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Abstract Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

Abstract Background Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features. Results We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC). Conclusion Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

In our study, we harnessed an original Enhanced Speed Structured Illumination Microscopy (Fast-SIM) imaging setup to explore the dynamics of mitochondrial and inner membrane ultrastructure under specific photo-oxidation stress induced by Chlorin-e6 and light irradiation. Notably, our Fast-SIM system allowed us to observe and quantify a distinct remodeling and shortening of the mitochondrial structure after 60–80 s of irradiation. These changes were accompanied by fusion events of adjacent inner membrane cristae and global swelling of the organelle. Preceding these alterations, a larger sequence was characterized by heightened dynamics within the mitochondrial network, featuring events such as mitochondrial fission, rapid formation of tubular prolongations, and fluctuations in cristae structure. Our findings provide compelling evidence that, among enhanced-resolution microscopy techniques, Fast-SIM emerges as the most suitable approach for non-invasive dynamic studies of mitochondrial structure in living cells. For the first time, this approach allows quantitative and qualitative characterization of successive steps in the photo-induced oxidation process with sufficient spatial and temporal resolution.

Sulfurous thermal waters (STWs) are used as a complementary treatment for allergic rhinitis. However, there is scant data on the effects of STW on nasal epithelial cells, and in vitro models are warranted. The main aim of this study was to evaluate the dose and time effects of exposure to 3D nasal inserts (MucilAirTM-HF allergic rhinitis model) with STW or isotonic sodium chloride solution (ISCS) aerosols. Transepithelial electrical resistance (TEER) and histology were assessed before and after nebulizations. Chemokine/cytokine levels in the basal supernatants were assessed by enzyme-linked immunosorbent assay. The results showed that more than four daily nebulizations of four or more minutes compromised the normal epithelial integrity. In contrast, 1 or 2 min of STW or ISCS nebulizations had no toxic effect up to 3 days. No statistically significant changes in release of inflammatory chemokines MCP-1/CCL2 > IL-8/CXCL8 > MIP-1α/CCL3, no meaningful release of “alarmins” (IL-1α, IL-33), nor of anti-inflammatory IL-10 cytokine were observed. We have characterized safe time and dose conditions for aerosol nebulizations using a novel in vitro 3D nasal epithelium model of allergic rhinitis patients. This may be a suitable in vitro setup to mimic in vivo treatments of chronic rhinitis with STW upon triggering an inflammatory stimulus in the future.

Summary: Vitamin K is a micronutrient necessary for γ-carboxylation of glutamic acids. This post-translational modification occurs in the endoplasmic reticulum (ER) and affects secreted proteins. Recent clinical studies implicate vitamin K in the pathophysiology of diabetes, but the underlying molecular mechanism remains unknown. Here, we show that mouse β cells lacking γ-carboxylation fail to adapt their insulin secretion in the context of age-related insulin resistance or diet-induced β cell stress. In human islets, γ-carboxylase expression positively correlates with improved insulin secretion in response to glucose. We identify endoplasmic reticulum Gla protein (ERGP) as a γ-carboxylated ER-resident Ca2+-binding protein expressed in β cells. Mechanistically, γ-carboxylation of ERGP protects cells against Ca2+ overfilling by diminishing STIM1 and Orai1 interaction and restraining store-operated Ca2+ entry. These results reveal a critical role of vitamin K-dependent carboxylation in regulation of Ca2+ flux in β cells and in their capacity to adapt to metabolic stress.

Purpose: Multi-omics offer worthwhile and increasingly accessible technologies to diagnostic laboratories seeking potential second-tier strategies to help patients with unresolved rare diseases, especially patients clinically diagnosed with a rare OMIM (Online Mendelian Inheritance in Man) disease. However, no consensus exists regarding the optimal diagnostic care pathway to adopt after negative results with standard approaches.Methods: In 15 unsolved individuals clinically diagnosed with recognizable OMIM diseases but with negative or inconclusive first-line genetic results, we explored the utility of a multi-step approach using several novel omics technologies to establish a molecular diagnosis. Inclusion criteria included a clinical autosomal recessive disease diagnosis and single heterozygous pathogenic variant in the gene of interest identified by first-line analysis (60%–9/15) or a clinical diagnosis of an X-linked recessive or autosomal dominant disease with no causative variant identified (40%–6/15). We performed a multi-step analysis involving short-read genome sequencing (srGS) and complementary approaches such as mRNA sequencing (mRNA-seq), long-read genome sequencing (lrG), or optical genome mapping (oGM) selected according to the outcome of the GS analysis.Results: SrGS alone or in combination with additional genomic and/or transcriptomic technologies allowed us to resolve 87% of individuals by identifying single nucleotide variants/indels missed by first-line targeted tests, identifying variants affecting transcription, or structural variants sometimes requiring lrGS or oGM for their characterization.Conclusion: Hypothesis-driven implementation of combined omics technologies is particularly effective in identifying molecular etiologies. In this study, we detail our experience of the implementation of genomics and transcriptomics technologies in a pilot cohort of previously investigated patients with a typical clinical diagnosis without molecular etiology.

