Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis.

BACKGROUND & AIMS: Acute severe ulcerative colitis (ASUC) is a life-threatening condition managed with intravenous steroids followed by infliximab, cyclosporine, or colectomy (for patients with steroid resistance). There are no biomarkers to identify patients most likely to respond to therapy; ineffective medical treatment can delay colectomy and increase morbidity and mortality. We aimed to identify biomarkers of response to medical therapy for patients with ASUC. METHODS: We performed a retrospective analysis of 47 patients with ASUC, well characterized for their responses to steroids, cyclosporine, or infliximab, therapy at 2 centers in France. Fixed colonic biopsies, collected before or within the first 3 days of treatment, were used for microarray analysis of microRNA expression profiles. Deep neural network-based classifiers were used to derive candidate biomarkers for discriminating responders from non-responders to each treatment and to predict which patients would require colectomy. Levels of identified microRNAs were then measured by quantitative PCR analysis in a validation cohort of 29 independent patients-the effectiveness of the classification algorithm was tested on this cohort. RESULTS: A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (area under the curve, 0.91). We identified 3 algorithms, based on microRNA levels, that identified responders to infliximab vs non-responders (84% accuracy, AUC = 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC = 0.79). CONCLUSION: We developed an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues.

Mathematical deconvolution uncovers the genetic regulatory signal of cancer cellular heterogeneity on resistance to paclitaxel.

Drug resistance remains a major problem in combating malignancies, resulting critical the resistance to paclitaxel used in the treatment of many different cancers. Elucidating the cellular heterogeneity composition of tumours may be relevant to designing more effective treatment strategies on drug resistance. In particular, such heterogeneity correlates with the measurement of gene expression below the population level. However, experimental assays capturing differential response are limited and cannot discern the variation in gene expression specific to different cellular types in tumour populations. These limitations led us to consider a mathematical modelling approach, in which the gene expression of cellular subpopulations is recovered by deconvolution. Mathematically, the deconvolution is a multi-linear regression-based problem. We combined herein data on cellular subpopulation frequency composition with gene expression values from 16 tumour lines (8 resistant and 8 sensitive to paclitaxel treatment) to find genes that are differentially expressed between paclitaxel resistant and paclitaxel sensitive tumour lines in different cellular subpopulations. The results indicate that many genes differentially expressed between paclitaxel resistant and sensitive cancer lines are only detected when considering their heterogeneous cellular composition. Overall, our methodology is thought to keep in mind phenotypic heterogeneity improving our resolution in the identification of biomarkers on resistance to chemo-therapeutic agents.

FUN-L: gene prioritization for RNAi screens.

MOTIVATION: Most biological processes remain only partially characterized with many components still to be identified. Given that a whole genome can usually not be tested in a functional assay, identifying the genes most likely to be of interest is of critical importance to avoid wasting resources. RESULTS: Given a set of known functionally related genes and using a state-of-the-art approach to data integration and mining, our Functional Lists (FUN-L) method provides a ranked list of candidate genes for testing. Validation of predictions from FUN-L with independent RNAi screens confirms that FUN-L-produced lists are enriched in genes with the expected phenotypes. In this article, we describe a website front end to FUN-L. AVAILABILITY AND IMPLEMENTATION: The website is freely available to use at http://funl.org

Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction.

In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposed method, named topological autoencoder with best linear combination for optimal reduction of embeddings (Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better results compared to other models. We validate the effectiveness of Taelcore in reducing the prediction error rate on four datasets. Furthermore, we demonstrate that Taelcore's topological improvements have a positive effect on the majority of the machine learning algorithms used. By providing a new way to diagnose patients and detect complications early, this work contributes to improved clinical outcomes in lung transplantation.

Plasma proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology.

