Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer.

The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Increasing and sustaining blood-borne virus screening in Spain and Portugal throughout the COVID-19 pandemic: a multi-center quality improvement intervention.

Background: Around 57,000 people in Spain and Portugal currently living with HIV or chronic hepatitis C are unaware of their infection. The COVID-19 pandemic severely disrupted screening efforts for these infections. We designed an intervention to increase and sustain opportunistic blood-borne virus (BBV) screening and linkage to care (SLTC) by implementing the TEST model. Methods: The Plan Do Study Act (PDSA) method of quality improvement (QI) was implemented in 8 healthcare organizations (HCOs), including four hospitals, two clusters of community health centers, and two community-based organizations (CBOs). Baseline assessment included a review of BBV SLTC practices, testing volume, and results 12 months before the intervention. Changes in BBV testing rates over time were measured before, during, and after the COVID-19 lockdowns in 2020. A mixed ANOVA model was used to analyze the possible effect on testing volumes among HCOs over the three study periods. Intervention: BBV testing was integrated into normal clinical flow in all HCOs using existing clinical infrastructure and staff. Electronic health record (EHR) systems were modified whenever possible to streamline screening processes, implement systemic institutional policy changes, and promote QI. Results: Two years after the launch of the intervention in screening practices, testing volumes increased by 116%, with formal healthcare settings recording larger increases than CBOs. The start of the COVID-19 lockdowns was accompanied by a global 60% decrease in testing in all HCOs. Screening emergency department patients or using EHR systems to automate screening showed the highest resilience and lowest reduction in testing. HCOs recovered 77% of their testing volume once the lockdowns were lifted, with CBOs making the fullest recovery. Globally, enhanced screening techniques enabled HCOs to diagnose a total of 1,860 individuals over the research period. Conclusions: Implementation of the TEST model enabled HCOs to increase and sustain BBV screening, even during COVID-19 lockdowns. Although improvement in screening was noted in all HCOs, additional work is needed to develop strong patient linkage to care models in challenging times, such as global pandemics.

Evaluating molecular representations in machine learning models for drug response prediction and interpretability.

Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models.

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale.

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.

Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells.

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

Radioterapia en tumor de células gigantes tenosinovial (sinovitis villonodular pigmentada) / Radiation therapy in tenosynovial giant cell tumor (pigmented villonodular synovitis)

Presentar nuestra experiencia de 18 años en el tratamiento con radioterapia y evaluar cifras de control tumoral local en pacientes con diagnóstico de tumor de células gigantes tenosinovial difuso sinovitis villonodular pigmentada difusa. 33 pacientes, tratados durante el período 2000-2018. En 19 (57,6 %) se practicó sinovectomía parcial, 10 (30,3 %) fueron tratados con artroplastia y sinovectomía, 4 (12,2 %) con sinovectomía total. 32 pacientes recibieron radioterapia posoperatoria, 1 paciente preoperatoria. Técnica más empleada fue planificación 2D 51,5 % seguida de conformada con planificación 3D (RTC3D) 48,5 %. La dosis total promedio administrada 44 Gy (rango 10,5 - 50). Tiempo promedio de tratamiento radiante 28 días (8-35). Tiempo de seguimiento entre 0,7 - 240,8 meses, mediana 12 meses, promedio 52,1 meses. 26 pacientes (79 %) presentaron mejoría de la sintomatología inicial y 6 (18 %) refirieron estabilidad de los síntomas. La respuesta clínica al tratamiento en relación al tiempo de seguimiento, 12 pacientes (36,4 %) estaban asintomáticos, 10 con un seguimiento mayor a 60 meses; 14 (42,4 %) refieren respuesta clínica satisfactoria, (2 con un seguimiento mayor a 60 meses) 6 pacientes presentaban enfermedad estable, para un control local del 97 %. El 87,9 % presentaron dermatitis grado I, 1 desarrolló dermatitis grado II, 3 no presentaron efectos adversos. La radioterapia es una modalidad de tratamiento muy efectiva como adyuvante a la sinovectomía, observándose altas tasas de control local de la enfermedad con una baja morbilidad(AU)

