DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

MOTIVATION: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. RESULTS: In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. AVAILABILITY AND IMPLEMENTATION: DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis.

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy.

Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome-wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers each haplotype independently. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

A large-scale validation of NOCIt's a posteriori probability of the number of contributors and its integration into forensic interpretation pipelines.

Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution's and defense's hypotheses have been developed. In the current paper, we validate a new version of the NOCIt inference platform that determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise and allelic drop-out while taking into account allele frequencies in a reference population. We established the algorithm's performance by conducting tests on samples that were representative of types often encountered in practice. In total, we tested NOCIt's performance on 815 degraded, UV-damaged, inhibited, differentially degraded, or uncompromised DNA mixture samples containing up to 5 contributors. We found that the model makes accurate, repeatable and reliable inferences about the NOCs and significantly outperformed methods that rely on signal filtering. By leveraging recent theoretical results of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establish the NOC can be treated as a nuisance variable, we demonstrated that when NOCIt's APP is used in conjunction with a downstream likelihood ratio (LR) inference system that employs the same probabilistic model, a full evaluation across multiple contributor numbers is rendered. This work, therefore, illustrates the power of modern probabilistic systems to report holistic and interpretable weights-of-evidence to the trier-of-fact without assigning a specified number of contributors or filtering signal.

Statistical modeling of STR capillary electrophoresis signal.

BACKGROUND: In order to isolate an individual's genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited. The signal can be further compounded by the presence of noise and PCR artifacts such as stutter which can mask or mimic biological alleles. Because manual interpretation methods cannot comprehensively account for such nuances, it would be valuable to develop a signal model that can effectively characterize the various components of STR signal independent of a priori knowledge of the quantity or quality of DNA. RESULTS: First, we seek to mathematically characterize the quality of the profile by measuring changes in the signal with respect to amplicon size. Next, we examine the noise, allele, and stutter components of the signal and develop distinct models for each. Using cross-validation and model selection, we identify a model that can be effectively utilized for downstream interpretation. Finally, we show an implementation of the model in NOCIt, a software system that calculates the a posteriori probability distribution on the number of contributors. CONCLUSION: The model was selected using a large, diverse set of DNA samples obtained from 144 different laboratory conditions; with DNA amounts ranging from a single copy of DNA to hundreds of copies, and the quality of the profiles ranging from pristine to highly degraded. Implemented in NOCIt, the model enables a probabilisitc approach to estimating the number of contributors to complex, environmental samples.

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution.

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs.

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family-genome graphs) for the analysis of pathogenicity traits in prokaryotes.

Metabolic connectivity as a driver of host and endosymbiont integration.

The origin of oxygenic photosynthesis in the Archaeplastida common ancestor was foundational for the evolution of multicellular life. It is very likely that the primary endosymbiosis that explains plastid origin relied initially on the establishment of a metabolic connection between the host cell and captured cyanobacterium. We posit that these connections were derived primarily from existing host-derived components. To test this idea, we used phylogenomic and network analysis to infer the phylogenetic origin and evolutionary history of 37 validated plastid innermost membrane (permeome) metabolite transporters from the model plant Arabidopsis thaliana. Our results show that 57% of these transporter genes are of eukaryotic origin and that the captured cyanobacterium made a relatively minor (albeit important) contribution to the process. We also tested the hypothesis that the bacterium-derived hexose-phosphate transporter UhpC might have been the primordial sugar transporter in the Archaeplastida ancestor. Bioinformatic and protein localization studies demonstrate that this protein in the extremophilic red algae Galdieria sulphuraria and Cyanidioschyzon merolae are plastid targeted. Given this protein is also localized in plastids in the glaucophyte alga Cyanophora paradoxa, we suggest it played a crucial role in early plastid endosymbiosis by connecting the endosymbiont and host carbon storage networks. In summary, our work significantly advances understanding of plastid integration and favors a host-centric view of endosymbiosis. Under this view, nuclear genes of either eukaryotic or bacterial (noncyanobacterial) origin provided key elements of the toolkit needed for establishing metabolic connections in the primordial Archaeplastida lineage.

Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms.

BACKGROUND: High-throughput sequencing technologies are lifting major limitations to molecular-based ecological studies of eukaryotic microbial diversity, but analyses of the resulting millions of short sequences remain a major bottleneck for these approaches. Here, we introduce the analytical and statistical framework of sequence similarity networks, increasingly used in evolutionary studies and graph theory, into the field of ecology to analyze novel pyrosequenced V4 small subunit rDNA (SSU-rDNA) sequence data sets in the context of previous studies, including SSU-rDNA Sanger sequence data from cultured ciliates and from previous environmental diversity inventories. RESULTS: Our broadly applicable protocol quantified the progress in the description of genetic diversity of ciliates by environmental SSU-rDNA surveys, detected a fundamental historical bias in the tendency to recover already known groups in these surveys, and revealed substantial amounts of hidden microbial diversity. Moreover, network measures demonstrated that ciliates are not globally dispersed, but are structured by habitat and geographical location at intermediate geographical scale, as observed for bacteria, plants, and animals. CONCLUSIONS: Currently available 'universal' primers used for local in-depth sequencing surveys provide little hope to exhaust the significantly higher ciliate (and most likely microbial) diversity than previously thought. Network analyses such as presented in this study offer a promising way to guide the design of novel primers and to further explore this vast and structured microbial diversity.

Deciphering primordial cyanobacterial genome functions from protein network analysis.

The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1-3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6]. Relatively little is known, however, about the evolutionary history and gene content of primordial cyanobacteria [7, 8]. To address these issues, we used protein similarity networks [9], containing proteomes from 48 cyanobacteria as the test group, and reference proteomes from 84 microbes representing four distinct metabolic groups from most reducing to most oxidizing: methanogens, obligate anaerobes (nonmethanogenic), facultative aerobes, and obligate aerobes. These four metabolic groups represent extant bioinformatic proxies for ancient redox chemistries, extending from an anoxic origin through the GOE and ultimately to obligate aerobes [10-13]. Analysis of the network metric degree showed a strong relationship between cyanobacteria and obligate anaerobes, from which cyanobacteria presumably arose, for core functions that include translation, photosynthesis, energy conservation, and environmental interactions. These data were used to reconstruct primordial functions in cyanobacteria that included nine gene families involved in photosynthesis, hydrogenases, and proteins involved in defense from environmental stress. The presence of 60% of these genes in both reaction center I (RC-I) and RC-II-type bacteria may be explained by selective loss of either RC in the evolutionary history of some photosynthetic lineages. Finally, the network reveals that cyanobacteria occupy a unique position among prokaryotes as a hub between anaerobes and obligate aerobes.

Exploring biotic interactions within protist cell populations using network methods.

The study of diseased human cells and of cells isolated from the natural environment will likely be revolutionized by single cell genomics (SCG). Here, we used protein similarity networks to explore within- and between-cell DNA differences from SCG data derived from six individual rhizarian cells related to Paulinella ovalis and proteins from the complete genome of another rhizarian, Bigelowiella natans. We identified shared and distinct DNA components within our SCG data and between P. ovalis and B. natans. We show that network properties such as assortativity and degree effectively discriminate genome features between SCG assemblies and that SCG data follow the power law with a small number of protein families dominating networks.

A multi-level parcellation approach for brain functional connectivity analysis.

Most brain functional connectivity methods in fMRI require a brain parcellation into functionally homogeneous regions. In this work we propose a novel parcellation approach based on a spatial hierarchical clustering, that provides clusters within a multi-level framework. The method has the advantage of producing several brain parcellations rather than a single one from a fixed size-homogeneity criterion. Results obtained on real data demonstrate the relevance of the approach. Finally, a connectivity study shows the benefit of a prior multi-level parcellation of the brain.