Proteínas como autómatas finitos

 Hola,

hoy quería comentar un artículo reciente de Bozovic et al,  donde observan el alosterismo de un dominio PDZ al unirse a su péptido ligando (ver más aquí). Lo que me ha llamado más la atención de este trabajo es que combinando medidas experimentales y simulaciones de dinámica molecular, definen poblaciones de conformaciones y cronometran las transiciones, de manera que al final proponen un modelo que parece un autómata finito, como los que se usan en teoría de la computación o para explicar expresiones regulares.

 

Figura 1. Cambios en la conformación del dominio proteico PDZ2.

Izq: unido a su ligando (trans). Dcha: tras liberarse (cis). Las distancias 

entre los residuos 20,71 y 4,55 sirven para hacer un seguimiento de

las conformaciones en experimentos y simulaciones de dinámica 

molecular. Figura tomada de https://www.pnas.org/content/117/42/26031

 

Figura 2. Autómata que describe los 5 estados/conformaciones de PDZ2.

El diámetro de cada estado es proporcional a su frecuencia en las

poblaciones moleculares. Las flechas son las transiciones entre estados en ms.

Figura tomada de https://www.pnas.org/content/117/42/26031

 

Al final, resulta que las herramientas desarrolladas hace décadas en las ciencias de la computación sirven hoy para describir poblaciones de proteínas. O dicho de otro modo, esta proteína computa,

hasta luego,

Bruno

Plant Genomes in a Changing environment 2020

Hi, this year's Plant Genomes in a Changing Environment was virtual with hashtag #PlantGenomes20.  Check the full program here. I post here my notes on the talks I was able to attend, either live or on the recorded videos, in case they're useful for anyone out there.

Giles Oldroyd (Sainbury lab, UK) talked about a pathway conserved in plants forming intracellular endosymbiotic mycorrhizaephytes. The pathway is not found in species forming exclusively extracellular symbioses, such as ectomycorrhizae, and those forming associations with cyanobacteria. This is described at https://www.nature.com/articles/s41477-020-0613-7. Receptors used with potential symbionts are the same used to recognize chitin and peptide-glicans of pathogens. Symbiotic potential decreases when there's enough nutriends at hand.

 

Figure from https://www.biorxiv.org/content/10.1101/804591v1.full

 

N Marmiroli (SITEIA.PARMA, Italy) presented a poster about the https://simbaproject.eu, where they are testing in plots in Italy and Germany the application of Plant Growth-Promoting Microbes to wheat, maize, tomato and potato under abiotic stress conditions.

Trevor Nolan (Duke U, USA) talked about their efforts in harmonizing single cell mRNA sequencing data from 110K cells to construct an atlas for the developing Arabidopsis thaliana root. Read more at https://www.biorxiv.org/content/10.1101/2020.06.29.178863v1

Francesco Licausi (Oxford U, UK) described the strategies of flooding tolerance in plants, which can be used to breed flooding varieties of crops. Flooding is a composite stress, he focus on two components: oxygen sensing and responses to oxidative stress, which are mediated among others by NAC transcription factors such as ANAC017, read more at https://onlinelibrary.wiley.com/doi/full/10.1111/pce.13037 and https://onlinelibrary.wiley.com/doi/abs/10.1111/tpj.14975

 Ana Caño Delgado (CRAG, Spain) delivered a lecture on physiology and molecular biology of Arabidopsis plants and their responses to drought mediated by brassinosteroids receptors in the root tip. Among many experiments, they measure response to drought as function of root branching angles. Read more at https://www.nature.com/articles/s41467-018-06861-3 and https://science.sciencemag.org/content/368/6488/266.abstract

Steve Long (Lancaster U, UK) shared his knowledge on breeding for incresing photosynthesis efficienty. This figure is from a 2010 review https://www.annualreviews.org/doi/10.1146/annurev-arplant-042809-112206, but still quite impressive:

Alexander Borowsky (U California, Riverside, USA) uses their own coexpression network pipeline (Wisecaver 2019) and the Taiji pipeline to analyze their ATACseq data in order to find key transcription factors regulating responses to water logging and water deficit in rice. They plan to do validation work using CRISPR to confirm their results.

Julia Bailey-Serres (U California, Riverside, USA) gave a keynote with two parts. While the second was related to A Borowsky0s talk, on the first part, she and her team looked at the transcriptional regulation of responses to submergence/flooding by comparing 4 different species (rice, Medicago truncatula, domesticated tomato and its dryland-adapted wild relative Solanum pennellii). By combining RNA-seq and ATACseq data and different computational analyses they found 68 Submerged Upregulated oRthologous Families (SURFs) upregulated in both monocots and dicots, with hypoxia-responsive promoter element (HRPE) motifs being abundant in rice but scarce in promoters of SURFs in dryland-adapted S. pennellii. This piece of work is described at https://science.sciencemag.org/content/365/6459/1291

Figure from https://science.sciencemag.org/content/365/6459/1291

They have really nive figure, shown below, where DNA motifs instead of transcription factors are the vertices of the graph. Despite the differences observed in the network across species, 4 motifs seem to be highly conserved:

