Mtfp1 ablation enhances mitochondrial respiration and protects against hepatic steatosis.

Hepatic steatosis is the result of imbalanced nutrient delivery and metabolism in the liver and is the first hallmark of Metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD is the most common chronic liver disease and involves the accumulation of excess lipids in hepatocytes, inflammation, and cancer. Mitochondria play central roles in liver metabolism yet the specific mitochondrial functions causally linked to MASLD remain unclear. Here, we identify Mitochondrial Fission Process 1 protein (MTFP1) as a key regulator of mitochondrial and metabolic activity in the liver. Deletion of Mtfp1 in hepatocytes is physiologically benign in mice yet leads to the upregulation of oxidative phosphorylation (OXPHOS) activity and mitochondrial respiration, independently of mitochondrial biogenesis. Consequently, liver-specific knockout mice are protected against high fat diet-induced steatosis and metabolic dysregulation. Additionally, Mtfp1 deletion inhibits mitochondrial permeability transition pore opening in hepatocytes, conferring protection against apoptotic liver damage in vivo and ex vivo. Our work uncovers additional functions of MTFP1 in the liver, positioning this gene as an unexpected regulator of OXPHOS and a therapeutic candidate for MASLD.

Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae.

Pangenomes provide access to an accurate representation of the genetic diversity of species, both in terms of sequence polymorphisms and structural variants (SVs). Here we generated the Saccharomyces cerevisiae Reference Assembly Panel (ScRAP) comprising reference-quality genomes for 142 strains representing the species' phylogenetic and ecological diversity. The ScRAP includes phased haplotype assemblies for several heterozygous diploid and polyploid isolates. We identified circa (ca.) 4,800 nonredundant SVs that provide a broad view of the genomic diversity, including the dynamics of telomere length and transposable elements. We uncovered frequent cases of complex aneuploidies where large chromosomes underwent large deletions and translocations. We found that SVs can impact gene expression near the breakpoints and substantially contribute to gene repertoire evolution. We also discovered that horizontally acquired regions insert at chromosome ends and can generate new telomeres. Overall, the ScRAP demonstrates the benefit of a pangenome in understanding genome evolution at population scale.

Gallocin A, an Atypical Two-Peptide Bacteriocin with Intramolecular Disulfide Bonds Required for Activity.

Streptococcus gallolyticus subsp. gallolyticus (SGG) is an opportunistic gut pathogen associated with colorectal cancer. We previously showed that colonization of the murine colon by SGG in tumoral conditions was strongly enhanced by the production of gallocin A, a two-peptide bacteriocin. Here, we aimed to characterize the mechanisms of its action and resistance. Using a genetic approach, we demonstrated that gallocin A is composed of two peptides, GllA1 and GllA2, which are inactive alone and act together to kill "target" bacteria. We showed that gallocin A can kill phylogenetically close relatives of the pathogen. Importantly, we demonstrated that gallocin A peptides can insert themselves into membranes and permeabilize lipid bilayer vesicles. Next, we showed that the third gene of the gallocin A operon, gip, is necessary and sufficient to confer immunity to gallocin A. Structural modeling of GllA1 and GllA2 mature peptides suggested that both peptides form alpha-helical hairpins stabilized by intramolecular disulfide bridges. The presence of a disulfide bond in GllA1 and GllA2 was confirmed experimentally. Addition of disulfide-reducing agents abrogated gallocin A activity. Likewise, deletion of a gene encoding a surface protein with a thioredoxin-like domain impaired the ability of gallocin A to kill Enterococcus faecalis. Structural modeling of GIP revealed a hairpin-like structure strongly resembling those of the GllA1 and GllA2 mature peptides, suggesting a mechanism of immunity by competition with GllA1/2. Finally, identification of other class IIb bacteriocins exhibiting a similar alpha-helical hairpin fold stabilized with an intramolecular disulfide bridge suggests the existence of a new subclass of class IIb bacteriocins. IMPORTANCE Streptococcus gallolyticus subsp. gallolyticus (SGG), previously named Streptococcus bovis biotype I, is an opportunistic pathogen responsible for invasive infections (septicemia, endocarditis) in elderly people and is often associated with colon tumors. SGG is one of the first bacteria to be associated with the occurrence of colorectal cancer in humans. Previously, we showed that tumor-associated conditions in the colon provide SGG with an ideal environment to proliferate at the expense of phylogenetically and metabolically closely related commensal bacteria such as enterococci (1). SGG takes advantage of CRC-associated conditions to outcompete and substitute commensal members of the gut microbiota using a specific bacteriocin named gallocin, recently renamed gallocin A following the discovery of gallocin D in a peculiar SGG isolate. Here, we showed that gallocin A is a two-peptide bacteriocin and that both GllA1 and GllA2 peptides are required for antimicrobial activity. Gallocin A was shown to permeabilize bacterial membranes and kill phylogenetically closely related bacteria such as most streptococci, lactococci, and enterococci, probably through membrane pore formation. GllA1 and GllA2 secreted peptides are unusually long (42 and 60 amino acids long) and have very few charged amino acids compared to well-known class IIb bacteriocins. In silico modeling revealed that both GllA1 and GllA2 exhibit a similar hairpin-like conformation stabilized by an intramolecular disulfide bond. We also showed that the GIP immunity peptide forms a hairpin-like structure similar to GllA1/GllA2. Thus, we hypothesize that GIP blocks the formation of the GllA1/GllA2 complex by interacting with GllA1 or GllA2. Gallocin A may constitute the first class IIb bacteriocin which displays disulfide bridges important for its structure and activity and might be the founding member of a subtype of class IIb bacteriocins.