The PI3K-related kinase (PIKK) SMG1 monitors the progression of metazoan nonsense-mediated mRNA decay (NMD) by phosphorylating the RNA helicase UPF1. Previous work has shown that the activity of SMG1 is impaired by small molecule inhibitors, is reduced by the SMG1 interactors SMG8 and SMG9, and is downregulated by the so-called SMG1 insertion domain. However, the molecular basis for this complex regulatory network has remained elusive. Here, we present cryo-electron microscopy reconstructions of human SMG1-9 and SMG1-8-9 complexes bound to either a SMG1 inhibitor or a non-hydrolyzable ATP analog at overall resolutions ranging from 2.8 to 3.6 Å. These structures reveal the basis with which a small molecule inhibitor preferentially targets SMG1 over other PIKKs. By comparison with our previously reported substrate-bound structure (Langer et al.,2020), we show that the SMG1 insertion domain can exert an autoinhibitory function by directly blocking the substrate-binding path as well as overall access to the SMG1 kinase active site. Together with biochemical analysis, our data indicate that SMG1 autoinhibition is stabilized by the presence of SMG8. Our results explain the specific inhibition of SMG1 by an ATP-competitive small molecule, provide insights into regulation of its kinase activity within the NMD pathway, and expand the understanding of PIKK regulatory mechanisms in general.

In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops.

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

(1) Background: The development of mitochondrial medicine has been severely impeded by a lack of effective therapies. (2) Methods: To better understand Mitochondrial Encephalopathy Lactic Acidosis Syndrome Stroke-like episodes (MELAS) syndrome, neuronal cybrid cells carrying different mutation loads of the m.3243A > G mitochondrial DNA variant were analysed using a multi-omic approach. (3) Results: Specific metabolomic signatures revealed that the glutamate pathway was significantly increased in MELAS cells with a direct correlation between glutamate concentration and the m.3243A > G heteroplasmy level. Transcriptomic analysis in mutant cells further revealed alterations in specific gene clusters, including those of the glutamate, gamma-aminobutyric acid pathways, and tricarboxylic acid (TCA) cycle. These results were supported by post-mortem brain tissue analysis from a MELAS patient, confirming the glutamate dysregulation. Exposure of MELAS cells to ketone bodies significantly reduced the glutamate level and improved mitochondrial functions, reducing the accumulation of several intermediate metabolites of the TCA cycle and alleviating the NADH-redox imbalance. (4) Conclusions: Thus, a multi-omic integrated approach to MELAS cells revealed glutamate as a promising disease biomarker, while also indicating that a ketogenic diet should be tested in MELAS patients.

Abstract Background The organization of chromatin in the nucleus plays an essential role in gene regulation. About half of the mammalian genome comprises transposable elements. Given their repetitive nature, reads associated with these elements are generally discarded or randomly distributed among elements of the same type in genome-wide analyses. Thus, it is challenging to identify the activities and properties of individual transposons. As a result, we only have a partial understanding of how transposons contribute to chromatin folding and how they impact gene regulation. Results Using PCR and Capture-based chromosome conformation capture (3C) approaches, collectively called 4Tran, we take advantage of the repetitive nature of transposons to capture interactions from multiple copies of endogenous retrovirus (ERVs) in the human and mouse genomes. With 4Tran-PCR, reads are selectively mapped to unique regions in the genome. This enables the identification of transposable element interaction profiles for individual ERV families and integration events specific to particular genomes. With this approach, we demonstrate that transposons engage in long-range intra-chromosomal interactions guided by the separation of chromosomes into A and B compartments as well as topologically associated domains (TADs). In contrast to 4Tran-PCR, Capture-4Tran can uniquely identify both ends of an interaction that involve retroviral repeat sequences, providing a powerful tool for uncovering the individual transposable element insertions that interact with and potentially regulate target genes. Conclusions 4Tran provides new insight into the manner in which transposons contribute to chromosome architecture and identifies target genes that transposable elements can potentially control.