Efforts to understand the molecular mechanisms of COVID-19 have led to the identification of ACE2 as the main receptor for the SARS-CoV-2 spike protein on cell surfaces. However, there are still important questions about the role of other proteins in disease progression. To address these questions, we modelled the plasma proteome of 384 COVID-19 patients using protein level measurements taken at three different times and incorporating comprehensive clinical evaluation data collected 28 d after hospitalisation. Our analysis can accurately assess the severity of the illness using a metric based on WHO scores. By using topological vectorisation, we identified proteins that vary most in expression based on disease severity, and then utilised these findings to construct a graph convolutional network. This dynamic model allows us to learn the molecular interactions between these proteins, providing a tool to determine the severity of a COVID-19 infection at an early stage and identify potential pharmacological treatments by studying the dynamic interactions between the most relevant proteins.

Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation.

The vascular endothelium is a hot spot in the response to radiation therapy for both tumors and normal tissues. To improve patient outcomes, interpretable systemic hypotheses are needed to help radiobiologists and radiation oncologists propose endothelial targets that could protect normal tissues from the adverse effects of radiation therapy and/or enhance its antitumor potential. To this end, we captured the kinetics of multi-omics layers-i.e. miRNome, targeted transcriptome, proteome, and metabolome-in irradiated primary human endothelial cells cultured in vitro. We then designed a strategy of deep learning as in convolutional graph networks that facilitates unsupervised high-level feature extraction of important omics data to learn how ionizing radiation-induced endothelial dysfunction may evolve over time. Last, we present experimental data showing that some of the features identified using our approach are involved in the alteration of angiogenesis by ionizing radiation.

Personalized risk predictor for acute cellular rejection in lung transplant using soluble CD31.

We evaluated the contribution of artificial intelligence in predicting the risk of acute cellular rejection (ACR) using early plasma levels of soluble CD31 (sCD31) in combination with recipient haematosis, which was measured by the ratio of arterial oxygen partial pressure to fractional oxygen inspired (PaO2/FiO2) and respiratory SOFA (Sequential Organ Failure Assessment) within 3 days of lung transplantation (LTx). CD31 is expressed on endothelial cells, leukocytes and platelets and acts as a "peace-maker" at the blood/vessel interface. Upon nonspecific activation, CD31 can be cleaved, released, and detected in the plasma (sCD31). The study included 40 lung transplant recipients, seven (17.5%) of whom experienced ACR. We modelled the plasma levels of sCD31 as a nonlinear dependent variable of the PaO2/FiO2 and respiratory SOFA over time using multivariate and multimodal models. A deep convolutional network classified the time series models of each individual associated with the risk of ACR to each individual in the cohort.

Deciphering the Dynamic Molecular Program of Radiation-Induced Endothelial Senescence.

PURPOSE: Radiation-induced cellular senescence is a double-edged sword, acting as both a tumor suppression process limiting tumor proliferation, and a crucial process contributing to normal tissue injury. Endothelial cells play a role in normal tissue injury after radiation therapy. Recently, a study observed an accumulation of senescent endothelial cells (ECs) around radiation-induced lung focal lesions following stereotactic radiation injury in mice. However, the effect of radiation on EC senescence remains unclear because it depends on dose and fractionation, and because the senescent phenotype is heterogeneous and dynamic. METHODS AND MATERIALS: Using a systems biology approach in vitro, we deciphered the dynamic senescence-associated transcriptional program induced by irradiation. RESULTS: Flow cytometry and single-cell RNA sequencing experiments revealed the heterogeneous senescent status of irradiated ECs and allowed to deciphered the molecular program involved in this status. We identified the Interleukin-1 signaling pathway as a key player in the radiation-induced premature senescence of ECs, as well as the endothelial-to-mesenchymal transition process, which shares strong hallmarks of senescence. CONCLUSIONS: Our work provides crucial information on the dynamics of the radiation-induced premature senescence process, the effect of the radiation dose, as well as the molecular program involved in the heterogeneous senescent status of ECs.

