A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status.

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient. Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small.

The LEGACy study: a European and Latin American consortium to identify risk factors and molecular phenotypes in gastric cancer to improve prevention strategies and personalized clinical decision making globally.

BACKGROUND: Gastric Cancer (GC) is the fourth most deadly cancer worldwide. Enhanced understanding of its key epidemiological and molecular drivers is urgently needed to lower the incidence and improve outcomes. Furthermore, tumor biology in European (EU) and Latin American (LATAM) countries is understudied. The LEGACy study is a Horizon 2020 funded multi-institutional research approach to 1) detail the epidemiological features including risk factors of GC in current time and 2) develop cost-effective methods to identify and integrate biological biomarkers needed to guide diagnostic and therapeutic approaches with the aim of filling the knowledge gap on GC in these areas. METHODS: This observational study has three parts that are conducted in parallel during 2019-2023 across recruiting centers from four EU and four LATAM countries: Part 1) A case-control study (800 cases and 800 controls) using questionnaires on candidate risk factors for GC, which will be correlated with clinical, demographic and epidemiological parameters. Part 2) A case-control tissue sampling study (400 cases and 400 controls) using proteome, genome, microbiome and immune analyses to characterize advanced (stage III and IV) GC. Patients in this part of the study will be followed over time to observe clinical outcomes. The first half of samples will be used as training cohort to identify the most relevant risk factors and biomarkers, which will be selected to propose cost-effective diagnostic and predictive methods that will be validated with the second half of samples. Part 3) An educational study, as part of our prevention strategy (subjects recruited from the general public) to test and disseminate knowledge on GC risk factors and symptoms by a questionnaire and informative video. Patients could be recruited for more than one of the three LEGACy studies. DISCUSSION: The LEGACy study aims to generate novel, in-depth knowledge on the tumor biological characteristics through integrating epidemiological, multi-omics and clinical data from GC patients at an EU-LATAM partnership. During the study, cost-effective panels with potential use in clinical decision making will be developed and validated. TRIAL REGISTRATION: ClinicalTrials.gov Identifiers: Part 1: NCT03957031 . Part 2: NCT04015466 . Part 3: NCT04019808 .

Targeting autophagy in disease: established and new strategies.

Macroautophagy/autophagy is an evolutionarily conserved pathway responsible for clearing cytosolic aggregated proteins, damaged organelles or invading microorganisms. Dysfunctional autophagy leads to pathological accumulation of the cargo, which has been linked to a range of human diseases, including neurodegenerative diseases, infectious and autoimmune diseases and various forms of cancer. Cumulative work in animal models, application of genetic tools and pharmacologically active compounds, has suggested the potential therapeutic value of autophagy modulation in disease, as diverse as Huntington, Salmonella infection, or pancreatic cancer. Autophagy activation versus inhibition strategies are being explored, while the role of autophagy in pathophysiology is being studied in parallel. However, the progress of preclinical and clinical development of autophagy modulators has been greatly hampered by the paucity of selective pharmacological agents and biomarkers to dissect their precise impact on various forms of autophagy and cellular responses. Here, we summarize established and new strategies in autophagy-related drug discovery and indicate a path toward establishing a more efficient discovery of autophagy-selective pharmacological agents. With this knowledge at hand, modern concepts for therapeutic exploitation of autophagy might become more plausible.Abbreviations: ALS: amyotrophic lateral sclerosis; AMPK: AMP-activated protein kinase; ATG: autophagy-related gene; AUTAC: autophagy-targeting chimera; CNS: central nervous system; CQ: chloroquine; GABARAP: gamma-aminobutyric acid type A receptor-associated protein; HCQ: hydroxychloroquine; LYTAC: lysosome targeting chimera; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; NDD: neurodegenerative disease; PDAC: pancreatic ductal adenocarcinoma; PE: phosphatidylethanolamine; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3K: class III phosphatidylinositol 3-kinase; PtdIns3P: phosphatidylinositol 3-phosphate; PROTAC: proteolysis-targeting chimera; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SQSTM1/p62: sequestosome 1; ULK1: unc-51 like autophagy activating kinase 1.

The mechanism of macroautophagy: The movie.

This animated movie presents the mechanism of macroautophagy, hereafter autophagy, by showing the molecular features of the formation of autophagosomes, the hallmark organelle of this intracellular catabolic pathway. It is based on our current knowledge and it also illustrates how autophagosomes can recognize and eliminate selected cargoes.

Identification of the most vulnerable populations in the psychosocial sphere: a cross-sectional study conducted in Catalonia during the strict lockdown imposed against the COVID-19 pandemic.