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

The yeast THO complex is recruited to active genes and interacts with the RNA-dependent ATPase Sub2 to facilitate the formation of mature export-competent messenger ribonucleoprotein particles and to prevent the co-transcriptional formation of RNA:DNA-hybrid-containing structures. How THO-containing complexes function at the mechanistic level is unclear. Here, we elucidated a 3.4 Å resolution structure of Saccharomyces cerevisiae THO-Sub2 by cryo-electron microscopy. THO subunits Tho2 and Hpr1 intertwine to form a platform that is bound by Mft1, Thp2, and Tex1. The resulting complex homodimerizes in an asymmetric fashion, with a Sub2 molecule attached to each protomer. The homodimerization interfaces serve as a fulcrum for a seesaw-like movement concomitant with conformational changes of the Sub2 ATPase. The overall structural architecture and topology suggest the molecular mechanisms of nucleic acid remodeling during mRNA biogenesis.

Chronic intestinal pseudoobstruction (CIPO) is a severe form of intestinal dysmotility, and patients often undergo iterative abdominal surgeries and require parenteral nutrition. Several genes are known to be responsible for this pathology, including ACTG2 (autosomal dominant) and MYH11 (autosomal recessive).We report the first case of unexpected trio medical exome sequencing diagnosis of mucopolysaccharidosis type I (MPS-I) in a patient with an early CIPO. There was no clinical suspicion of MPS-I at the time of the prescription. It allowed biochemical confirmation of MPS-I, expert clinical evaluation and early treatment. Enzyme replacement therapy (ERT) with laronidase was started at 9 months old, and hematopoietic stem cell transplantation was carried out at 10 months and a half. The patient also had a 1.7 mb heterozygous deletion in chromosomal region 16p13.11p12.3, comprising several genes, including MYH11, paternally inherited. Her father has no symptoms of CIPO or other digestive symptoms. One previous association of CIPO and MPS-I was reported in 1986. Moreover, the number of incidental findings of inherited metabolic disorders with therapeutic impact will inevitably increase as pangenomic analyses become cheaper and easily available.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

PI3K-related kinases (PIKKs) are large Serine/Threonine (Ser/Thr)-protein kinases central to the regulation of many fundamental cellular processes. PIKK family member SMG1 orchestrates progression of an RNA quality control pathway, termed nonsense-mediated mRNA decay (NMD), by phosphorylating the NMD factor UPF1. Phosphorylation of UPF1 occurs in its unstructured N- and C-terminal regions at Serine/Threonine-Glutamine (SQ) motifs. How SMG1 and other PIKKs specifically recognize SQ motifs has remained unclear. Here, we present a cryo-electron microscopy (cryo-EM) reconstruction of a human SMG1-8-9 kinase complex bound to a UPF1 phosphorylation site at an overall resolution of 2.9 Å. This structure provides the first snapshot of a human PIKK with a substrate-bound active site. Together with biochemical assays, it rationalizes how SMG1 and perhaps other PIKKs specifically phosphorylate Ser/Thr-containing motifs with a glutamine residue at position +1 and a hydrophobic residue at position -1, thus elucidating the molecular basis for phosphorylation site recognition.

Summary: Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During HIV-1 infection, human dendritic cells (DCs) can detect the virus through an innate sensing pathway, leading to antiviral interferon and DC maturation. Here, we develop an iterative experimental and computational approach to map the HIV-1 innate response circuitry in monocyte-derived DCs (MDDCs). By integrating genome-wide chromatin accessibility with expression kinetics, we infer a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observe that an interferon response is required, yet insufficient, to drive MDDC maturation and identify PRDM1 and RARA as essential regulators of the interferon response and MDDC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the response to infection. : Pathogen sensing leads to host transcriptional reprogramming to protect against infection. However, it is unclear how transcription factor activity is coordinated across the genome. Johnson et al. integrate chromatin accessibility and gene expression data to infer and validate a gene regulatory network that directs the innate immune response to HIV. Keywords: network inference, DNA sensing, innate signaling, cGAS, STING, IRF3, NF-κB, chromatin modification, ATAC-seq, RNA-seq

