REVISTA MINERÍA 552 | EDICIÓN SEPTIEMBRE 2023

MINERÍA la mejor puerta de acceso al sector minero MINERÍA / SEPTIEMBRE 2023 / EDICIÓN 552 110 El análisis de abundancia es uno de los principales métodos utilizados para determinar diferencias en cuanto a la composición microbiana entre condiciones o grupos de muestras e identificar los taxones microbianos asociados con ciertos factores ambientales, biológicos y/o clínicos, a este tipo de taxones altamente diferenciadores, se les denomina bioindicadores. Se aplicó la librería DESeq2 siendo el objetivo detectar posibles filotipos candidatos a ser bioindicadores de las condiciones AC, ANC, SC, SNC, a través de comparar categorías dos a dos para determinar qué géneros se encontraban diferencialmente abundantes en la condición de interés respecto a la condición de estudio, aplicando un modelo binomial negativo ajustado a datos de secuenciación masiva de amplicones, con una estimación de la medida de efecto mediante la media geométrica, aplicando un modelo local con un conteo por poscount, con corrección por comparaciones múltiples mediante FDR (p<0.05) y fijando para el LFC [logaritmo del Fold-Change (FC) como medida de efecto] en cero (Tabla 3; Figuras 9, 10 y 11) (Lin & Peddada, 2020). Por su parte, en la Figura 12 se refleja la abundancia relativa media (%) para cada categoría (AC/ANC/SC/SNC) de los géneros que se obtuvieron como significativos en los dos anteriores análisis estadísticos para las zonas contaminadas, así como ciertos géneros asociados a ambientes mineros de interés. En conclusión, mediante los análisis de abundancia se han podido identificar determinados géneros asociados Tabla 3. Resultados del Análisis de Bioindicadores Mediante DESeq2 Table 3. Results of Bioindicator Analysis Using DESeq2 mine] and Sulfobacillus (0.01%) [acidophilic isolated from sulfide-bearing gold concentrates]. Abundance analysis is one of the main methods used to determine differences in microbial composition between conditions or groups of samples and to identify microbial taxa associated with certain environmental, biological and/or clinical factors; such highly differentiating taxa are called bioindicators. The DESeq2 library was applied to detect possible candidate phylotypes as bioindicators of the AC, ANC, SC, SNC conditions, by comparing categories two by two to determine which genera were found to be differentially abundant in the condition of interest with respect to the study condition, applying a negative binomial model fitted to massive amplicon sequencing data, with an estimation of the effect measure using the geometric mean, applying a local model with a poscount, with correction for multiple comparisons using FDR (p<0.05) and setting the LFC (logarithm of the Fold-Change [FC] as effect measure) to zero (Table 3; Figures 9, 10 and 11) (Lin & Peddada, 2020). Figure 12 shows the mean relative abundance (%) for each category (AC/ANC/SC/SNC) of the genera that were found to be significant in the two previous statistical analyses for the contaminated areas, as well as certain genera associated with mining environments of interest. In conclusion, through abundance analysis it has been possible to identify certain genera significantly associated with contaminated areas, which could be proposed as candidates as bioindicators of these environmental conditions and which will have to be evaluated with greater precision with a greater number of samples, as well as collecting samples from unsampled areas that present similar characteristics to the samples from Anama Pit and "natural" areas away from the radius of action of the mining activity in the area. Comments and implications of these results for a more effective environmental management in the mining system The results shown, although they correspond to the analysis of a few samples from limited environments of the Anama Pit, show the power of metagenomics to describe the ecology and biodiversity of the microenvironments of a mining system. The implications of these findings are multiple, since they allow (Yuan et al., 2021) to identify bioindicators to describe undesirable ecosystemic effects in the mining system, to glimpse how this knowledge can be accompanied by the design of new remediation processes for environments affected by mining, to better follow up on mine closure processes or to complement the environmental monitoring currently being carried out.

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