DESIGN AND OBJECTIVES: A cross-sectional study to evaluate the impact of COVID-19 on the psychosocial sphere in both the general population and healthcare workers (HCWs). METHODS: The study was conducted in Catalonia (Spain) during the first wave of the COVID-19 pandemic when strict lockdown was in force. The study population included all people aged over 16 years who consented to participate in the study and completed the survey, in this case a 74-question questionnaire shared via social media using snowball sampling. A total of 56 656 completed survey questionnaires were obtained between 3 and 19 April 2020.The primary and secondary outcome measures included descriptive statistics for the non-psychological questions and the psychological impact of the pandemic, such as depression, anxiety, stress and post-traumatic stress disorder question scores. RESULTS: A n early and markedly negative impact on family finances, fear of working with COVID-19 patients and ethical issues related to COVID-19 care among HCWs was observed. A total of seven target groups at higher risk of impaired mental health and which may therefore benefit from an intervention were identified, namely women, subjects aged less than 42 years, people with a care burden, socioeconomically deprived groups, people with unskilled or unqualified jobs, patients with COVID-19 and HCWs working with patients with COVID-19. CONCLUSIONS: Active implementation of specific strategies to increase resilience and to prepare an adequate organisational response should be encouraged for the seven groups identified as high risk and susceptible to benefit from an intervention. TRIAL REGISTRATION NUMBER: NCT04378452.

CuBlock: a cross-platform normalization method for gene-expression microarrays.

MOTIVATION: Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. RESULTS: We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. AVAILABILITY AND IMPLEMENTATION: CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is a B lymphoid malignancy highly dependent on the microenvironment. Despite new targeted therapies such as ibrutinib and venetoclax, disease progression and relapse remain an issue. CLL cell interactions with the supportive tissue microenvironment play a critical role in disease pathogenesis. We used a platform for drug discovery based on systems biology and artificial intelligence, to identify drugs targeting key proteins described to have a role in the microenvironment. The selected compounds were screened in CLL cell lines in the presence of stromal cells to mimic the microenvironment and validated the best candidates in primary CLL cells. Our results showed that the commercial drug simvastatin was the most effective and selective out of the tested compounds. Simvastatin decreased CLL cell survival and proliferation as well as cell adhesion. Importantly, this drug enhanced the antitumor effect of venetoclax and ibrutinib. We proposed that systems biology approaches combined with pharmacological screening could help to find new drugs for CLL treatment and to predict new combinations with current therapies. Our results highlight the possibility of repurposing widely used drugs such as statins to target the microenvironment and to improve the efficacy of ibrutinib or venetoclax in CLL cells.

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Characterization of inflammatory response in hepatorenal syndrome: Relationship with kidney outcome and survival.

BACKGROUND: Several lines of evidence indicate that decompensated cirrhosis is characterized by the presence of systemic inflammation. Hepatorenal syndrome (HRS-AKI) is a unique type of renal failure that occurs at late stages of cirrhosis. However, confirmation of the presence and significance of such inflammatory response in HRS-AKI is lacking. AIM AND METHODS: To characterize the systemic inflammatory response, as estimated by measuring a large number of cytokines, in 161 patients hospitalized for an acute decompensation of cirrhosis: 44 patients without acute kidney injury (AKI), 63 patients with hypovolaemia-induced AKI and 58 patients with HRS-AKI. RESULTS: HRS-AKI was characterized by an altered cytokine profile compared to the other two groups, particularly IL-6, IL-8, TNF-α, VCAM-1, fractalkine and MIP-1α. The inflammatory response was not related to presence of bacterial infection, concomitant acute-on-chronic liver failure or severity of renal dysfunction. Patients who responded to terlipressin and albumin had only a decrease in TNF-α and RANTES after treatment without changes in other cytokines. Interestingly, patients with persistent HRS-AKI had higher levels of IP-10 and VCAM-1 compared to those with resolution of HRS-AKI. VCAM-1 was also an independent predictor of 3-month mortality. A systems biology analysis approach showed that the inflammatory status of HRS-AKI was similar to that of chronic nonhepatic inflammatory conditions, such as lupus erythematosus or inflammatory bowel disease. CONCLUSION: Hepatorenal syndrome is characterized by a marked systemic inflammatory state, reminiscent of that of nonhepatic inflammatory diseases, that correlates with patient outcomes.

Driving next-generation autophagy researchers towards translation (DRIVE), an international PhD training program on autophagy.

