Las bacterias responsables de la lepra (Mycobacterium leprae) pueden regenerar los tejidos

 

Un grupo de investigadores de la Universidad de Edimburgo ha descubierto que las bacterias responsables de la lepra (Mycobacterium leprae) pueden regenerar los tejidos, al realizar un estudio aplicado en pangolines. En dicha regeneración no se evidenció ninguna alteración o mutación dentro de las células que derive al cáncer, motivo por el cual, el profesor Anura Rambukkana, responsable de la investigación, quedó bastante sorprendido.

 

La mencionada investigación es un avance bastante importante para la ciencia, pues en el caso de manipular correctamente dicho proceso, podríamos estar hablando de tratamientos regenerativos en órganos dañados en los próximos años.

 

In vivo partial reprogramming by bacteria promotes adult liver organ growth without fibrosis and tumorigenesis

DOI:https://doi.org/10.1016/j.xcrm.2022.100820

 Las terapias ideales para la medicina regenerativa o el envejecimiento saludable requieren el crecimiento y rejuvenecimiento de órganos sanos, pero actualmente no se dispone de un enfoque a nivel de órgano. Utilizando Mycobacterium leprae (ML) con capacidad de reprogramación celular parcial natural y su huésped animal armadillo de nueve bandas, presentamos un modelo evolutivamente refinado de crecimiento y regeneración del hígado adulto. En los armadillos infectados, ML reprograma todo el hígado y aumenta significativamente la relación hígado/peso corporal total mediante el aumento de los lóbulos hepáticos sanos, incluyendo la proliferación de hepatocitos y la expansión proporcional de la vasculatura, y los sistemas biliares. Los hígados infectados con ML son microarquitectónicos y funcionalmente normales, sin daños, fibrosis o tumorigénesis. La reprogramación inducida por las bacterias reactiva los genes progenitores/de desarrollo/fetales del hígado y regula al alza los marcadores asociados al crecimiento, el metabolismo y el antienvejecimiento, con un cambio mínimo en los genes de senescencia y tumorales, lo que sugiere el secuestro bacteriano de las vías homeostáticas de regeneración para promover la organogénesis de novo. Esto puede facilitar el desentrañamiento de las vías endógenas que reanudan de forma eficaz y segura el crecimiento de los órganos hepáticos, con amplias implicaciones terapéuticas que incluyen la regeneración y el rejuvenecimiento de los órganos.

 

RNA extraction from armadillo livers

Liver samples previously freshly isolated and stored frozen in RNAlater were thawed prior to use and then homogenized in TRIzol at room temperature. The RNA fraction was collected using chloroform and isopropanol-based extraction. Total RNA was resuspended in distilled, RNase-free water and quantified using a Nanodrop ND-1000 Spectrophotometer.

RNA-sequencing of livers from control and infected armadillos

Triplicate RNA samples isolated from 24-month ML-infected, 30-month ML-infected and two control livers were submitted to Arraystar Inc. (Rockville, USA) services for paired-end RNA-sequencing. 1–2 μg total RNA was used to prepare the sequencing libraries. Library preparation involved oligo (dT) magnetic bead mRNA enrichment, highly strand-specific dUTP method using KAPA Stranded RNA-Seq Library Prep Kit, library size distribution and yield QC with Agilent 2100 Bioanalyzer and by absolute quantification qPCR. To sequence the libraries on the IlluminaNovaSeq 6000 instrument, the barcoded libraries were mixed, denatured to single stranded DNA in NaOH, captured on Illumina flow cell, amplified in situ, and subsequently sequenced for 150 cycles for both ends. Image analysis and base calling were performed using Solexa pipeline v1.8 (Off-Line Base Caller software, v1.8). Sequence quality was examined using the FastQC

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software (v0.11.7).

For sequencing quality control, raw data files in FASTQ format were generated from the Illumina sequencer and the sequencing quality examined by plotting the quality score for each sample. Quality score Q is logarithmically related to the base-calling error probability (P): Q = −10log10(P). Q30 means the incorrect base calling probability to be 0.001 or 99.9% base calling accuracy, and high-quality data was indicated by the percentage of the number of bases with Q ≥ 30 being greater than 80%. The trimmed reads (trimmed 5′, 3′-adaptor bases using cutadapt

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(v1.17)) were aligned to the reference genome (Dasypus novemcinctus, Dasnov3.0, GCA_000208655.2 Ensembl) using Hisat2 software (v2.1.0)⁸². The Ensembl Dasnov3.0, INSDC Assembly GCA_000208655.2 was last updated/patched in May 2016, which is well annotated with 22,711 coding genes. The transcript abundances in FPKM at gene and transcript levels were assembled and computed with StringTie (v1.3.3)^83,84. The differential gene expression was analysed using R package Ballgown (v2.10.0)^85–87. The novel genes and transcripts not present in the reference genome/transcriptome were predicted by StringTie and their protein coding potentials were scored by CPAT (v1.2.4)⁸⁸.

