Microbial metabolic reconstruction at large scale

December 1st, 2020: I was invited to give a 2-hour class for the "Metabolic Pathways Engineering in Bacteria course" part of the Graduate Program In Biochemical Science UNAM Mexico.

Transcript

1. Infiriendo el metabolismo del universo microbiano en ambientes extremos Valerie De Anda Research Associate The University of Texas Marine Science Institute @val_deanda
2. Contenido 01 Inferencia de metabolismo Diversidad Microbiana 02 03 04 Genomas a partir de metagenomas Origen del los eucariontes
3. 01 Diversidad Microbiana Selecciona una de las siguientes opciones Existen más_________________ A) Átomos en el cuerpo humano B) Estrellas en el universo observable C) Microorganismos en el planeta Tierra
4. 1027 1 000 000 000 000 000 000 000 000 000 Átomos Tomada de Enrico Ramirez Ruiz . Your body was forged in the spectacular death of stars. TED talk 01 Diversidad Microbiana
5. 1021 1 000 000 000 000 000 000 000 Estrellas 01 Diversidad Microbiana
6. 1030 1 000 000 000 000 000 000 000 000 000 000 Microorganismos 01 Diversidad Microbiana
7. 01 Diversidad Microbiana Normal matter 15% Galaxies, planets, stars, dust, gas, us 85% Dark matter Direct detection: Underground LZ experiment Indirect detection. Fermi Gamma-Ray Space Telescope Particle coliders: ATLAS experiment Culturable taxa 15% 99% Microbial dark matter Direct detection: Omics: Metagenomics, Metaproteomics, Metatranscriptomics Next generation Physiology Next generation Bioprospecting 1% Pharmaceutical, Industrial, Medicine, Model organisms
8. 01 Diversidad Microbiana 2005 2008 2015 2016 2020 1 2 3 4 Origin of metagenomis Human microbiome Ocean microbiome New tree of life First Asgard culture Archaea tree of life Next Generation Physiology Jan Feb May
9. 01 Diversidad Microbiana Hug et al. 2016 Nature Micro 112 Phyla Cultured representatives Bacteria 36 27 Archaea 6 32% 22% Microbial “species” 1012
10. 01 Diversidad Microbiana Hug et al. 2016 Nature Micro 106 Eukaryotic species 9 million
11. 01 Diversidad Microbiana Solden et al., 2016 Curr. Opin. Microbiol. Bacteria Archaea
12. 01 Diversidad Microbiana Baker, De Anda et al., 2020 Nat. Microbiol
13. Contenido 01 Inferencia de metabolismo Diversidad Microbiana 02 03 04 Genomas a partir de metagenomas Origen de los eucariontes
14. 02 Genomas a partir de metagenomas Una célula Comunidades enteras Genómica Transcriptomica Proteomica Metagenómica Metatranscriptomica Metaproteomica Dick G 2019
15. 02 Genomas a partir de metagenomas Una célula Comunidades enteras Genómica Transcriptomica Proteomica Metagenómica Metatranscriptomica Metaproteomica Dick G 2019
16. 01 Diversidad Microbiana Kan et al., 2015
17. Contenido 01 Inferencia de metabolismo Diversidad Microbiana 02 03 04 Genomas a partir de metagenomas Origen de los eucariontes
18. 03 Inferencia de metabolismo ¿Por qué estudiar el metabolismo microbiano?
19. 03 Inferencia de metabolismo ¿Por qué estudiar el metabolismo microbiano? Primeros habitantes en el planeta Oxigenaron la atmósfera primitiva Dieron lugar a organismos multicelulares Han modificado (y continuan) la biogeoquímica del planeta Son la base fundamental de cualquier ecosistema Son los primeros en responder ante perturbaciones ambientales Cruciales para entender cambio climático global
20. Culturable taxa 99% Microbial dark matter Direct detection: Omics: Metagenomics, Metaproteomics, Metatranscriptomics Next generation Physiology Next generation Bioprospecting 1% 03 Inferencia de metabolismo ¿Cómo analizar lo que no ha sido descrito?