The European autophagy consortium Driving next-generation autophagy researchers towards translation (DRIVE) held its kick-off meeting in Groningen on the 14th and 15th of June 2018. This Marie Sklodowska-Curie Early Training Network was approved under the European Union's Horizon 2020 Research and Innovation Program and is funded for 4 years. Within DRIVE, 14 European research teams from academia and industry will train 15 PhD students through applied, cross-disciplinary and collaborative macroautophagy/autophagy research. The goal of DRIVE is to stimulate applied approaches in autophagy research and provide training towards translation, while advancing our knowledge on autophagy in specific physiological and pathological states. The strong focus on translation will prepare the PhD students to be at the forefront to exploit autophagy for the development of therapies directly benefitting patients. Thereby, DRIVE will contribute to filling the educational gap that currently exists between academia and industry, and will prepare its PhD students for alternative and highly flexible professional paths.

Revealing the molecular relationship between type 2 diabetes and the metabolic changes induced by a very-low-carbohydrate low-fat ketogenic diet.

BACKGROUND: The prevalence of type 2 diabetes is increasing worldwide, accounting for 85-95% of all diagnosed cases of diabetes. Clinical trials provide evidence of benefits of low-carbohydrate ketogenic diets in terms of clinical outcomes on type 2 diabetes patients. However, the molecular events responsible for these improvements still remain unclear in spite of the high amount of knowledge on the primary mechanisms of both the diabetes and the metabolic state of ketosis. Molecular network analysis of conditions, diseases and treatments might provide new insights and help build a better understanding of clinical, metabolic and molecular relationships among physiological conditions. Accordingly, our aim is to reveal such a relationship between a ketogenic diet and type 2 diabetes through systems biology approaches. METHODS: Our systemic approach is based on the creation and analyses of the cell networks representing the metabolic state in a very-low-carbohydrate low-fat ketogenic diet. This global view might help identify unnoticed relationships often overlooked in molecule or process-centered studies. RESULTS: A strong relationship between the insulin resistance pathway and the ketosis main pathway was identified, providing a possible explanation for the improvement observed in clinical trials. Moreover, the map analyses permit the formulation of some hypothesis on functional relationships between the molecules involved in type 2 diabetes and induced ketosis, suggesting, for instance, a direct implication of glucose transporters or inflammatory processes. The molecular network analysis performed in the ketogenic-diet map, from the diabetes perspective, has provided insights on the potential mechanism of action, but also has opened new possibilities to study the applications of the ketogenic diet in other situations such as CNS or other metabolic dysfunctions.

Unveiling the role of network and systems biology in drug discovery.

Network and systems biology offer a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets, and the effects of modifying them, without losing the key molecular details. Here we review some recent advances in network and systems biology applied to human health, and discuss how they can have a big impact on some of the most interesting areas of drug discovery. In particular, we claim that network biology will play a central part in the development of novel polypharmacology strategies to fight complex multifactorial diseases, where efficacious therapies will need to center on altering entire pathways rather than single proteins. We briefly present new developments in the two areas where we believe network and system biology strategies are more likely to have an immediate contribution: predictive toxicology and drug repurposing.

Analysis of the contribution of the globin and reductase domains to the ligand-binding properties of bacterial haemoglobins.

Bacterial Hbs (haemoglobins), like VHb (Vitreoscilla sp. Hb), and flavoHbs (flavohaemoglobins), such as FHP (Ralstonia eutropha flavoHb), have different autoxidation and ligand-binding rates. To determine the influence of each domain of flavoHbs on ligand binding, we have studied the kinetic ligand-binding properties of oxygen, carbon monoxide and nitric oxide to the chimaeric proteins, FHPg (truncated form of FHP comprising the globin domain alone) and VHb-Red (fusion protein between VHb and the C-terminal reductase domain of FHP) and compared them with those of their natural counterparts, FHP and VHb. Moreover, we also analysed polarity and solvent accessibility to the haem pocket of these proteins. The rate constants for the engineered proteins, VHb-Red and FHPg, do not differ significantly from those of their natural counterparts, VHb and FHP respectively. Our results suggest that the globin domain structure controls the reactivity towards oxygen, carbon monoxide and nitric oxide. The presence or absence of a reductase domain does not affect the affinity to these ligands.

Ligand binding properties of bacterial hemoglobins and flavohemoglobins.