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Wiedemann–Steiner syndrome (WDSTS) is a Mendelian syndromic intellectual disability (ID) condition associated with hypertrichosis cubiti, short stature, and characteristic facies caused by pathogenic variants in the KMT2A gene. Clinical features can be inconclusive in mild and unusual WDSTS presentations with variable ID (mild to severe), facies (typical or not) and other associated malformations (bone, cerebral, renal, cardiac and ophthalmological anomalies). Interpretation and classification of rare KMT2A variants can be challenging. A genome-wide DNA methylation episignature for KMT2A-related syndrome could allow functional classification of variants and provide insights into the pathophysiology of WDSTS. Therefore, we assessed genome-wide DNA methylation profiles in a cohort of 60 patients with clinical diagnosis for WDSTS or Kabuki and identified a unique highly sensitive and specific DNA methylation episignature as a molecular biomarker of WDSTS. WDSTS episignature enabled classification of variants of uncertain significance in the KMT2A gene as well as confirmation of diagnosis in patients with clinical presentation of WDSTS without known genetic variants. The changes in the methylation profile resulting from KMT2A mutations involve global reduction in methylation in various genes, including homeobox gene promoters. These findings provide novel insights into the molecular etiology of WDSTS and explain the broad phenotypic spectrum of the disease.

(1) Assessing growth and recruitment can be difficult during early life history, especially for rare species such as the pallid sturgeon Scaphirhynchus albus (federally endangered in the United States). One potential tool to address this knowledge gap is the stocking of age-0 individuals. (2) During July 2018, two stocking events of marked age-0 pallid sturgeon (53–56 or 41–42 days old) occurred in the lower Missouri River resulting in the release of 8495 individuals. Over the following three years, pallid sturgeon were sampled with baited trotlines and benthic trawls during routine monitoring. (3) During this period, we captured 77 individuals that recruited to age 1 or older. Only 9% of captured individuals yielded length-at-age values falling below the 95% prediction interval for pallid sturgeon raised entirely in an energetically favorable hatchery environment with abundant food resources. (4) These results suggest that the lower Missouri River is capable of supporting hatchery-level growth for young pallid sturgeon. This study also provided a unique opportunity to document recruitment to age-1 of age-0 pallid sturgeon stocked at relatively small sizes, which highlights the potential benefits of utilizing stocking to address endangered species knowledge gaps.

Mainland France is characterized by low-to-moderate seismic activity, yet it is known that major earthquakes could strike this territory (e.g., Liguria in 1887 or Basel in 1356). Assessing this French seismic hazard is thus necessary in order to support building codes and to lead prevention actions towards the population. The Probabilistic Seismic Hazard Assessment (PSHA) is the classical approach used to estimate the seismic hazard. One way to apply PSHA is to generate synthetic earthquakes by propagating information from past seismicity and building various seismic scenarios. In this paper, we present an implementation of a stochastic generator of earthquakes and discuss its relevance to mimic the seismicity of low-to-moderate seismic areas. The proposed stochastic generator produces independent events (main shocks) and their correlated seismicity (only aftershocks). Main shocks are simulated first in time and magnitude considering all available data in the area, and then localized in space with the use of a probability map and regionalization. Aftershocks are simulated around main shocks by considering both the seismic moment ratio and distribution of the aftershock’s proportion. The generator is tested with mainland France data.

Summary: Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes. : This work demonstrates that extending the biophysical accuracy of the assumed model of transcriptional regulation improves large-scale regulatory network inference. As a proof of concept, Tchourine et al. show that incorporating RNA degradation into the model results in better network recovery while simultaneously predicting accurate RNA degradation rates. Keywords: gene regulatory networks, network inference, RNA degradation rates, RNA stability, transcriptional regulatory networks, biophysical modeling, systems biology, machine learning, saccharomyces cerevisiae, network remodeling

Usher syndrome is an autosomal recessive disorder characterized by congenital hearing loss combined with retinitis pigmentosa, and in some cases, vestibular areflexia. Three clinical subtypes are distinguished, and MYO7A and USH2A represent the two major causal genes involved in Usher type I, the most severe form, and type II, the most frequent form, respectively. Massively parallel sequencing was performed on a cohort of patients in the context of a molecular diagnosis to confirm clinical suspicion of Usher syndrome. We report here 231 pathogenic MYO7A and USH2A genotypes identified in 73 Usher type I and 158 Usher type II patients. Furthermore, we present the ACMG classification of the variants, which comprise all types. Among them, 68 have not been previously reported in the literature, including 12 missense and 16 splice variants. We also report a new deep intronic variant in USH2A. Despite the important number of molecular studies published on these two genes, we show that during the course of routine genetic diagnosis, undescribed variants continue to be identified at a high rate. This is particularly pertinent in the current era, where therapeutic strategies based on DNA or RNA technologies are being developed.

