Hydrocarbon contamination cause serious environmental damage and human health problems, therefore, to resolve this problem has been proposed diversely remediation techniques friendly with the environment i.e. bioaugmentation, biostimulation, natural attenuation and phytoremediation. Among options, the phytoremediation causes minimum alterations to soil, low cost, it has proved good eff iciency and gained public acceptation. In the present study, the capacity of Mimosa pigra to restore a contaminated soil with both anthracene and phenanthrene was evaluated, along with the soil physicochemical changes due to both plant and contaminant presence in the soil. A silt-loam contaminated soil with 100 mg kg-1 (dry weight) anthracene and 200 mg kg-1 (dry weight) phenanthrene was used in all three treatments: Contaminated soil with M. pigra (C1), contaminated soil without M. pigra (C2), and not contaminated soil with M. pigra (C0) as control. After 70 days of experimentation both biomass and height of M. pigra was not affected due to soil contaminants. There was no difference among treatments for some physicochemical characteristics e.g. soil pH, electrolytic conductivity, soil-texture, and total nitrogen content throughout the experiment. M. pigra increased the soil water holding capacity after 50 and 70 days of the experiment, however, total organic carbon content was lower after 70 days compared to the control treatment. The higher contaminant removal rate was before 7 days of experimentation for all treatments. Anthracene and phenanthrene content were lower in treatments with the presence of M. pigra: 92% of phenanthrene and 80% of anthracene were removed in C1 while nearly 77% of phenanthrene and 60% of anthracene were removed in C2. This suggest a greater removal of contaminants using M. pigra. Total removal of contaminants was not achieved. However, M. pigra was able to accumulate both compounds, showing the M. pigra capability for bioremediation use in contaminated soils.

We studied three soils of the former lake Texcoco with different electrolytic conductivity (1.9 dS m⁻¹, 17.3 dS m⁻¹, and 33.4 dS m⁻¹) and pH (9.3, 10.4, and 10.3) amended with young maize plants and their neutral detergent fibre (NDF) fraction and aerobically incubated in the laboratory for 14 days while the soil bacterial community structure was monitored by means of 454-pyrosequencing of their 16S rRNA marker gene. We identified specific bacterial groups that showed adaptability to soil salinity, i.e., Prauseria in soil amended with young maize plants and Marinobacter in soil amended with NDF. An increase in soil salinity (17.3 dS m⁻¹, 33.4 dS m⁻¹) showed more bacterial genera enriched than soil with low salinity (1.9 dS m⁻¹). Functional prediction showed that members of Alfa-, Gamma-, and Deltaproteobacteria, which are known to adapt to extreme conditions, such as salinity and low nutrient soil content, were involved in the lignocellulose degradation, e.g., Marinimicrobium and Pseudomonas as cellulose degraders, and Halomonas and Methylobacterium as lignin degraders. This research showed that the taxonomic annotation and their functional prediction both highlighted keystone bacterial groups with the ability to degrade complex C-compounds, such as lignin and (hemi)cellulose, in the extreme saline-alkaline soil of the former Lake of Texcoco.

A modified method for the direct extraction of DNA from alkaline-saline soils with minimum DNA fragmentation and a possible reduction in chimera formation during polymerase chain reaction (PCR) was developed. The commercial extraction kit Power Soil DNA (Mo Bio™ Laboratories, Inc.) was used as a reference technique. The method reported here was based on cell lysis employing ethylenediaminetetraacetic acid (EDTA), sodium dodecyl sulfate (SDS), and cell disruption with mechanical force with FastPrep-24™ equip followed by one cycle of freezing at -40 °C for 60 min and thawing at 65 °C for 20 min. The extraction method was tested for allophonic soils with large concentrations of organic matter, fulvic and humic acids, electrolytic conductivity (EC) ranging between 2.6 dS m-1 and 39.9 dS m-1, and pH between 8.8 and 10.9. The yield of DNA extracted depended on soil type, i.e., DNA extracted from soil varied between 2.35 (Texcoco-2) to 3.66 (Texcoco-1) μg DNA g-1 soil. The proposed method in this study produced enough DNA with yield and quality for PCR amplification of 16S rDNA when bovine serum albumin (BSA) was added to the reaction buffer. The DNA obtained had sufficient quality and yield for later use for 16S sequencing or possible use in other sequencing technologies, e.g. whole metagenome shotgun sequencing.

El antiguo lago de Texcoco (Noreste del valle de México) sirvió como fuente de alimentación y transporte al imperio de Tenochtitlan en los años 1325 a 1521. Posterior a la conquista, los españoles empezaron a desviar el curso del lago por los problemas de inundación que sucedían en la capital del país. El proceso de desvío continuó hasta los años 1970; año en el que se se inició el rescate de las zonas lacustres mediante el “Plan Texcoco”. Algunas zonas del antiguo lago sufrieron cambios negativos en la estructura del suelo debido a los procesos de drenado; la salinización se convirtió en un problema principal. La salinización limita (y define) el crecimiento de la flora y fauna en la zona, así como el microbioma del suelo. Diversas especies vegetales con tolerancia a la salinidad fueron introducidas en esta zona para tratar de restaurar los problemas de salinidad, por ejemplo, Distichlis spicata, Tamarix spp. y Suaeda nigra, entre otras. A pesar de las condiciones extremas de salinidad, diversos microorganismos habitan esta zona y cumplen funciones biogeoquímicas importantes para el medio ambiente, además, poseen potencial biotecnológico para su uso en la remediación de suelos y en la promoción del crecimiento vegetal.

La diversidad de microorganismos en el suelo es elevada y su identificación mediante el uso de técnicas tradicionales de cultivo resulta inadecuada y limitada para un elevado porcentaje de los mismos. En la actualidad se cuentan con tecnologías de secuenciamiento masivo del ADN que ha permitido, junto a otras técnicas y herramientas, incluyendo la bioinfomática, la identificación de microrganismos sin necesidad del uso de medios de cultivos. Sin embargo, la secuenciación masiva ha generado enormes cantidades de información que requiere ser analizada y por ende demanda un esfuerzo computacional considerable. Existen diversos programas bioinformáticos, basados en uno o varios lenguajes de programación, para el análisis molecular in silico de secuencias de ADN, e.g., MOTHUR, QIIME1, DADA2 y QIIME2. De estos, QIIME2, por sus siglas en inglés “Quantitative insights into microbial ecology”, es una herramienta frecuentemente empleada para el analisis datos de secuenciamiento de marcadores moleculares o genes funcionales, e.g., 16S rRNA, 18S rRNA, ITS, COI, entre otros. Dada la importancia de éstas, y de la necesidad de acceso a este conocimiento en lenguaje español, en esta revisión se describe y detalla el flujo de trabajo para el análisis de secuencias del gen 16S rRNA provenientes de muestras ambientales empleando QIIME2.