://dx.doi.org/10.1002/cpbi.69DOI Listing
March 2019
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