Mining Biological Networks towards Protein complex Detection and Gene-Disease Association

Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover Disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is to detect protein complexes in protein-protein interaction networks and to associate the detected entities to diseases. The work is divided into three main objectives: first, develop a suitable method for the identification of protein complexes in static interaction networks; second, model the dynamic aspect of protein interaction networks and detect complexes accordingly; and third, design a learning model to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network topology attributes and several biological features of proteins to form a classification model for gene-disease association. The reliability of the proposed methods is supported by various experimental studies conducted to compare them with existing approaches. Pro Rank+ detects more protein complexes than other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than similar techniques. Then, our learning model shows that combining topological and biological features can greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of breast cancer in which we pinpoint disease genes using our learning model; subsequently, we detect favorable groupings of those genes in a protein interaction network using the Pro-rank+ algorithm

Quantitative transcriptomics, and lipidomics in evaluating ovarian developmental effects in Atlantic cod (Gadus morhua) caged at a capped marine waste disposal site

In the present study, a previously capped waste disposal site at Kollevåg (Norway) was selected to study the effects of contaminant leakage on biomarkers associated with Atlantic cod (Gadus morhua) reproductive endocrinology and development. Immature cod were caged for 6 weeks at 3 locations, selected to achieve a spatial gradient of contamination, and compared to a reference station. Quantitative transcriptomic, and lipidomic analysis was used to evaluate the effects of the potential complex contaminant mixture on ovarian developmental and endocrine physiology. The number of expressed transcripts, with 0.75 log2-fold differential expression or more, varied among stations and paralleled the severity of contamination. Particularly, significant bioaccumulation of ∑PCB-7, ∑DDTs and ∑PBDEs were observed at station 1, compared to the other station, including the reference station. Respectively 1416, 698 and 719 differentially expressed genes (DEGs), were observed at stations 1, 2 and 3, compared to the reference station, with transcripts belonging to steroid hormone synthesis pathway being significantly upregulation. Transcription factors such as esr2 and ahr2 were increased at all three stations, with highest fold-change at Station 1. MetaCore pathway maps identified affected pathways that are involved in ovarian physiology, where some unique pathways were significantly affected at each station. For the lipidomics, sphingolipid metabolism was particularly affected at station 1, and these effects paralleled the high contaminant burden at this station. Overall, our findings showed a novel and direct association between contaminant burden and ovarian toxicological and endocrine physiological responses in cod caged at the capped Kollevåg waste disposal site.publishedVersio

ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.publishedVersio

Diagnosis and management of pseudohypoparathyroidism and related disorders : first international Consensus Statement

This Consensus Statement covers recommendations for the diagnosis and management of patients with pseudohypoparathyroidism (PHP) and related disorders, which comprise metabolic disorders characterized by physical findings that variably include short bones, short stature, a stocky build, early-onset obesity and ectopic ossifications, as well as endocrine defects that often include resistance to parathyroid hormone (PTH) and TSH. The presentation and severity of PHP and its related disorders vary between affected individuals with considerable clinical and molecular overlap between the different types. A specific diagnosis is often delayed owing to lack of recognition of the syndrome and associated features. The participants in this Consensus Statement agreed that the diagnosis of PHP should be based on major criteria, including resistance to PTH, ectopic ossifications, brachydactyly and early-onset obesity. The clinical and laboratory diagnosis should be confirmed by a molecular genetic analysis. Patients should be screened at diagnosis and during follow-up for specific features, such as PTH resistance, TSH resistance, growth hormone deficiency, hypogonadism, skeletal deformities, oral health, weight gain, glucose intolerance or type 2 diabetes mellitus, and hypertension, as well as subcutaneous and/or deeper ectopic ossifications and neurocognitive impairment. Overall, a coordinated and multidisciplinary approach from infancy through adulthood, including a transition programme, should help us to improve the care of patients affected by these disorders.Peer reviewe

Modern meat: the next generation of meat from cells

Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

Diagnosis and management of pseudohypoparathyroidism and related disorders: first international Consensus Statement

This Consensus Statement covers recommendations for the diagnosis and management of patients with pseudohypoparathyroidism (PHP) and related disorders, which comprise metabolic disorders characterized by physical findings that variably include short bones, short stature, a stocky build, early-onset obesity and ectopic ossifications, as well as endocrine defects that often include resistance to parathyroid hormone (PTH) and TSH. The presentation and severity of PHP and its related disorders vary between affected individuals with considerable clinical and molecular overlap between the different types. A specific diagnosis is often delayed owing to lack of recognition of the syndrome and associated features. The participants in this Consensus Statement agreed that the diagnosis of PHP should be based on major criteria, including resistance to PTH, ectopic ossifications, brachydactyly and early-onset obesity. The clinical and laboratory diagnosis should be confirmed by a molecular genetic analysis. Patients should be screened at diagnosis and during follow-up for specific features, such as PTH resistance, TSH resistance, growth hormone deficiency, hypogonadism, skeletal deformities, oral health, weight gain, glucose intolerance or type 2 diabetes mellitus, and hypertension, as well as subcutaneous and/or deeper ectopic ossifications and neurocognitive impairment. Overall, a coordinated and multidisciplinary approach from infancy through adulthood, including a transition programme, should help us to improve the care of patients affected by these disorders

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.

Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present "DyCluster", a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster

The number of matched (in green) and detected (in blue) complexes per detection method.

<p>The number of matched (in green) and detected (in blue) complexes per detection method.</p

Statistical significance of scores differences between pairs of protein-complex detection methods without and with gene expression data based on the proposed framework.

<p>The displayed <i>p</i>-values are the ones less than or equal to 0.1 reflecting improvements in the scores, i.e. the matching qualities of the detected protein complexes.</p