Summary: Low-grade astrocytomas (LGAs) carry neomorphic mutations in isocitrate dehydrogenase (IDH) concurrently with P53 and ATRX loss. To model LGA formation, we introduced R132H IDH1, P53 shRNA, and ATRX shRNA into human neural stem cells (NSCs). These oncogenic hits blocked NSC differentiation, increased invasiveness in vivo, and led to a DNA methylation and transcriptional profile resembling IDH1 mutant human LGAs. The differentiation block was caused by transcriptional silencing of the transcription factor SOX2 secondary to disassociation of its promoter from a putative enhancer. This occurred because of reduced binding of the chromatin organizer CTCF to its DNA motifs and disrupted chromatin looping. Our human model of IDH mutant LGA formation implicates impaired NSC differentiation because of repression of SOX2 as an early driver of gliomagenesis. : In a human neural stem cell model of low-grade astrocytoma, Modrek et al. show that mutant IDH1 and loss of P53 and ATRX together block differentiation via disassociation of SOX2 from putative enhancers. This occurs because of disruption of chromatin looping secondary to hypermethylation at CTCF motifs. Keywords: low-grade glioma, astrocytoma, IDH, P53, ATRX, neural stem cells, SOX2, chromatin looping, CTCF, DNA methylation

The RNA-degrading exosome mediates the processing and decay of many cellular transcripts. In the yeast nucleus, the ubiquitous 10-subunit exosome core complex (Exo-9–Rrp44) functions with four conserved cofactors (Rrp6, Rrp47, Mtr4, and Mpp6). Biochemical and structural studies to date have shed insights into the mechanisms of the exosome core and its nuclear cofactors, with the exception of Mpp6. We report the 3.2-Å resolution crystal structure of a S. cerevisiae Exo-9–Mpp6 complex, revealing how linear motifs in the Mpp6 middle domain bind Rrp40 via evolutionary conserved residues. In particular, Mpp6 binds near a tryptophan residue of Rrp40 that is mutated in human patients suffering from pontocerebellar hypoplasia. Using biochemical assays, we show that Mpp6 is required for the ability of Mtr4 to extend the trajectory of an RNA entering the exosome core, suggesting that it promotes the channeling of substrates from the nuclear helicase to the processive RNase.

Summary: Tryptophan as the precursor of several active compounds, including kynurenine and serotonin, is critical for numerous important metabolic functions. Enhanced tryptophan metabolism toward the kynurenine pathway has been associated with myelodysplastic syndromes (MDSs), which are preleukemic clonal diseases characterized by dysplastic bone marrow and cytopenias. Here, we reveal a fundamental role for tryptophan metabolized along the serotonin pathway in normal erythropoiesis and in the physiopathology of MDS-related anemia. We identify, both in human and murine erythroid progenitors, a functional cell-autonomous serotonergic network with pro-survival and proliferative functions. In vivo studies demonstrate that pharmacological increase of serotonin levels using fluoxetine, a common antidepressant, has the potential to become an important therapeutic strategy in low-risk MDS anemia refractory to erythropoietin. : Sibon et al. identify a cell-autonomous serotonergic network in human and mouse erythroid progenitors. Reduced levels of serotonin lead to decreased proliferation and survival of erythroid progenitors. Increasing serotonin’s concentration through fluoxetine, commonly used to treat depression, could be a valuable therapeutic intervention to correct myelodysplastic-syndrome-related anemia. Keywords: serotonin, Tph1, erythropoiesis, myelodysplastic syndrome, anemia, SSRI

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

The Périgord black truffle, Tuber melanosporum Vittad., is an ectomycorrhizal fungus that forms edible hypogeous ascomata. It is now harvested in plantations and is recognized as an agricultural product by European policy. Empirical techniques without scientific demonstration of their efficiency are often used to improve the production of truffles in plantations. One of these techniques is “truffle trapping” which consists in practicing holes inside the potential productive area and to fill them with a substrate containing ascospores. We report an experiment in a truffle orchard where 784 holes were set under 196 trees. Two years after the installation of the holes, 95% of the truffles were found inside the holes corresponding to only 5% of the productive area. This study confirms the efficiency of this empirical technique and demonstrates new ways for in situ studies of the truffle life cycle.