21. 03 Inferencia de metabolismo Obtención de la muestra Secuenciación Procesamiento de datos Interpretacion biologica 45K 3K 30K Retos importantes a considerar
22. De Anda et al., 2017 GigaScience 03 Inferencia de metabolismo Microbial ecology-derived ‘omic’ studies Most abundant features Marker genes Omic data Differentially features Metagenomic’s iceberg illusion
23. 03 Inferencia de metabolismo Computationally efficient (high performance, accuracy, high speed, data processing, reproducibility) What do we need to improve efficiency of data processing? Biological data interpretation (evaluate, compare and analyze complex data in a large scale)
24. Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery 1 MEBS
25. To reduce the solution space and/or to focus the analysis on biological meaningful regions (specific metabolic machineries) Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery Prior Knowledge 1 2 MEBS
26. What's the available data that can be use to characterize large-scale metabolic machineries? To reduce the solution space and/or to focus the analysis on biological meaningful regions (specific metabolic machineries) Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery Prior Knowledge 1 2 3 Available data MEBS
27. What's the available data that can be use to characterize large-scale metabolic machineries? To reduce the solution space and/or to focus the analysis on biological meaningful regions (specific metabolic machineries) Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery Prior Knowledge 1 2 3 Available data 4 Mathematical model MEBS
28. What's the available data that can be use to characterize large-scale metabolic machineries? To reduce the solution space and/or to focus the analysis on biological meaningful regions (specific metabolic machineries) Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery Prior Knowledge 1 2 3 Available data 4 Mathematical model MEBS Single Informative Value
29. What's the available data that can be use to characterize large-scale metabolic machineries? To reduce the solution space and/or to focus the analysis on biological meaningful regions (specific metabolic machineries) Does is it really work? Data integration 03 Inferencia de metabolismo For a given system, multiple sources (and possible types) of data are available and we want to study them integratively to improve knowledge discovery Prior Knowledge 1 2 3 Available data 4 Mathematical model MEBS Single Informative Value Improves efficiency of data processing?
30. Does is it really work? Genomes Metagenomes MAGs 03 Inferencia de metabolismo Assembly Protein coding genes MEBS Entropy-based Scores Visualization Tree of life Metabolic clustering Time series Geographical potential
31. Does is it really work? Genomes Metagenomes MAGs 03 Inferencia de metabolismo Assembly Protein coding genes MEBS Entropy-based Scores
32. MEBS 03 Inferencia de metabolismo Sulfur cycle potential novel organisms Sulfur cycle geographical potential De Anda et al., 2017 GigaScience C,O,N,S,Fe potential across 2 year period of time De Anda et al., 2018 Front. Microbiol 2k Deltaproteobacteria metabolism inference Langwig et al., Metabolic discovery of novel branches of the tree of life Carlton et al., De Anda et al., Appler et al.,
33. MEBS 03 Inferencia de metabolismo Metabolic virome from hydrothermal vents Castelán-Sánchez et al., 2019 Structure and function of communities hypersaline environment Anaerobic phenanthrene degrading bacteria characterization Kraiselburd et al., 2019 Environ Micro Marine bone degrading microbiome Borcher et al., 2020 Preprint Castelán-Sánchez et al., 2019
34. 03 Inferencia de metabolismo Environment Enrichment Culture Metagenome Sequencing MAGs Metagenome Controlled conditions C and Energy Source Microbial interactions Environmental variables No prior knowledge C and Energy Source Only at the time of sampling Environmental variables Enrichment Sequencing
35. 03 Inferencia de metabolismo Biosynthesis and energy production are required for all living things. Energy source Phototrophs Chemotrophs Chemical Light
36. 03 Inferencia de metabolismo Biosynthesis and energy production are required for all living things. Energy source Phototrophs Chemotrophs Chemoheterotrophs Chemoautotrophs Photoheterotrophs Photoautotrophs Organic compounds CO2 Chemical Light Carbon source Organic compounds CO2 Carbon source
37. 03 Inferencia de metabolismo Biosynthesis and energy production are required for all living things. Energy source Phototrophs Chemotrophs Chemoheterotrophs Chemoautotrophs Photoheterotrophs Photoautotrophs Organic compounds CO2 Chemical Light Carbon source Organic compounds CO2 Carbon source O2 Not O2 Organic Inorganic Final e- acceptor Yes NO H20 to reduce CO2
38. Baker, De Anda et al., 2020 Nat. Microbiol ? 03 Inferencia de metabolismo Describiendo un nuevo phylum para la ciencia.
