Achal Dhariwal

Bio: Achal Dhariwal is an academic researcher from University of Oslo. The author has contributed to research in topics: Resistome & Microbiome. The author has an hindex of 4, co-authored 11 publications receiving 706 citations. Previous affiliations of Achal Dhariwal include McGill University.

MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data.

Abstract: The widespread application of next-generation sequencing technologies has revolutionized microbiome research by enabling high-throughput profiling of the genetic contents of microbial communities. How to analyze the resulting large complex datasets remains a key challenge in current microbiome studies. Over the past decade, powerful computational pipelines and robust protocols have been established to enable efficient raw data processing and annotation. The focus has shifted toward downstream statistical analysis and functional interpretation. Here, we introduce MicrobiomeAnalyst, a user-friendly tool that integrates recent progress in statistics and visualization techniques, coupled with novel knowledge bases, to enable comprehensive analysis of common data outputs produced from microbiome studies. MicrobiomeAnalyst contains four modules - the Marker Data Profiling module offers various options for community profiling, comparative analysis and functional prediction based on 16S rRNA marker gene data; the Shotgun Data Profiling module supports exploratory data analysis, functional profiling and metabolic network visualization of shotgun metagenomics or metatranscriptomics data; the Taxon Set Enrichment Analysis module helps interpret taxonomic signatures via enrichment analysis against >300 taxon sets manually curated from literature and public databases; finally, the Projection with Public Data module allows users to visually explore their data with a public reference data for pattern discovery and biological insights. MicrobiomeAnalyst is freely available at http://www.microbiomeanalyst.ca.

DJ-1 (Park7) affects the gut microbiome, metabolites and the development of innate lymphoid cells (ILCs).

Abstract: The proper communication between gut and brain is pivotal for the maintenance of health and, dysregulation of the gut-brain axis can lead to several clinical disorders. In Parkinson's disease (PD) 85% of all patients experienced constipation many years before showing any signs of motor phenotypes. For differential diagnosis and preventive treatment, there is an urgent need for the identification of biomarkers indicating early disease stages long before the disease phenotype manifests. DJ-1 is a chaperone protein involved in the protection against PD and genetic mutations in this protein have been shown to cause familial PD. However, how the deficiency of DJ-1 influences the risk of PD remains incompletely understood. In the present study, we provide evidence that DJ-1 is implicated in shaping the gut microbiome including; their metabolite production, inflammation and innate immune cells (ILCs) development. We revealed that deficiency of DJ-1 leads to a significant increase in two specific genera/species, namely Alistipes and Rikenella. In DJ-1 knock-out (DJ-1-/-) mice the production of fecal calprotectin and MCP-1 inflammatory proteins were elevated. Fecal and serum metabolic profile showed that malonate which influences the immune system was significantly more abundant in DJ-1-/- mice. DJ-1 appeared also to be involved in ILCs development. Further, inflammatory genes related to PD were augmented in the midbrain of DJ-1-/- mice. Our data suggest that metabolites and inflammation produced in the gut could be used as biomarkers for PD detection. Perhaps, these metabolites and inflammatory mediators could be involved in triggering inflammation resulting in PD pathology.

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

Abstract: 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. This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data.

Evaluación de la diversidad taxonómica y funcional de la comunidad microbiana relacionada con el ciclo del nitrógeno en suelos de cultivo de arroz con diferentes manejos del tamo

Abstract: La quema del tamo de arroz a campo abierto es una de las mayores fuentes de contaminacion agricola, es por esto que el retorno de los residuos vegetales al suelo ha sido propuesto como una alternativa de manejo eficiente de los residuos pos-cosecha. Sin embargo, es poco conocido el impacto que tiene sobre los microorganismos edaficos involucrados en la disponibilidad y ciclaje de nutrientes. Por lo anterior, se planteo un experimento en campo para evaluar los cambios generados sobre la comunidad microbiana vinculada al ciclo del nitrogeno por la aplicacion de 4 tratamientos diferentes de manejo del tamo de arroz: (Cob+mo) cobertura del terreno con tamo de arroz inoculado con un consorcio microbiano de degradacion, (Inc+mo) tamo de arroz inoculado con el consorcio microbiano e incorporado, (Quema) quema del tamo y (Cob) cobertura del terreno con tamo de arroz sin inocular. Se realizaron 4 muestreos de suelo de soporte y suelo rizosferico antes y durante el ciclo de cultivo. Se evaluo la diversidad, estructura y composicion de la comunidad bacteriana a traves del analisis del gen 16S rRNA y se determino la actividad de las enzimas nitrogenasa, proteasa y ureasa vinculadas con el ingreso de nitrogeno al sistema edafico. Al final del ciclo de cultivo, los mapas de calor basados en la composicion y abundancia de especies, mostraron que las comunidades microbianas de los tratamientos alternos a la quema son mas similares entre si, indicando que la adicion de materia organica influencia la comunidad edafica microbiana. La actividad de las enzimas proteasa y ureasa se vio afectada por la aplicacion del consorcio de degradacion y la forma de retorno del tamo de arroz al suelo respectivamente. Palabras clave: (Incorporacion, cobertura, tamo de arroz, actividad enzimatica, 16S rRNA).

MicroRNAs and complex diseases: from experimental results to computational models.

Abstract: Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

Culturing the human microbiota and culturomics

Abstract: The gut microbiota has an important role in the maintenance of human health and in disease pathogenesis. This importance was realized through the advent of omics technologies and their application to improve our knowledge of the gut microbial ecosystem. In particular, the use of metagenomics has revealed the diversity of the gut microbiota, but it has also highlighted that the majority of bacteria in the gut remain uncultured. Culturomics was developed to culture and identify unknown bacteria that inhabit the human gut as a part of the rebirth of culture techniques in microbiology. Consisting of multiple culture conditions combined with the rapid identification of bacteria, the culturomic approach has enabled the culture of hundreds of new microorganisms that are associated with humans, providing exciting new perspectives on host-bacteria relationships. In this Review, we discuss why and how culturomics was developed. We describe how culturomics has extended our understanding of bacterial diversity and then explore how culturomics can be applied to the study of the human microbiota and the potential implications for human health.

Role of the microbiome in human development.

Abstract: The host-microbiome supraorganism appears to have coevolved and the unperturbed microbial component of the dyad renders host health sustainable. This coevolution has likely shaped evolving phenotypes in all life forms on this predominantly microbial planet. The microbiota seems to exert effects on the next generation from gestation, via maternal microbiota and immune responses. The microbiota ecosystems develop, restricted to their epithelial niches by the host immune system, concomitantly with the host chronological development, providing early modulation of physiological host development and functions for nutrition, immunity and resistance to pathogens at all ages. Here, we review the role of the microbiome in human development, including evolutionary considerations, and the maternal/fetal relationships, contributions to nutrition and growth. We also discuss what constitutes a healthy microbiota, how antimicrobial modern practices are impacting the human microbiota, the associations between microbiota perturbations, host responses and diseases rocketing in urban societies and potential for future restoration.