During class switch recombination (CSR), B cells replace the Igh Cμ or δ exons with another downstream constant region exon (CH), altering the antibody isotype. CSR occurs through the introduction of AID-mediated double-strand breaks (DSBs) in switch regions and subsequent ligation of broken ends. Here, we developed an assay to investigate the dynamics of DSB formation in individual cells. We demonstrate that the upstream switch region Sμ is first targeted during recombination and that the mechanism underlying this control relies on 53BP1. Surprisingly, regulation of break order occurs through residual binding of 53BP1 to chromatin before the introduction of damage and independent of its established role in DNA repair. Using chromosome conformation capture, we show that 53BP1 mediates changes in chromatin architecture that affect break order. Finally, our results explain how changes in Igh architecture in the absence of 53BP1 could promote inversional rearrangements that compromise CSR.

V(D)J recombination relies on the presence of proximal enhancers that activate the antigen receptor (AgR) loci in a lineage- and stage-specific manner. Unexpectedly, we find that both active and inactive AgR enhancers cooperate to disseminate their effects in a localized and long-range manner. Here, we demonstrate the importance of short-range contacts between active enhancers that constitute an Igk super-enhancer in B cells. Deletion of one element reduces the interaction frequency between other enhancers in the hub, which compromises the transcriptional output of each component. Furthermore, we establish that, in T cells, long-range contact and cooperation between the inactive Igk enhancer MiEκ and the active Tcrb enhancer Eβ alters enrichment of CBFβ binding in a manner that impacts Tcrb recombination. These findings underline the complexities of enhancer regulation and point to a role for localized and long-range enhancer-sharing between active and inactive elements in lineage- and stage-specific control.

Abiotic stresses, such as drought, salinity, and extreme temperatures, are major limiting factors in global crop productivity and are predicted to be exacerbated by climate change. The overproduction of reactive oxygen species (ROS) is a common consequence of many abiotic stresses. Ascorbate, also known as vitamin C, is the most abundant water-soluble antioxidant in plant cells and can combat oxidative stress directly as a ROS scavenger, or through the ascorbate−glutathione cycle—a major antioxidant system in plant cells. Engineering crops with enhanced ascorbate concentrations therefore has the potential to promote broad abiotic stress tolerance. Three distinct strategies have been utilized to increase ascorbate concentrations in plants: (i) increased biosynthesis, (ii) enhanced recycling, or (iii) modulating regulatory factors. Here, we review the genetic pathways underlying ascorbate biosynthesis, recycling, and regulation in plants, including a summary of all metabolic engineering strategies utilized to date to increase ascorbate concentrations in model and crop species. We then highlight transgene-free strategies utilizing genome editing tools to increase ascorbate concentrations in crops, such as editing the highly conserved upstream open reading frame that controls translation of the GDP-L-galactose phosphorylase gene.

Wolbachia are maternally inherited endosymbiotic bacteria, widespread among arthropods thanks to host reproductive manipulations that increase their prevalence into host populations. The most commonly observed manipulation is cytoplasmic incompatibility (CI). CI leads to embryonic death in crosses between i) infected males and uninfected females and ii) individuals infected with incompatible Wolbachia strains. CI can be conceptualized as a toxin-antidote system where a toxin deposited by Wolbachia in the sperm would induce embryonic death unless countered by an antidote produced by Wolbachia present in the eggs. In Drosophila melanogaster, transgenic expression of Wolbachia effector cidB revealed its function of CI-inducing toxin. Moreover in Culex pipiens, the diversity of cidB variants present in wPip strains accounts for the diversity in crossing-types. We conducted cytological analyses to determine the CI mechanisms that lead to embryonic death in C. pipiens, and assess whether diversity in crossing-types could be based on variations in these mechanisms. We revealed that paternal chromatin condensation and segregation defects during the first embryonic division are always responsible for embryonic death. The strongest observed defects lead to an exclusion of the paternal chromatin from the first zygotic division, resulting in haploid embryos unable to hatch. The proportion of unhatched haploid embryos, developing with only maternal chromatin, which reflects the frequency of strong defects can be considered as a proxy of CI intensity at the cellular level. We thus studied the putative effect of variations in crossing types and cidB diversification on CI defects intensity. Incompatible crosses involving distinct wPip strains revealed that CI defects intensity depends on the Wolbachia strains hosted by the males and is linked to the diversity of cidB genes harbored in their genomes. These results support that, additionally to its implication in C. pipiens crossing type variability, cidB diversification also influences the strength of CI embryonic defects.