Finding the "dark matter" in human and yeast protein network prediction and modelling.

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

Computational Learning of microRNA-Based Prediction of Pouchitis Outcome After Restorative Proctocolectomy in Patients With Ulcerative Colitis.

BACKGROUND: Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). However, inflammation often develops in the pouch, leading to acute or recurrent/chronic pouchitis (R/CP). MicroRNAs (miRNA) are used as accurate diagnostic and predictive biomarkers in many human diseases, including inflammatory bowel diseases. Therefore, we aimed to identify an miRNA-based biomarker to predict the occurrence of R/CP in patients with UC after colectomy and IPAA. METHODS: We conducted a retrospective study in 3 tertiary centers in France. We included patients with UC who had undergone IPAA with or without subsequent R/CP. Paraffin-embedded biopsies collected from the terminal ileum during the proctocolectomy procedure were used for microarray analysis of miRNA expression profiles. Deep neural network-based classifiers were used to identify biomarkers predicting R/CP using miRNA expression and relevant biological and clinical factors in a discovery cohort of 29 patients. The classification algorithm was tested in an independent validation cohort of 28 patients. RESULTS: A combination of 11 miRNA expression profiles and 3 biological/clinical factors predicted the outcome of R/CP with 88% accuracy (area under the curve = 0.94) in the discovery cohort. The performance of the classification algorithm was confirmed in the validation cohort with 88% accuracy (area under the curve = 0.90). Apoptosis, cytoskeletal regulation by Rho GTPase, and fibroblast growth factor signaling were the most dysregulated targets of the 11 selected miRNAs. CONCLUSIONS: We developed and validated a computational miRNA-based algorithm for accurately predicting R/CP in patients with UC after IPAA.

Singular manifolds of proteomic drivers to model the evolution of inflammatory bowel disease status.

The conditions used to describe the presence of an immune disease are often represented by interaction graphs. These informative, but intricate structures are susceptible to perturbations at different levels. The mode in which that perturbation occurs is still of utmost importance in areas such as cell reprogramming and therapeutics models. In this sense, module identification can be useful to well characterise the global graph architecture. To help us with this identification, we perform topological overlap-related measures. Thanks to these measures, the location of highly disease-specific module regulators is possible. Such regulators can perturb other nodes, potentially causing the entire system to change behaviour or collapse. We provide a geometric framework explaining such situations in the context of inflammatory bowel diseases (IBD). IBD are severe chronic disorders of the gastrointestinal tract whose incidence is dramatically increasing worldwide. Our approach models different IBD status as Riemannian manifolds defined by the graph Laplacian of two high throughput proteome screenings. It also identifies module regulators as singularities within the manifolds (the so-called singular manifolds). Furthermore, it reinterprets the characteristic nonlinear dynamics of IBD as compensatory responses to perturbations on those singularities. Then, particular reconfigurations of the immune system could make the disease status move towards an innocuous target state.

In silico prediction of targets for anti-angiogenesis and their in vitro evaluation confirm the involvement of SOD3 in angiogenesis.

Biocomputational network approaches are being successfully applied to predict and extract previously unknown information of novel molecular components of biological systems. In the present work, we have used this approach to predict new potential targets of anti-angiogenic therapies. For experimental validation of predictions, we made use of two in vitro assays related to two key steps of the angiogenic process, namely, endothelial cell migration and formation of "tubular-like" structures on Matrigel. From 7 predicted candidates, experimental tests clearly show that superoxide dismutase 3 silencing or blocking with specific antibodies inhibit both key steps of angiogenesis. This experimental validation was further confirmed with additional in vitro assays showing that superoxide dismutase 3 blocking produces inhibitory effects on the capacity of endothelial cells to form "tubular-like" structure within type I collagen matrix, to adhere to elastin-coated plates and to invade a Matrigel layer. Furthermore, angiogenesis was also inhibited in the en vivo aortic ring assay and in the in vivo mouse Matrigel plug assay. Therefore, superoxide dismutase 3 is confirmed as a putative target for anti-angiogenic therapy.

