Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)
Reaction Inclusion by Parsimony and Transcript Distribution
Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches have been shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. This method, known as RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.
Please cite when using:
Jenior ML, Moutinho Jr TJ, Dougherty BV, & Papin JA. (2020). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLOS Comp Biol. https://doi.org/10.1371/journal.pcbi.1007099.
>=python-3.6.4 >=cobra-0.15.3 >=pandas-0.24.1 >=symengine-0.4.0 >=scipy-1.3.0
$ pip install riptide
Arguments for core RIPTiDe functions:
riptide.read_transcription_file() - Generates dictionary of transcriptomic abundances from a file
REQUIRED file : string User-provided file name which contains gene IDs and associated transcription values OPTIONAL header : boolean Defines if read abundance file has a header that needs to be ignored Default is no header sep: string Defines what character separates entries on each line Defaults to tab (.tsv) rarefy : bool Rarefies rounded transcript abundances to 90% of the smallest replicate Default is False level : int Level by which to rarefy samples Default is 100000 binning : boolean Perform discrete binning of transcript abundances into quantiles OPTIONAL, not advised Default is False quant_max : float Largest quantile to consider Default is 0.9 quant_min : float Smallest quantile to consider Default is 0.5 step : float Step size for parsing quantiles Default is 0.125 norm : bool Normalize transcript abundances using RPM calculation Performed by default factor : numeric Denominator for read normalization calculation Default is 1e6 (RPM)
riptide.contextualize() - Create context-specific model based on transcript distribution with maximum fit of flux distribution to input transctiptome
REQUIRED model : cobra.Model The model to be contextualized transcriptome : dictionary Dictionary of transcript abundances, output of read_transcription_file() OPTIONAL cpus : int CPUs number for parallelization Default is all available frac_min : float Lower bound for range of minimal fractions to test Default is 0.25 frac_max : float Upper bound for range of minimal fractions to test Default is 0.85 samples : int Number of flux samples to collect Default is 500 silent : bool Silences std out Default is False minimum : float Minimum linear coefficient allowed during weight calculation for pFBA Default is None conservative : bool Conservatively remove inactive reactions based on GPR rules (all member reactions must be inactive to prune) Default is False objective : bool Sets previous objective function as a constraint with minimum flux equal to user input fraction Default is True additive : bool Pool transcription abundances for reactions with multiple contributing gene products Default is False important : list List of gene or reaction ID strings for which the highest weights are assigned regardless of transcription Default is False direct : bool Assigns both minimization and maximization step coefficents directly, instead of relying on abundance distribution Default is False set_bounds : bool Uses flux variability analysis results from constrained model to set new bounds for all reactions Default is True tasks : list List of gene or reaction ID strings for forced inclusion in final model (metabolic tasks or essential genes) exclude : list List of reaction ID strings for forced exclusion from final model gpr : bool Determines if GPR rules will be considered during coefficient assignment Default is False threshold : float Minimum flux a reaction must acheive in order to avoid pruning during flux sum minimization step Default is 1e-6 defined : False or list User defined range of linear coeffients, needs to be defined in a list like [1, 0.5, 0.1, 0.01, 0.001] Works best paired with binned abundance catagories from riptide.read_transcription_file() Default is False open_exchanges : bool Sets all exchange reactions bounds to (-1000., 1000.) Default is False skip_fva : bool Skips final flux variability analysis Default is False
riptide.single_contextualize() - Create context-specific model based on transcript distribution with user-defined objective flux minimum
REQUIRED model : cobra.Model The model to be contextualized OPTIONAL transcriptome : dictionary Dictionary of transcript abundances, output of read_transcription_file() With default, an artifical transcriptome is generated where all abundances equal 1.0 fraction : float Minimum objective fraction used during single run setting Default is 0.8 * Other arguments from iterative implementation are carried over (excluding frac_min and frac_max)
riptide.save_output() - Writes RIPTiDe results to files in a new directory
REQUIRED riptide_obj : RIPTiDe object Class object creared by riptide.contextualize() OPTIONAL path : str New directory to write output files file_type : str Type of output file for RIPTiDe model Accepts either sbml or json Default is JSON
Comments before starting:
- Make sure that genes in the transcriptome file matches those that are in your model.