Bioinformatics and differential expression visualization

Sets of total protein coding genes from human (GRCh38.p13) and armadillo (Dasnov3.0) reference genomes were acquired from Ensembl Biomart, and subsequently filtered for genes annotated with gene symbols for cross-comparison. Total armadillo protein coding genes with and without gene symbol annotations were queried against all detected genes from RNAseq from any sample and presented as a waffle diagram. Heatmaps of data were produced from sets of differentially expressed genes using pheatmap in R, Python or shell environment (Python and Shell: in-house scripts, Arraystar). For heatmap generation, the log2 transformed FPKM values of the expressed genes were tested by ANOVA across the samples for significant difference in expression (p <= 0.05) and selected for unsupervised hierarchical clustering using Euclidean distance measure and the ‘average’ agglomeration method. The heatmap was scaled row-wise, with the colour scale representing the Z-scores.

Gene ontology analysis was performed using GSEA (Broad Institute, MA) software with differentially expressed genes sets pre-ranked in order of fold change, with minimum gene set sizes set to 10. REVIGO

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was used to visualize GO terms in bubble diagrams, and chord diagrams of differentially expressed genes related to specific GO annotations were created using GOplot.

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Selection of functional genes of interest used manual literature searches (PubMed) and gene annotations (GeneCards, NCBI). For the liver cell type gene expression pattern analysis, data sets from

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were used, specifically the gene expression signatures associated with the distinct clusters identified in that study. Clusters and their gene lists were pooled together into the groups; “hepatocytes”, “cholangiocytes/biliary/EPCAM+”, “Endothelial/Stellate/MyoFB”, “Kupffer” and “NK, NKT and T cell” where possible. These lists were then cross-referenced with the Armadillo liver differentially expressed genes of 30-month infected liver vs. control livers to provide the differentially expressed infected armadillo liver genes potentially associating with particular liver cell types. The top differentially expressed genes in each of these groups, and selected other functional genes of interest, were used to produce heatmaps (software as described above). Common oncogenes and tumour-suppressors were compiled from an independently determined reference list of genes (https://www.arraystar.com/lncpath-cancer-microarrays/) and compared with detected genes in Armadillo liver RNAseq to determine whether those detected were significantly differentially expressed in 24- and 30-month infected Armadillo livers, presented as heatmaps after hierarchical clustering. Differential expression of genes in chronically infected armadillos was compared with the differential expression of genes from a discrete lineage of scar-orchestrating cells in a murine model of fibrogenesis.

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The common set of differentially genes overexpressed in infected armadillo liver and in scar-orchestrating cells after profibrotic injury was used for GO term analysis using g:profiler(ref) and visualized using REVIGO and with chord diagrams from GOplot(ref). The specific expression of collagen species was examined. Genes differentially-expressed in 30-month infected liver versus uninfected control liver were compared with published gene expression data from putative adult and fetal hepatic progenitors, with gene expression data from a rat partial hepatectomy model,and the common set up genes used for GO term analysis and visualization, as above.

To identify changes in senescence-associated genes in ML-infected armadillo liver, genes significantly up- or down-regulated in infected armadillo livers (versus control) were compared with the genes identified in the CellAge database of cell senescence genes as senescence-inducing or senescence-inhibiting.

Quantification and statistical analysis

For armadillo liver: body weight comparisons, liver enzyme assays and for western blot quantification, labelled positive cell quantification in situ, statistical significance was calculated using two-tailed t-tests, with errors bars denoting mean ± SEM. RNAseq data was processed as described, with significance in log2 transformed FPKM values of the expressed genes tested by ANOVA. Boxplots were generated by geom_boxplot in the ggplot2 package in R environment, displaying the median, first and third quartiles, with whiskers extending to the largest or smallest values no further than 1.5x the interquartile range from the third or first quartile, with outlier points beyond this plotted individually. Boxplot data was checked for assumptions allowing parametric testing, with lobule area analysis applying Kruskal-Wallis one-way ANOVA, nuclear density analysis using ANOVA with post-hoc Tukey’s.

Data and code availability

• RNA-sequencing data generated in this study has been deposited to GEO and the accession code included in the key resources table. Whole slide images of H&E stained sections of liver from nine-banded armadillos chronically systemically infected by Mycobacterium leprae, resistant to systemic infection by Mycobacterium leprae, or uninfected and used in this paper (acquired on a Hamamatsu NanoZoomer in. ndpi format) are available at https://doi.org/10.7488/ds/3147. Detailed description of bespoke data analysis methods and pipelines using code are provided within the published article. All other data reported in this paper will be shared by the lead contact upon request without restriction. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

 

 

https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(22)00379-2?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2666379122003792%3Fshowall%3Dtrue&fbclid=IwAR3IF_8DtZdl5H6ZkDQpwN_mX0XPK7Ze22Y326RnH5kH20H2_vaWdeNuplY 

In vivo partial reprogramming by bacteria promotes adult liver organ growth without fibrosis and tumorigenesis

https://www.sciencedirect.com/science/article/pii/S2666379122003792?fbclid=IwAR3lLhNvvc1fIklTMgIwdW6M_0_ycMNRg2mv6GGb02iZMAqfQIz4LtD9cTQ