Posters - Schedules

Biological systems change over time, conditions, and interactions. The gut microbiota is not an exception; the interactions between bacterial populations are dynamic, and they are constantly exposed to changing environmental conditions. These systems also can keep their inner conditions and global characteristics despite the biotic and abiotic perturbations. However, when a perturbation changes the usual behavior of the microbiota dynamics, the system suffers a transition that can be expressed as a disease. When this happens, it is called dysbiosis. Temporal networks are one of the most valuable tools to explore the changing behavior of the microbiota through time because they allow us to analyze the changes in the interactions between their components by studying the layer’s architecture. We built co-abundance networks using abundance data of the taxa found in human stool samples. Both subjects of this study suffered intoxication, so during the period of sickness, their microbiota showed a different behavior than in the previous period. We found highly interconnected communities of (Short Chain Fatty Acids) SCFA-producing bacteria during the dysbiosis period, as well as the change in the abundances of the present phyla in the microbiota samples. This change in the abundances and interactions, as well as the characteristics of the network’s architecture during the dysbiosis, could help us to understand how the microbiota responds to perturbations and how it can reorganize itself.

In metagenomics, there is a growing need for the use of mathematical and computational tools. An open problem is distinguishing the assembly quality and the correctness of a taxonomic assignation when similar species are present in a sample. For this reason, it is essential to have a set of data that suits the condition of containing similar species and a quality rating system for these data. We applied our results in simulated data to the discrimination between Clavibacter species in public rhizosphere data sets.

First, we created a python-based metagenomic simulator that uses reads cropped from genomic data. Simulations contained data from several Clavibacter species and strains. The real genomes are used to qualify the accuracy of taxonomic classifiers and assemblers. There are several taxonomic classifiers of genomic sequences, where we compared how well they classify at a desired taxonomic level. For the quality of the assemblies we evaluate the pertinence of applying the genomic metric N50 to metagenomic data.

Additionally, to discriminate between close Clavibacter species, we implemented a topological data analysis on simulated and real reads. Finally, an evaluation of the content and diversity of Biosynthetic gene clusters is accounted for the metagenomes. Real data are public datasets from rhizosphere crops such as tomato, potato, chili, and corn.

PyMetaSeem, our easy-to-use data simulator in a conda environment, is available for public use. With this tool, we simulated metagenomes containing mixed data of similar genomes and evaluate the ability of current public metagenomic tools' ability to assign a correct taxonomic and assembly.

In Mexico, the production of agave distillates is one of the most deeply rooted processes involving microorganisms. These distillates result from the agave pine processing of more than 20 different species of agave. The production of these distillates occurs in many regions of the country, in most of which its production is artisanal and its fermentation is "spontaneous" with native microorganisms. The study of fermentation microbiomes helps to better understand the production processes with the potential to improve the quality of the final product. With this in mind, the objective of this work was to explore the diversity, structure and their main determinants of the microbial community throughout the range of distillate production in Mexico using metagenetic techniques. We used specific molecular markers for the identification of fungi and bacteria: the Internal Transcribed Spacer (ITS) region for fungi and 16s rRNA for bacteria. These amplicons were massively sequenced and their analysis revealed a large number of microorganisms and allowed us to explore the microbial diversity that interacts during fermentation for the production of agave distillates.1290 ASVs were found in 16 main bacterial genera and 609 ASVs in 10 main fungal genera. The genus Lactobacillus was the dominant bacteria, followed by Oenococcus, an ethanol producer bacteria. The most abundant fungal genera throughout the process were Pichia and Saccharomyces. A core of 7 bacterial genera were present throughout the different regions (Lactobacilluss, Oenococcus, Weissella, Gluconobacter, Acetobacter and Komagataeibacter, Leuconostoc), while 3 genera were observed in fungi (Saccharomyces, Pichia, Kluyveromyces). The Shannon and Simpson indices indicated that there is greater richness and heterogeneity in the bacterial population. The main drivers of diversity and community structure are geographical region and agave species, followed by fermentation stage. The type and material of the fermentation tanks shows little impact on microbial diversity and composition.

Given a data set we can associate to it a metric space with a given distance, i.e., for each data we obtain a point in our metric space in such a way that we construct a simplicial complex that reflects the properties of our data set. Topological data analysis (TDA) gives us a form to extract these topological and geometrical properties from our data set. Due to the increase of accessible data bases of metagenomes it is natural to think about applying the techniques of TDA to genomic, metagenomics and pangenomics.
Objectives: The first objective of this work is to distinguish the presence of Clavibacter Michiganensis Michiganensis (CMM) in a metagenomics data which includes different subspecies of Clavibacter related between each other. The second objective is to give an alternative description of gene families in metagenomic data of Clavibacter to determine how preserved these families are.
Tools: Simulation of reads from genome groups (CAMISIM). Aligning sequencing reads to different closer genomes groups (bowtie2). Compute deep coverage of reads (Samtools). Get homologues. Anvi'o. Persistence diagrams.