Male sex in houseflies is determined by Mdmd, a paralog of the generic splice factor gene CWC22.

Across species, animals have diverse sex determination pathways, each consisting of a hierarchical cascade of genes and its associated regulatory mechanism. Houseflies have a distinctive polymorphic sex determination system in which a dominant male determiner, the M-factor, can reside on any of the chromosomes. We identified a gene, Musca domesticamale determiner (Mdmd), as the M-factor. Mdmd originated from a duplication of the spliceosomal factor gene CWC22 (nucampholin). Targeted Mdmd disruption results in complete sex reversal to fertile females because of a shift from male to female expression of the downstream genes transformer and doublesex The presence of Mdmd on different chromosomes indicates that Mdmd translocated to different genomic sites. Thus, an instructive signal in sex determination can arise by duplication and neofunctionalization of an essential splicing regulator.

Integrative Network-based Analysis of Colonic Detoxification Gene Expression in Ulcerative Colitis According to Smoking Status.

BACKGROUNDS AND AIMS: The effect of cigarette smoking [CS] is ambivalent since smoking improves ulcerative colitis [UC] while it worsens Crohn's disease [CD]. Although this clinical relationship between inflammatory bowel disease [IBD] and tobacco is well established, only a few experimental works have investigated the effect of smoking on the colonic barrier homeostasis focusing on xenobiotic detoxification genes. METHODS: A comprehensive and integrated comparative analysis of the global xenobiotic detoxification capacity of the normal colonic mucosa of healthy smokers [n = 8] and non-smokers [n = 9] versus the non-affected colonic mucosa of UC patients [n = 19] was performed by quantitative real-time polymerase chain reaction [qRT PCR]. The detoxification gene expression profile was analysed in CD patients [n = 18], in smoking UC patients [n = 5], and in biopsies from non-smoking UC patients cultured or not with cigarette smoke extract [n = 8]. RESULTS: Of the 244 detoxification genes investigated, 65 were dysregulated in UC patients in comparison with healthy controls or CD patients. The expression of ≥ 45/65 genes was inversed by CS in biopsies of smoking UC patients in remission and in colonic explants of UC patients exposed to cigarette smoke extract. We devised a network-based data analysis approach for differentially assessing changes in genetic interactions, allowing identification of unexpected regulatory detoxification genes that may play a major role in the beneficial effect of smoking on UC. CONCLUSIONS: Non-inflamed colonic mucosa in UC is characterised by a specifically altered detoxification gene network, which is partially restored by tobacco. These mucosal signatures could be useful for developing new therapeutic strategies and biomarkers of drug response in UC.

Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies.

RAS proteins are the founding members of the RAS superfamily of GTPases. They are involved in key signaling pathways regulating essential cellular functions such as cell growth and differentiation. As a result, their deregulation by inactivating mutations often results in aberrant cell proliferation and cancer. With the exception of the relatively well-known KRAS, HRAS and NRAS proteins, little is known about how the interactions of the other RAS human paralogs affect cancer evolution and response to treatment. In this study we performed a comprehensive analysis of the relationship between the phylogeny of RAS proteins and their location in the protein interaction network. This analysis was integrated with the structural analysis of conserved positions in available 3D structures of RAS complexes. Our results show that many RAS proteins with divergent sequences are found close together in the human interactome. We found specific conserved amino acid positions in this group that map to the binding sites of RAS with many of their signaling effectors, suggesting that these pairs could share interacting partners. These results underscore the potential relevance of cross-talking in the RAS signaling network, which should be taken into account when considering the inhibitory activity of drugs targeting specific RAS oncoproteins. This study broadens our understanding of the human RAS signaling network and stresses the importance of considering its potential cross-talk in future therapies.