Bacterial hemoglobins and flavohemoglobins share a common globin fold but differ otherwise in structural and functional aspects. The bases of these differences were investigated through kinetic studies on oxygen, carbon monoxide, and nitric oxide binding. The novel bacterial hemoglobins from Clostridium perfringens and Campylobacter jejuni and the flavohemoglobins from Bacillus subtilis and Salmonella enterica serovar Typhi have been analyzed. Examination of the biochemical and ligand binding properties of these proteins shows a clear distinction between the two groups. Flavohemoglobins show a much greater tendency to autoxidation compared to bacterial hemoglobins. The differences in affinity for oxygen, carbon monoxide, and nitric oxide between bacterial hemoglobins and flavohemoglobins are mainly due to differences in the association rate constants. The second-order rate constants for oxygen and carbon monoxide binding to bacterial hemoglobins are severalfold higher than those for flavohemoglobins. A similar trend is observed for NO association with the oxidized iron(III) form of the proteins. No major differences are observed among the values obtained for the dissociation rate constants for the two groups of bacterial proteins studied, and these constants are all rather similar to those for myoglobin. Taken together, our data suggest that differences exist between the mechanisms of ligand binding to bacterial hemoglobins and flavohemoglobins, suggesting different functions in the cell.

Expression of Vitreoscilla haemoglobin in tobacco cell cultures relieves nitrosative stress in vivo and protects from NO in vitro.

Targeted expression of Vitreoscilla haemoglobin (VHb) has been analysed in Nicotiana tabacum plants and suspension cultures under various growth and stress conditions. VHb localization to different cell compartments (cytoplasm, chloroplast and mitochondria) was successful, as judged by signal peptide cleavage. The presence of VHb in subcellular compartments did not result in phenotypical differences between these plant lines. In contrast with previous reports, we were unable to discern any significant changes in growth and other phenotypical characteristics between VHb-expressing and transformed control plants under standard growth conditions. When exposed to nitrosative stress, growth of VHb-expressing cultures was less affected relative to transformed controls. Furthermore, a diminished inactivation of the NO-sensitive enzyme aconitase was observed in the presence of VHb. In contrast, no protective effect of VHb expression against oxidative stress could be detected.

Bacterial hemoglobins and flavohemoglobins for alleviation of nitrosative stress in Escherichia coli.

Escherichia coli MG1655 cells expressing novel bacterial hemoglobin and flavohemoglobin genes from a medium-copy-number plasmid were grown in shake flask cultures under nitrosative and oxidative stress. E. coli cells expressing these proteins display enhanced resistance against the NO(.) releaser sodium nitroprusside (SNP) relative to that of the control strain bearing the parental plasmid. Expression of bacterial hemoglobins originating from Campylobacter jejuni (CHb) and Vitreoscilla sp. (VHb) conferred resistance on SNP-challenged cells. In addition, it has been shown that NO(.) detoxification is also a common feature of flavohemoglobins originating from different taxonomic groups and can be transferred to a heterologous host. These observations have been confirmed in a specific in vitro NO(.) consumption assay. Protein extracts isolated from E. coli strains overexpressing flavohemoglobins consumed authentic NO(.) more readily than protein extracts from the wild-type strain. Oxidative challenge to the cells evoked nonuniform responses from the various cell cultures. Improved oxidative-stress-sustaining properties had also been observed when the flavohemoglobins from E. coli, Klebsiella pneumoniae, Deinococcus radiodurans, and Pseudomonas aeruginosa were expressed in E. coli.

Improved cell growth in tobacco suspension cultures expressing Vitreoscilla hemoglobin.

Expression of the gene encoding bacterial hemoglobin (VHb) from Vitreoscilla has been previously used to improve recombinant cell growth and enhance product formation under microaerobic conditions, a common phenomenon in large-scale cultivations of bacteria. This technology has now been applied to tobacco suspension cultures. Tobacco suspension cultures have been generated from VHb-expressing tobacco plants. Cell cultures were capable of producing an active hemoglobin. When grown in shake flasks, the cells did not show any lag-phase and exhibited improved cell growth, compared to controls carrying the parental plasmid.

Expressing creatine kinase in transgenic tobacco--a first step towards introducing an energy buffering system in plants.

Creatine kinase a key enzyme in cellular energy homeostasis of vertebrates offers the promise of engineering plants with enhanced stress tolerance. In order to provide plants with such an energy buffering system, tobacco was transformed with a cDNA, encoding the cytosolic brain-type isoform of chicken creatine kinase (BB-CK), the expression of which was under the control of the cauliflower mosaic virus 35S (CaMV 35S) promoter. Transgenic tobacco plants were selected and suspension cultures generated. Both transgenic plants and suspension cultures were shown to stably express enzymatically active BB-CK in vitro and in vivo, and in most cases for three successive generations (T0-T2). Exogenously supplied creatine was shown to enter the plant cells and resulted in only a slight reduction in root growth at concentrations up to 10 mM. Furthermore, the BB-CK expressing tobacco plants and cell suspension cultures were able to convert creatine into phosphocreatine.