Defining the Molecular Signal Pathways and Upstream Regulators in Cutaneous Leishmaniasis with Transcriptomic Data Approach

WOS:000613932200006PubMed: 33590982Leishmaniasis is a disease caused by the genus Leishmania spp., which are intracellular parasites. Depending on parasite species and host immune response, there are three basic clinical forms of the disease: cutaneous, mucocutaneous, and visceral leishmaniasis. Cutaneous leishmaniasis is a chronic disease and characterized by the presence of ulcerated skin lesions. The type of skin pathology seen during disease is determined in part by the infecting Leishmania spp., but also by a combination of inflammatory and antiinflammatory host immune response factors resulting in diverse clinical outcomes. in this study, it was aimed to determine the genes, molecular signaling mechanisms and biological functions of the molecules that play a role in the pathogenesis of the disease and immune response and determine host-parasite interactions in mice that are naturally resistant and susceptible to Leishmania major and Leishmania brazifiensis. For this, transcriptomic series GSE56029 was downloaded from "Gene Expression Omnibus" (GEO) data base, including expression profiling of twenty-four tissue samples that were recovered from both naive mice and mice (BALB/c, C57BL/6) infected with L.major and L.braziliensis. Then, "Differentially Expressed Genes" (DEGs) were identified by limma package in R script. FDR q 2 as threshold values were accepted in the analysis. Subsequently, functional and pathway enrichment analyses were performed for the DEGs by "Ingenuity Pathway Analysis" (IPA). For each of DEGs, p 1 were used and analyzed with the software program IPA 8.0. Ingenuity Pathway Analysis revealed the most enrichment pathways to be the inflammation, dendritic cell maturation and "Triggering Receptor Expressed on Myeloid Cells 1" (TREM-1) signal mechanisms and that the DEGs related to the regulation of immune system process were closely associated with the progress of cutaneous leishmaniasis. The upstream regulator analysis predicted that TNF-alpha, IFN gamma, IL-1 beta, IL-10RA and "Signal Transducer and Activator of Transcription-1" (STAT-1) are the regulators that explained gene expression changes causing biological activities in the tissues. Chemical compounds that may have anti-leishmanial effects were also identified in the study. in this study, the mechanisms belonging to the parasite species and host that determine the resistance/susceptibility phenotype were attempted to elucidate. Assessment of gene expression patterns, cytokine/chemokines, and signaling pathways in BALB/c and C57BL/6 mice infected with L.major and L.braziliensis will provide a better understanding of the potential mechanisms underlying infection from a genetic perspective. These results may guide for the future studies in terms of developing potential biomarkers for the diagnosis and prognosis prediction of cutaneous leishmaniasis and providing information about new treatment targets

MAIRA- real-time taxonomic and functional analysis of long reads on a laptop

Background: Advances in mobile sequencing devices and laptop performance make metagenomic sequencing and analysis in the field a technologically feasible prospect. However, metagenomic analysis pipelines are usually designed to run on servers and in the cloud. Results: MAIRA is a new standalone program for interactive taxonomic and functional analysis of long read metagenomic sequencing data on a laptop, without requiring external resources. The program performs fast, online, genus-level analysis, and on-demand, detailed taxonomic and functional analysis. It uses two levels of frame-shift-aware alignment of DNA reads against protein reference sequences, and then performs detailed analysis using a protein synteny graph. Conclusions: We envision this software being used by researchers in the field, when access to servers or cloud facilities is difficult, or by individuals that do not routinely access such facilities, such as medical researchers, crop scientists, or teachers

Using AnnoTree to get more assignments, faster, in DIAMOND+ MEGAN microbiome analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines

Microbial phylogenetic context using phylogenetic outlines

Microbial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the Genome Taxonomy Database taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general than a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality

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