Increased Proliferation of the Ileal Epithelium as a Remote Effect of Ulcerative Colitis.

BACKGROUND: Aside from cases of backwash ileitis, the ileal mucosa of patients with ulcerative colitis (UC), an idiotypic inflammatory bowel disease, has received little attention despite the fact that colitis is known to trigger alterations in morphology and/or functions of the small intestine remotely. METHODS: The ileal mucosa was studied in patients with UC and in a spontaneous model of colitis (Il10/Nox1 mice) mimicking the histological and clinical features of UC and was also studied in acute and chronic murine models of chemically induced colitis. Proliferation and apoptosis were assessed using morphological and immunohistological methods and Western blot analysis. Peyer's patch immune cell subsets were analyzed. Cytokines levels were quantified using quantitative PCR and Luminex xMAP technology. Total RNA from isolated ileal crypts was used for whole genome transcriptome analysis. RESULTS: The most striking features were an increased ileal crypt length associated with an enhanced cell proliferation of the transit-amplifying cells along with activation of the Wnt/ß-catenin and MAPkinase pathways. These changes did not result from intestinal inflammation as assessed by histology and/or pro-inflammatory cytokine expression levels. The increased proliferation rate was dependent on the duration but not on the severity of colitis and was observed in different mouse models of colitis, including the Il10/Nox1 model and 2,4,6-trinitrobenzenesulfonic acid-treated mice. Interestingly, the ileal mucosa of patients with UC also displayed longer crypts and enhanced cell proliferation compared with control patients. CONCLUSIONS: These data show that despite the absence of inflammation in the small intestine, alterations in the ileal mucosa homeostasis are present in UC.

Protein-protein interfaces from cytochrome c oxidase I evolve faster than nonbinding surfaces, yet negative selection is the driving force.

Respiratory complexes are encoded by two genomes (mitochondrial DNA [mtDNA] and nuclear DNA [nDNA]). Although the importance of intergenomic coadaptation is acknowledged, the forces and constraints shaping such coevolution are largely unknown. Previous works using cytochrome c oxidase (COX) as a model enzyme have led to the so-called "optimizing interaction" hypothesis. According to this view, mtDNA-encoded residues close to nDNA-encoded residues evolve faster than the rest of positions, favoring the optimization of protein-protein interfaces. Herein, using evolutionary data in combination with structural information of COX, we show that failing to discern the effects of interaction from other structural and functional effects can lead to deceptive conclusions such as the "optimizing hypothesis." Once spurious factors have been accounted for, data analysis shows that mtDNA-encoded residues engaged in contacts are, in general, more constrained than their noncontact counterparts. Nevertheless, noncontact residues from the surface of COX I subunit are a remarkable exception, being subjected to an exceptionally high purifying selection that may be related to the maintenance of a suitable heme environment. We also report that mtDNA-encoded residues involved in contacts with other mtDNA-encoded subunits are more constrained than mtDNA-encoded residues interacting with nDNA-encoded polypeptides. This differential behavior cannot be explained on the basis of predicted thermodynamic stability, as interactions between mtDNA-encoded subunits contribute more weakly to the complex stability than those interactions between subunits encoded by different genomes. Therefore, the higher conservation observed among mtDNA-encoded residues involved in intragenome interactions is likely due to factors other than structural stability.

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function-mitotic chromosome condensation-and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.

Uncovering the molecular machinery of the human spindle--an integration of wet and dry systems biology.

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called "hidden spindle hub", proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system.

The function of histamine receptor H4R in the brain revealed by interaction partners.

The histamine H4 receptor is mainly expressed in haematopoietic cells, hence is linked to inflammatory and immune system conditions. It has been recently discovered that the receptor is expressed also in the mammalian central nervous system (CNS), but its role in the brain remains unclear. We address the potential functions of the histamine H4 receptor in the human brain using a 'guilty by association' logic, by close examination of protein-protein functional associations networks in the human proteome.