The nuclear RNA exosome complex mediates the processing of structured RNAs and the decay of aberrant non-coding RNAs, an important function particularly in human cells. Most mechanistic studies to date have focused on the yeast system. Here, we reconstituted and studied the properties of a recombinant 14-subunit human nuclear exosome complex. In biochemical assays, the human exosome embeds a longer RNA channel than its yeast counterpart. The 3.8 Å resolution cryo-EM structure of the core complex bound to a single-stranded RNA reveals that the RNA channel path is formed by two distinct features of the hDIS3 exoribonuclease: an open conformation and a domain organization more similar to bacterial RNase II than to yeast Rrp44. The cryo-EM structure of the holo-complex shows how obligate nuclear cofactors position the hMTR4 helicase at the entrance of the core complex, suggesting a striking structural conservation from lower to higher eukaryotes.

The early-life intestinal microbiota plays a key role in shaping host immune system development. We found that a single early-life antibiotic course (1PAT) accelerated type 1 diabetes (T1D) development in male NOD mice. The single course had deep and persistent effects on the intestinal microbiome, leading to altered cecal, hepatic, and serum metabolites. The exposure elicited sex-specific effects on chromatin states in the ileum and liver and perturbed ileal gene expression, altering normal maturational patterns. The global signature changes included specific genes controlling both innate and adaptive immunity. Microbiome analysis revealed four taxa each that potentially protect against or accelerate T1D onset, that were linked in a network model to specific differences in ileal gene expression. This simplified animal model reveals multiple potential pathways to understand pathogenesis by which early-life gut microbiome perturbations alter a global suite of intestinal responses, contributing to the accelerated and enhanced T1D development.

Abstract Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method's value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

IRBIT is a molecule that interacts with the inositol 1,4,5-trisphosphate (IP3)-binding pocket of the IP3 receptor (IP3R), whereas the antiapoptotic protein, Bcl2l10, binds to another part of the IP3-binding domain. Here we show that Bcl2l10 and IRBIT interact and exert an additive inhibition of IP3R in the physiological state. Moreover, we found that these proteins associate in a complex in mitochondria-associated membranes (MAMs) and that their interplay is involved in apoptosis regulation. MAMs are a hotspot for Ca2+ transfer between endoplasmic reticulum (ER) and mitochondria, and massive Ca2+ release through IP3R in mitochondria induces cell death. We found that upon apoptotic stress, IRBIT is dephosphorylated, becoming an inhibitor of Bcl2l10. Moreover, IRBIT promotes ER mitochondria contact. Our results suggest that by inhibiting Bcl2l10 activity and promoting contact between ER and mitochondria, IRBIT facilitates massive Ca2+ transfer to mitochondria and promotes apoptosis. This work then describes IRBIT as a new regulator of cell death.

Seeds are involved in the vertical transmission of microorganisms from one plant generation to another and consequently act as reservoirs for the plant microbiota. However, little is known about the structure of seed-associated microbial assemblages and the regulators of assemblage structure. In this work, we have assessed the response of seed-associated microbial assemblages of Raphanus sativus to invading phytopathogenic agents, the bacterial strain Xanthomonas campestris pv. campestris (Xcc) 8004 and the fungal strain Alternaria brassicicola Abra43. According to the indicators of bacterial (16S rRNA gene and gyrB sequences) and fungal (ITS1) diversity employed in this study, seed transmission of the bacterial strain Xcc 8004 did not change the overall composition of resident microbial assemblages. In contrast seed transmission of Abra43 strongly modified the richness and structure of fungal assemblages without affecting bacterial assemblages. The sensitivity of seed-associated fungal assemblage to Abra43 is mostly related to changes in relative abundance of closely related fungal species that belong to the Alternaria genus. Variation in stability of the seed microbiota in response to Xcc and Abra43 invasions could be explained by differences in seed transmission pathways employed by these micro-organisms, which ultimately results in divergence in spatio-temporal colonization of the seed habitat.

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Negative examples - genes that are known not to carry out a given protein function - are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html).

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures - results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.