39. Hot springs Deep sea sediments Hua et al. , 2018 Nat Comm Dombrowski et al , 2018 Nat Comm De Anda et al. in review Nature Comm. 03 Inferencia de metabolismo
40. 37 conserved single copy protein-coding gene tree De Anda et al. in review Nature Comm. ? 03 Inferencia de metabolismo
41. 16S rRNA phylogenetic tree De Anda et al. in review Nature Comm. 03 Inferencia de metabolismo
42. De Anda et al. in review Nature Comm. 03 Inferencia de metabolismo
43. 37 conserved single copy protein-coding gene tree De Anda et al. in review Nature Comm. 03 Inferencia de metabolismo
44. Methanol Methylamine Dimethylamine (DMA) Trimethylamine (TMA) CH 3 NH 2 CH 3 NH 2 CH 3 CH 3 CH 3 OH C R Dimethylsulfide (DMS) CH 3 S CH 3 CH 3 NH 2 CH 3 Important components of the global C, N, S Are derived from different sources such as phytoplankton, plants, and the decay of organic matter Abundant in the oceans, atmosphere and sediments Methylated compounds 03 Inferencia de metabolismo De Anda et al. in review Nature Comm.
45. 03 Inferencia de metabolismo Previously undescribed mechanism for anaerobic methylotropy No methylotrophic members of the archaea domain have been described outside methanogenic groups De Anda et al. in review Nature Comm.
46. 03 Inferencia de metabolismo De Anda et al. in review Nature Comm.
47. 03 Inferencia de metabolismo De Anda et al. in review Nature Comm.
48. 03 Inferencia de metabolismo
49. 03 Inferencia de metabolismo De Anda et al. in review Nature Comm.
50. peika95 Peika10 Peika Baker Marine Microbial Ecology Lab bakermicrolab @archaeal Brenda Ortiz, ilustradora
51. 03 Inferencia de metabolismo
52. 03 Inferencia de metabolismo
53. 03 Inferencia de metabolismo
54. ~ 4 TB datos crudos: 4 sitios 16 muestras (~300 Gb por muestra) ~ 3000 MAGs + 551 MAGs Dowmbroski et al., 2018 Woodcroft et al Nature 2018 Emerson et al., Nat Micro 2018 Tsagaraki et al., 2918 Degradación de materia orgánica Permafrost en Suecia 214 muestras 1529 MAGs Permafrost: Cambio climático 2540 MAGs 47 Nuevos linajes a nivel de Phylum Análisis detallado de la interacción de los ciclos biogeoquímicos a nivel de genomas De Anda et al., In Progres Anantharaman, K. et al., 2016 Nature commun Acuíferos Sitios contaminados 03 Inferencia de metabolismo
55. Papel ecológico 12 phyla bacterianos Metabolismo Deltaproteobacteria Interacciones Virus-Procarionte s CP9 Asgard Análisis integral degradación de carbono Gong et al. Langwig et al. Rambo et al. Carlton, Vazquez et al Appler et al. De Anda et al. 03 Inferencia de metabolismo
56. Zipacnabacteria http://www.mithrakrishnan.com/zipacn Carlton et al., In Prep PoPol Vuh : Zipacná era en la mitología maya hijo de Vucub Caquix -Siete Guacamayo- y Chimalmat-. El que mueve montanas 03 Inferencia de metabolismo
57. Langwig M, et al. In progres 03 Inferencia de metabolismo Deltaproteobacteria Protein-level comparison of nearly 2,000 Deltaproteobacteria reveals new ecological and biogeochemical roles Langwig M, De Anda et al., ISME submitted
58. 03 Inferencia de metabolismo Protein-level comparison of nearly 2,000 Deltaproteobacteria reveals new ecological and biogeochemical roles Langwig M, De Anda et al., ISME submitted
59. Langwig M, et al. In progres 03 Inferencia de metabolismo Deltaproteobacteria Protein-level comparison of nearly 2,000 Deltaproteobacteria reveals new ecological and biogeochemical roles
60. Contenido 01 Inferencia de metabolismo Diversidad Microbiana 02 03 04 Genomas a partir de metagenomas Origen del los eucariontes
61. Loki’s castle hydrothermal vent field Spang et al. Nature 2015 The first genome of this group was reconstructed from sediments near the Loki’s Castle hydrothermal vent field in the North Atlantic Ocean and was subsequently named Lokiarchaeota Lokiarchaeota Asgard archaea are the descendants of the archaeal host involved in eukaryogenesis 03 Inferencia de metabolismo Asgard Emme et al., 2017 Nature Reviews