To report our eighteen-year experience with radiation therapy in the treatment of diffuse tenosinovial giant cell tumor / diffuse pigmented villonodular synovitis and to assess local control of the disease. A review of 33 patients with treated with radiation therapy during the period 2000-2018 was done. 19 (57.6 %) partial synovectomy was performed, 10 (30.3 %) underwent arthroplasty plus synovectomy, 4 (12.2 %) total synovectomy. 32 patients received radiotherapy postoperative and 1 pre-operative. Most common technique employed was conventional (2D) in 51.5 % and 3D conformal (3DCRT) in 48.5 %. The average total dose was 44 Gy (range 10.5-50), with a mean treatment time of 28 days (8-35). Follow-up time ranged from 0.7- 240.8 months, median time and mean time of 12 and 52.1 months respectively After RT 26 (79 %) of the patients obtained improvement of the initial symptoms and 6 (18 %) were stable. 12 patients (36.4 %) were asymptomatic with follow-up time longer than 36 months (10 of 12 had follow-up time >60 months), 14 (42.4 %) had significant clinical improvement (2 of 14 had follow-up time >60 months), and 6 had stable disease, local control of 97 %. Complications were few, acute skin toxicity was grade I in 29 (87.9%) and grade II in 1 patient. There was no significant chronic toxicity. Radiation therapy is an effective adjuvant treatment modality after synovectomy in patients with high local control rates and low morbidity(AU)

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Deep learning for drug response prediction in cancer.

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:  mrocha@di.uminho.pt.

Biological synthesis of iron nanoparticles using hydrolysates from a waste-based biorefinery.

The purpose of this work was to produce iron nanoparticles (Fe-NP) by microbial pathway from anaerobic bacteria grown in anaerobic fluidized bed reactors (AnFBRs) that constitute a new stage of a waste-based biorefinery. Bioparticles from biological fluidized bed reactors from a biorefinery of organic fraction of municipal solid wastes (that produces hydrolysates rich in reducing sugars) were nanodecorated (embedded nanobioparticle or nanodecorated bioparticle, ENBP) by biological reduction of iron salts. Factors "origin of bioparticles" (either from hydrogenogenic or methanogenic fluidized bed reactor) and "type of iron precursor salt" (iron chloride or iron citrate) were explored. SEM and high-resolution transmission electron microscopy (HRTEM) showed amorphous distribution of nanoparticles (NP) on the bioparticles surface, although small structures that are nanoparticle-like could be seen in the SEM micrographs. Some agglomeration of NPs was confirmed by DLS. Average NP size was lower in general for NP in ENBP-M than ENBP-H according to HRTEM. The factors did not have a significant influence on the specific surface area of NPs, which was high and in the range 490 to 650 m2 g-1. Analysis by EDS displayed consistent iron concentration 60-65% iron in nanoparticles present in ENBP-M (bioparticles previously grown in methanogenic bioreactor), whereas the iron concentration in NPs present in ENBP-H (bioparticles previously grown in hydrogenogenic bioreactor) was more variable in a range from 8.5 to 62%, depending on the iron salt. X-ray diffraction patterns showed the typical peaks for magnetite at 35° (3 1 1), 43° (4 0 0), and 62° (4 0 0); moreover, siderite diffraction pattern was found at 26° (0 1 2), 38° (1 1 0), and 42° (1 1 3). Results of infrared analysis of ENBP in our work were congruent with presence of magnetite and occasionally siderite determined by XRD analysis as well as presence of both Fe+2 and F+3 (and selected satellite signal peaks) observed by XPS. Our results on the ENBPs hold promise for water treatment, since iron NPs are commonly used in wastewater technologies that treat a wide variety of pollutants. Finally, the biological production of ENBP coupled to a biorefinery could become an environmentally friendly platform for nanomaterial biosynthesis as well as an additional source of revenues for a waste-based biorefinery.

Machine Learning-Enhanced T Cell Neoepitope Discovery for Immunotherapy Design.

Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases.

Greater osteoblast densities due to the addition of amphiphilic peptide nanoparticles to nano hydroxyapatite coatings.