Figure from https://science.sciencemag.org/content/365/6459/1291

 

Klaas Vandepoele (VIB-Ugent Center for Plant Systems Biology, Belgium) and his team have been doing a conceptually similar analysis, with RNA-seq from A. thaliana, Populus trichocarpa and maize, in order to identify gene triplets with conserved leaf expression pattern which are conserved across monocots and dicots. They validate their results by phenotyping A. thaliana mutants in the glass house.  Their work has been published at https://onlinelibrary.wiley.com/doi/full/10.1111/pbi.13223. He also mentions this paper https://pubmed.ncbi.nlm.nih.gov/31748815 which describes modules of genes involved in cell proliferation in the leaf.

Jeff Habben (Corteva, USA) describes their extensive field trials in low and high-yielding locations, mostly across the US, to check whether different transgenic lines that overexpress maize transcription factor zmm28 (MADS-box) have higher and more stable yields. Their work is described in detail at https://www.pnas.org/content/116/47/23850

Facundo Romani (CONICET, Argentina) explains how liverwort Marchantia polymorpha defends itself against hervibores by accumulating terpenes in oil bodies, a process controlled by HD-ZIP transcription factor MpC1HDZ. This work is described at https://www.sciencedirect.com/science/article/abs/pii/S0960982220307685

Sandra Knapp (Natural History Museum-London, UK) , a plant taxonomist, talks about diversity of Solanaceae (nightshades), starting with potatoes. 

Source: Sandra Knapp

She discusses how we usually think of aridification as something bad but according tho their work in Australia (https://onlinelibrary.wiley.com/doi/full/10.1111/jse.12638) it seems to be certainly a driver of diversification. She suggests we should be looking at plant lineages that have this pattern to prepare for climate change. One possible way would be to learn from resistant weeds in order to create resistant crops (https://www.sciencedirect.com/science/article/abs/pii/S0304423818307994). This is aligned with the work of Dana MacGregor, which also had a talk in this session.

She terminates by acknowledging the collective effort of people studying nightshades around the world (http://solanaceaesource.org) and a warning about the excess of P/N consum by agriculture and the current rates of extinction, that cause an unsustainable loss of diversity.

Michael Purugganan (New York University, USA) talks about the domestication of rice (which tokk 4K years) and molecular studies to understand the geographic spread of primarily japonica rice in Asia. His two main figures are:

 Figure from https://pubmed.ncbi.nlm.nih.gov/28087768/

Source: https://www.nature.com/articles/s41477-020-0659-6

 

Tal Dahan Meir (Weizmann Institute of Science, Israel) spoke about a long term observation seris of a wild Emmer wheat population in Isreal. Unfortunately I could not see the most relevant slides in the saved video, but het talk reminded me of the Eviatar Nevo's talk last year at the Brachypodium conference (see https://www.youtube.com/watch?v=UhNzH39zLbk).

Guy Naamati (Ensembl Plants, EMBL-EBI, UK) presented a poster about the wheat Pan Genome and status at Ensembl Plant. The programmatic access recipes mentioned in the poster can be found at https://github.com/Ensembl/plant_tools/tree/master/demo_scripts

Click on the image to download and see full size

Violines desde varios ficheros en R

 Hola,

la semana pasada necesitaba hacer una gráfica para mostrar lado a lado varias distribuciones de miles de observaciones. Para ello me decanté por las llamadas gráficas violín, que mi colega Pablo Vinuesa ya había usado en esta figura (panel D) hace unos años.

 Los violin plots permiten resumir distribuciones de manera concisa, mostrando la mediana (punto blanco), el rango intercuartil (el segmento negro grueso) y la densidad de puntos como una curva a cada lado. Por esta última característica son superiores a los diagramas de caja (boxplots). 

El siguiente código en R muestra como calculé esta gráfica horizontal usando la librería vioplot de R a partir de múltiples ficheros de texto, de un valor por línea y extensión .tsv, uno por cada serie de datos.

library(vioplot)

# setwd("path") # if required
filedir="./"

# parse input TSV file names
repeat_files = list.files(path=filedir, pattern="\\.tsv")
series_names = gsub("\\.tsv", "", repeat_files)

# actually read files into data frames
repeats = lapply(repeat_files, function(i){
  log10(read.table(i, header=FALSE))
})
names(repeats) <- series_names

# increase left and bottom margins to make room for axis labels
par(mar = c(4, 11, 1, 1)) 

plot("", 
  ylim = c(0.5, length(repeats)+0.5), 

  # low X values truncated in example   

  xlim = c(2, max(unlist(repeats))), 
  yaxt = "n",  
  ylab = "", 
  xlab = "log10 repeat length")
axis(2, labels = series_names, at = c(1:length(repeats)), las=1)

# add violins one by one
lapply(seq_along(repeats), function(x)
  vioplot(repeats[[x]], at = x,  add = T, box = F, horizontal=T)
)

Hasta pronto,

Bruno