62. 03 Inferencia de metabolismo Asgard Spang et al Science, 2017
63. 03 Inferencia de metabolismo Asgard Heimdallarchaeota may grow heterotrophically by fermentation or by anaerobic and aerobic respiration Odinarchaeum may be a thermophilic fermentative heterotroph Lokiarchaeota and Thorarchaeota can probably use organic compounds and hydrogen The Wood–Ljungdahl pathway (WLP) allows the reduction of carbon dioxide to acetyl-coenzyme A (CoA) and can be used to support both autotrophic carbon fixation and, when linked to chemiosmotic processes, energy conservation. Spang et al Nat Microbiol, 2019
64. Depiction of the syntrophic hypothesis previously proposed by Moreira and Lopez-Garcia in 1998 and 2006 which invokes two bacterial and one archaeal partner(s) in the origin of the eukaryotic cell; that is, first, a syntrophic relationship was established between a fermentative deltaproteobacterium and a hydrogen-dependent archaeal methanogen, which was incorporated into the cytoplasm of the bacterium through endosymbiosis. Subsequently, a second endosymbiosis event led to the uptake of a facultative aerobic alphaproteobacterium, which was suggested to have oxidized organic compounds and hydrocarbons produced by the host. Although this model can explain the origin of the nucleus from the archaeal endosymbiont, it currently lacks support from genomic and phylogenomic analyse 03 Inferencia de metabolismo Syntrophic hypothesis Methanogen Delta Alpha Spang et al Nat Microbiol, 2019
65. Depiction of the hydrogen hypothesis originally proposed by Martin and Müller in 1998 which suggests that a symbiosis between a strictly autotrophic hydrogen-dependent methanogenic archaeon and an H2-producing and CO2-producing alphaproteobacterium led to the origin of the eukaryotic cell. 03 Inferencia de metabolismo Hydrogen hypothesis Methanogen Alphaproteobacterium Spang et al Nat Microbiol, 2019
66. 03 Inferencia de metabolismo Updated hydrogen hypothesis Loki Alphaproteobacterium Depiction of the updated hydrogen hypothesis based on the first analysis of the metabolic repertoire of Lokiarchaeum, which suggests that the archaeal host was an autotrophic hydrogen-dependent acetogen rather than a methanogen Spang et al Nat Microbiol, 2019
67. Syntrophy model, referred to as the ‘reverse flow model’, is based on comparative analysis of the metabolic repertoire encoded by the various members of the Asgard archaea. This model suggests that a metabolic syntrophy between anaerobic ancestral Asgard archaea and facultative anaerobic alphaproteobacteria has provided the selective force for the establishment of a stable symbiotic interaction that has subsequently led to the origin of the eukaryotic cell. 03 Inferencia de metabolismo Reverse flow model Spang et al Nat Microbiol, 2019
68. 03 Inferencia de metabolismo First Asgard ever been culture Candidatus Prometheoarchaeum syntrophicum MK-D1 Imachi et al Nature 2020 Asgard are real Metabolic predictions are accurate
69. T 03 Inferencia de metabolismo López García & Moreira Cell 2020 The Entangle-Engulf-Endogenize (E3) model
70. 03 Inferencia de metabolismo López García & Moreira Cell 2020 Big questions remain unsolved Membrane transition 01 Eukaryotic nucleus 02
71. Contenido 01 Inferencia de metabolismo Diversidad Microbiana 02 03 04 Genomas a partir de metagenomas Origen del los eucariontes
72. @val_deanda Do you have any questions? valdeanda@utexas.edu +1 361 416 04 52 THANKS CREDITS: This presentation was created using icons by Flaticon and infographics & images by Freepik