- Check the example files for proper data formatting
- Binning genes into discrete thresholds for coefficient assignment is available in riptide.read_transcription_file() (not recommended)
- Opening the majority of exchange reactions (bounds = +/- 1000) may yeild better prediction when extracellular conditions are unknown
- The resulting RIPTiDe object has multiple properties including the context-specific model and flux analyses, accessing each is described below
import riptide my_model = cobra.io.read_sbml_model('tests/genre.sbml') transcript_abundances_1 = riptide.read_transcription_file('tests/transcriptome1.tsv') transcript_abundances_2 = riptide.read_transcription_file('tests/transcriptome2.tsv') # has replicates riptide_object_1_a = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1) riptide_object_1_b = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1, tasks=['rxn1'], exclude=['rxn2','rxn3']) riptide.save_output(riptide_obj=riptide_object_1_a, path='~/Desktop/example_riptide_output')
Example riptide.contextualize() stdout report:
Initializing model and integrating transcriptomic data... Pruning zero flux subnetworks... Analyzing context-specific flux distributions... Running max fit RIPTiDe for objective fraction range: 0.65 to 0.85 Progress: 100% Testing local fractions to 0.3... Progress: 100% Context-specific metabolism fit with 0.35 of optimal objective flux Reactions pruned to 285 from 1129 (74.76% change) Metabolites pruned to 285 from 1132 (74.82% change) Flux through the objective DECREASED to ~54.71 from ~65.43 (16.38% change) Context-specific metabolism correlates with transcriptome (r=0.149, p=0.011 *) Max fit RIPTiDe completed in, 4 minutes and 33 seconds
In the final step, RIPTiDe assesses the fit of transcriptomic data for the calculated network activity through correlation of transcript abundance and median flux value for each corresponding reaction. The Spearman correlation coefficient and associated p-value are the reported following the fraction of network topology that is pruned during the flux minimization step.
Max fit RIPTiDe tests all minimum objective flux fractions over the provided range and returns only the model with the best Spearman correlation between context-specific flux for reactions and the associated transcriptomic values. Note, terminating search if a subsequent iteration has a lower correlation coefficient than the last could result from a local maxima and must be considered if an exhaustive analysis is preferred.
Resulting RIPTiDe object (class) properties:
The resulting object is a container for the following data structures.
- model - Contextualized genome-scale metabolic network reconstruction
- transcriptome - Transcriptomic replicate abundances provided by user
- percent_of_mapping - Percent of genes in mapping file found in input GENRE
- minimization_coefficients - Linear coefficients used during flux sum minimization (based on transcriptome replicates)
- maximization_coefficients - Linear coefficients for each reaction based used during flux sampling
- pruned - Dictionary containing the IDs of genes, reactions, and metabolites pruned by RIPTiDe
- flux_samples - Flux samples from constrained model
- flux_variability - Flux variability analysis from constrained model
- fraction_of_optimum - Minimum specified percentage of optimal objective flux during contextualization
- metabolic_tasks - User defined reactions whose activity is saved from pruning
- concordance - Spearman correlation results between linear coefficients and median fluxes from sampling
- gpr_integration - Whether GPR rules were considered during assignment of linear coefficients
- defined_coefficients - Range of linear coefficients RIPTiDe is allowed to utilize provided as a list
- included_important - Reactions or Genes included in the final model which the user defined as important
- additional_parameters - Dictionary of additional parameters RIPTiDe uses
- fraction_bounds - Minimum and maximum values for the range of objective flux minimum fractions tested
- maxfit_iters - Objective flux and fit to transcriptome for each minimum flux fraction tested
Examples of accessing components of RIPTiDe output:
context_specific_GENRE = riptide_object.model context_specific_FVA = riptide_object.flux_variability context_specific_flux_samples = riptide_object.flux_samples
Thank you for your interest in RIPTiDe!
If you encounter any problems, please file an issue along with a detailed description.
Distributed under the terms of the MIT license, "riptide" is free and open source software
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