Abstract Background Leber's hereditary optic neuropathy (LHON) is caused by mutations in the complex I subunits of the respiratory chain. Although patients have been treated with idebenone since 1992, the efficacy of the drug is still a matter of debate. Methods We evaluated the effect of idebenone in fibroblasts from LHON patients using enzymatic and polarographic measurements. Results Complex I activity was 42% greater in treated fibroblasts compared to controls (p = 0.002). Despite this complex I activity improvement, the effects on mitochondrial respiration were contradictory, leading to impairment in some cases and stimulation in others. Conclusion These results indicate that idebenone is able to compensate the complex I deficiency in LHON patient cells with variable effects on respiration, indicating that the patients might not be equally likely to benefit from the treatment.

Abstract Background The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively co-regulated groups, as opposed to those that are simply co-expressed. Be cause genes may be co-regulated only across a subset of all observed experimental conditions, biclustering (clustering of genes and conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such a priori known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-regulatory motifs. In organisms where these motifs are not known, their de novo detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solutions. Results We have developed an algorithm, cMonkey, that detects putative co-regulated gene groupings by integrating the biclustering of gene expression data and various functional associations with the de novo detection of sequence motifs. Conclusion We have applied this procedure to the archaeon Halobacterium NRC-1, as part of our efforts to decipher its regulatory network. In addition, we used cMonkey on public data for three organisms in the other two domains of life: Helicobacter pylori, Saccharomyces cerevisiae, and Escherichia coli. The biclusters detected by cMonkey both recapitulated known biology and enabled novel predictions (some for Halobacterium were subsequently confirmed in the laboratory). For example, it identified the bacteriorhodopsin regulon, assigned additional genes to this regulon with apparently unrelated function, and detected its known promoter motif. We have performed a thorough comparison of cMonkey results against other clustering methods, and find that cMonkey biclusters are more parsimonious with all available evidence for co-regulation.

Abstract Background Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing. Results In this paper we describe the Gaggle -a simple, open-source Java software environment that helps to solve the problem of software and database integration. Guided by the classic software engineering strategy of separation of concerns and a policy of semantic flexibility, it integrates existing popular programs and web resources into a user-friendly, easily-extended environment. We demonstrate that four simple data types (names, matrices, networks, and associative arrays) are sufficient to bring together diverse databases and software. We highlight some capabilities of the Gaggle with an exploration of Helicobacter pylori pathogenesis genes, in which we identify a putative ricin-like protein -a discovery made possible by simultaneous data exploration using a wide range of publicly available data and a variety of popular bioinformatics software tools. Conclusion We have integrated diverse databases (for example, KEGG, BioCyc, String) and software (Cytoscape, DataMatrixViewer, R statistical environment, and TIGR Microarray Expression Viewer). Through this loose coupling of diverse software and databases the Gaggle enables simultaneous exploration of experimental data (mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations (operon, chromosomal proximity, phylogenetic pattern), metabolic pathways (KEGG) and Pubmed abstracts (STRING web resource), creating an exploratory environment useful to 'web browser and spreadsheet biologists', to statistically savvy computational biologists, and those in between. The Gaggle uses Java RMI and Java Web Start technologies and can be found at http://gaggle.systemsbiology.net.

Abstract Background Compartmentalization is a key feature of eukaryotic cells, but its evolution remains poorly understood. GTPases are the oldest enzymes that use nucleotides as substrates and they participate in a wide range of cellular processes. Therefore, they are ideal tools for comparative genomic studies aimed at understanding how aspects of biological complexity such as cellular compartmentalization evolved. Results We describe the identification and characterization of a unique family of circularly permuted GTPases represented by the human orthologue of yeast Lsg1p. We placed the members of this family in the phylogenetic context of the YlqF Related GTPase (YRG) family, which are present in Eukarya, Bacteria and Archea and include the stem cell regulator Nucleostemin. To extend the computational analysis, we showed that hLsg1 is an essential GTPase predominantly located in the endoplasmic reticulum and, in some cells, in Cajal bodies in the nucleus. Comparison of localization and siRNA datasets suggests that all members of the family are essential GTPases that have increased in number as the compartmentalization of the eukaryotic cell and the ribosome biogenesis pathway have evolved. Conclusion We propose a scenario, consistent with our data, for the evolution of this family: cytoplasmic components were first acquired, followed by nuclear components, and finally the mitochondrial and chloroplast elements were derived from different bacterial species, in parallel with the formation of the nucleolus and the specialization of nuclear components.