BACKGROUND: In vitro and in vivo studies have shown that metallic implants coated with nano hydroxyapatite (HA) reduce the time needed for complete osseointegration compared to metallic implants coated with conventional or micron-sized HA. Moreover, due to their biologically inspired nanometer dimensions, amphiphilic peptide nanoparticles (APNPs) can also promote osteoblast attachment and enhance other cell functions if used as a coating material. Coatings made of HA and APNPs could improve osteoblast functions, but have never been tested. PURPOSE: The objective of this study was to prepare coatings of nanocrystalline HA and APNPs on poly(2-hydroxyethyl methacrylate) (pHEMA) coatings in order to improve osteoblast (bone-forming cells) adhesion and cell density. METHODS: HA was synthesized by a wet chemical process. Coatings were synthesized with different conditions and components. RESULTS: X-ray diffraction infrared spectroscopy, transmission electron microscopy, and electron diffraction showed that nanocrystalline HA was synthesized with an expected nano size and shape distribution but with low impurities. pHEMA hydrogels with HA and APNPs increased osteoblast densities after 3 days compared to controls. CONCLUSION: Since cell proliferation is a prerequisite function for bone formation, these results imply that the current materials should be tested for a wide range of orthopedic applications.

Design, Synthesis, Experimental and Theoretical Characterization of a New Multitarget 2-Thienyl-N-Acylhydrazone Derivative.

Pulmonary arterial hypertension (PAH) is a chronic cardiovascular disease that displays inflammatory components, which contributes to the difficulty of adequate treatment with the available therapeutic arsenal. In this context, the N-acylhydrazone derivative LASSBio-1359 was previously described as a multitarget drug candidate able to revert the events associated with the progression of PAH in animal models. However, in spite of having a dual profile as PDE4 inhibitor and adenosine A2A receptor agonist, LASSBio-1359 does not present balanced potencies in the modulation of these two targets, which difficult its therapeutic use. In this paper, we describe the design concept of LASSBio-1835, a novel structural analogue of LASSBio-1359, planned by exploiting ring bioisosterism. Using X-ray powder diffraction, calorimetric techniques, and molecular modeling, we clearly indicate the presence of a preferred synperiplanar conformation at the amide function, which is fixed by an intramolecular 1,5-N∙∙∙S σ-hole intramolecular interaction. Moreover, the evaluation of LASSBio-1835 (4) as a PDE4 inhibitor and as an A2A agonist confirms it presents a more balanced dual profile, being considered a promising prototype for the treatment of PAH.

The long non-coding RNA HOTAIR is transcriptionally activated by HOXA9 and is an independent prognostic marker in patients with malignant glioma.

The lncRNA HOTAIR has been implicated in several human cancers. Here, we evaluated the molecular alterations and upstream regulatory mechanisms of HOTAIR in glioma, the most common primary brain tumors, and its clinical relevance. HOTAIR gene expression, methylation, copy-number and prognostic value were investigated in human gliomas integrating data from online datasets and our cohorts. High levels of HOTAIR were associated with higher grades of glioma, particularly IDH wild-type cases. Mechanistically, HOTAIR was overexpressed in a gene dosage-independent manner, while DNA methylation levels of particular CpGs in HOTAIR locus were associated with HOTAIR expression levels in GBM clinical specimens and cell lines. Concordantly, the demethylating agent 5-Aza-2'-deoxycytidine affected HOTAIR transcriptional levels in a cell line-dependent manner. Importantly, HOTAIR was frequently co-expressed with HOXA9 in high-grade gliomas from TCGA, Oncomine, and our Portuguese and French datasets. Integrated in silico analyses, chromatin immunoprecipitation, and qPCR data showed that HOXA9 binds directly to the promoter of HOTAIR. Clinically, GBM patients with high HOTAIR expression had a significantly reduced overall survival, independently of other prognostic variables. In summary, this work reveals HOXA9 as a novel direct regulator of HOTAIR, and establishes HOTAIR as an independent prognostic marker, providing new therapeutic opportunities to treat this highly aggressive cancer.

Design, synthesis and pharmacological evaluation of N-benzyl-piperidinyl-aryl-acylhydrazone derivatives as donepezil hybrids: Discovery of novel multi-target anti-alzheimer prototype drug candidates.