JC Polyomavirus Whole Genome Sequencing at the Single-Molecule Level Reveals Emerging Neurotropic Populations in Progressive Multifocal Leukoencephalopathy.

BACKGROUND: JC polyomavirus (JCV) mostly causes asymptomatic persistent renal infections but may give rise in immunosuppressed patients to neurotropic variants that replicate in the brain, causing progressive multifocal leukoencephalopathy (PML). Rearrangements in the JCV genome regulator noncoding control region (NCCR) and missense mutations in the viral capsid VP1 gene differentiate neurotropic variants from virus excreted in urine. METHODS: To investigate intrahost emergence of JCV neurotropic populations in PML, we deep sequenced JCV whole genome recovered from cerebrospinal fluid (CSF) and urine samples from 32 human immunodeficiency virus (HIV)-infected and non-HIV-infected PML patients at the single-molecule level. RESULTS: JCV strains distributed among 6 of 7 known genotypes. Common patterns of NCCR rearrangements included an initial deletion mostly located in a short 10-nucleotide sequence, followed by duplications/insertions. Multiple NCCR variants present in individual CSF samples shared at least 1 rearrangement, suggesting they stemmed from a unique viral population. NCCR variants independently acquired single or double PML-specific adaptive VP1 mutations. NCCR variants recovered from urine and CSF displayed opposite deletion or duplication patterns in binding sites for transcription factors. CONCLUSIONS: Long-read deep sequencing shed light on emergence of neurotropic JCV populations in PML.

Low CCR5 expression protects HIV-specific CD4+ T cells of elite controllers from viral entry.

HIV elite controllers maintain a population of CD4 + T cells endowed with high avidity for Gag antigens and potent effector functions. How these HIV-specific cells avoid infection and depletion upon encounter with the virus remains incompletely understood. Ex vivo characterization of single Gag-specific CD4 + T cells reveals an advanced Th1 differentiation pattern in controllers, except for the CCR5 marker, which is downregulated compared to specific cells of treated patients. Accordingly, controller specific CD4 + T cells show decreased susceptibility to CCR5-dependent HIV entry. Two controllers carried biallelic mutations impairing CCR5 surface expression, indicating that in rare cases CCR5 downregulation can have a direct genetic cause. Increased expression of ß-chemokine ligands upon high-avidity antigen/TCR interactions contributes to autocrine CCR5 downregulation in controllers without CCR5 mutations. These findings suggest that genetic and functional regulation of the primary HIV coreceptor CCR5 play a key role in promoting natural HIV control.

GDSCTools for mining pharmacogenomic interactions in cancer.

Motivation: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. Results: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. Contact: thomas.cokelaer@pasteur.fr or saezrodriguez.rwth-aachen.de or mg12@sanger.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models.

Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

MOTIVATION: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. RESULTS: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. AVAILABILITY: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online. CONTACT: santiago.videla@irisa.fr.

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

BACKGROUND: Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. RESULTS: Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. CONCLUSIONS: Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.