A new series of sixteen multifunctional N-benzyl-piperidine-aryl-acylhydrazones hybrid derivatives was synthesized and evaluated for multi-target activities related to Alzheimer's disease (AD). The molecular hybridization approach was based on the combination, in a single molecule, of the pharmacophoric N-benzyl-piperidine subunit of donepezil, the substituted hydroxy-piperidine fragment of the AChE inhibitor LASSBio-767, and an acylhydrazone linker, a privileged structure present in a number of synthetic aryl- and aryl-acylhydrazone derivatives with significant AChE and anti-inflammatory activities. Among them, compounds 4c, 4d, 4g and 4j presented the best AChE inhibitory activities, but only compounds 4c and 4g exhibited concurrent anti-inflammatory activity in vitro and in vivo, against amyloid beta oligomer (AßO) induced neuroinflammation. Compound 4c also showed the best in vitro and in vivo neuroprotective effects against AßO-induced neurodegeneration. In addition, compound 4c showed a similar binding mode to donepezil in both acetylated and free forms of AChE enzyme in molecular docking studies and did not show relevant toxic effects on in vitro and in vivo assays, with good predicted ADME parameters in silico. Overall, all these results highlighted compound 4c as a promising and innovative multi-target drug prototype candidate for AD treatment.

Nutrient depletion and TOR inhibition induce 18S and 25S ribosomal RNAs resistant to a 5'-phosphate-dependent exonuclease in Candida albicans and other yeasts.

BACKGROUND: Messenger RNA (mRNA) represents a small percentage of RNAs in a cell, with ribosomal RNA (rRNA) making up the bulk of it. To isolate mRNA from eukaryotes, typically poly-A selection is carried out. Recently, a 5´-phosphate-dependent, 5´â†’3´ processive exonuclease called Terminator has become available. It will digest only RNA that has a 5´-monophosphate end and therefore it is very useful to eliminate most of rRNAs in cell. RESULTS: We have found that in the pathogenic yeast Candida albicans, while 18S and 25S components isolated from yeast in robust growth phase are easily eliminated by Terminator, those isolated from cells in the nutritionally diminished stationary phase, become resistant to digestion by this enzyme. Additional digestions with alkaline phosphatase, tobacco pyrophosphatase combined with Terminator point toward the 5'-prime end of 18S and 25S as the source of this resistance. Inhibition of TOR by rapamycin also induces resistance by these molecules. We also find that these molecules are incorporated into the ribosome and are not just produced incidentally. Finally, we show that three other yeasts show the same behavior. CONCLUSIONS: Digestion of RNA by Terminator has revealed 18S and 25S rRNA molecules different from the accepted processed ones seen in ribosome generation. The reason for these molecules and the underlying mechanism for their formation is unknown. The preservation of this behavior across these yeasts suggests a useful biological role for it, worthy of further inquiry.

Resection of the Tracheobronchial Bifurcation With Complete Preservation of Lung Parenchyma.

BACKGROUND: The resection of the tracheobronchial bifurcation with complete preservation of lung parenchyma remains a challenge owing to the limited indications for surgery, anesthesiologic management, operative technique, and postoperative course. The aim of this retrospective study was to evaluate factors influencing the perioperative course and long-term survival. METHODS: Between 1989 and 2014, 19 patients underwent a resection of the distal trachea and carina with complete preservation of lung tissue, 16 for malignant tumors (7 adenoid cystic carcinomas, 3 carcinoid tumors, 3 mucoepidermoid tumors, 2 squamous cell carcinomas, and 1 small cell carcinomas), 2 for inflammatory stenosis, and 1 after a complex traumatic rupture. RESULTS: Surgical approach was posterolateral thoracotomy in 17 patients and median sternotomy in 2. In 16 patients, end-to-end anastomosis was performed, and in 3 patients, combined end-to-end and side-to-end anastomosis were performed. The operative mortality was 0%, the perioperative complication rate was 26.3%. Six patients with adenoid cystic carcinoma and all patients with lung carcinoma received adjuvant radiotherapy; only 1 patient with small cell lung cancer had chemotherapy before surgery. Long-term results are excellent in patients with benign disease, typical and atypical carcinoid tumor, mucoepidermoid carcinoma, and in most patients with adenoid cystic carcinoma. Two patients with lung cancer died 28 and 45 months after surgery, and 1 patient with adenoid cystic carcinoma died 75 months after surgery. CONCLUSIONS: Resection of the tracheobronchial bifurcation with complete preservation of lung indicated for selected patients with local tumor growth at the distal trachea and carina provides low perioperative mortality and complications and results in long-term survival rates.

Analysing Algorithms and Data Sources for the Tissue-Specific Reconstruction of Liver Healthy and Cancer Cells.

Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no "ideal" algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge.