A multi-omics study of circulating phospholipid markers of blood pressure

High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.

High BRCA1 gene expression increases the risk of early distant metastasis in ER+ breast cancers

Although the function of the BRCA1 gene has been extensively studied, the relationship between BRCA1 gene expression and tumor aggressiveness remains controversial in sporadic breast cancers. Because the BRCA1 protein is known to regulate estrogen signaling, we selected microarray data of ER+ breast cancers from the GEO public repository to resolve previous conflicting findings. The BRCA1 gene expression level in highly proliferative luminal B tumors was shown to be higher than that in luminal A tumors. Survival analysis using a cure model indicated that patients of early ER+ breast cancers with high BRCA1 expression developed rapid distant metastasis. In addition, the proliferation marker genes MKI67 and PCNA, which are characteristic of aggressive tumors, were also highly expressed in patients with high BRCA1 expression. The associations among high BRCA1 expression, high proliferation marker expression, and high risk of distant metastasis emerged in independent datasets, regardless of tamoxifen treatment. Tamoxifen therapy could improve the metastasis-free fraction of high BRCA1 expression patients. Our findings link BRCA1 expression with proliferation and possibly distant metastasis via the ER signaling pathway. We propose a testable hypothesis based on these consistent results and offer an interpretation for our reported associations.

Is strenuous exercise harmful in the era of COVID-19 vaccination and may increase the risk of the rare adverse effects of myocarditis and thrombosis in recently vaccinated young people? The secret is IL-6. A retrospective and prospective observational study.

Interleukin 6 (IL-6) is a type of interleukin that functions as both a pro-inflammatory and anti-inflammatory cytokine. It is encoded by the IL6 gene in humans. Both COVID-19 infection and S-Protein Based Vaccines for COVID-19 were found to induce the production of proinflammatory IL-6, and also, strenuous exercise was found to induce IL-6 secretion by the skeletal muscles via lactate. Exercise causes skeletal muscle cells to release IL-6, and it raises the plasma concentration of IL-6 100 times higher than at rest . Exercise-induced IL-6 release is highly correlated with exercise intensity and duration; thus, IL-6 is regarded as an energy sensor released by contracting muscles. Although, COVID-19 infection and S-Protein Based Vaccines for COVID-19 have similar pathological effects, such as myocardial infarction, thrombotic and coagulation abnormalities (deep thrombosis), but these adverse effects are rarely associated with S-Protein Based Vaccines for COVID-19. Recently, it was showed that most patients who experienced myocarditis after COVID-19 vaccine were young male youth aged 16 to 29 years had the highest incidence of myocarditis. Interestingly, It was observed that IL-6 was linked to adverse effects such as thrombosis and myocarditis, both of which are similar to that was caused by COVID-19 infection, and that S-Protein based vaccines for COVID-19. Here, we propose a testable hypothesis that strenuous exercise could be a risk and cofactor helping in the existing of these adverse effects in young people such as myocarditis and thrombosis via induction the secretion of proinflammatory IL-6. In our retrospective and prospective observational study we will assess the possible correlation among the strenuous exercise , IL-6, myocarditis and thrombosis. The study will be multi-center and will involve young patient who will be vaccinated with first ,second and third dose of S-Protein Based Vaccines for COVID-19 (Moderna and Pfizer-BioNTech vaccine). Pfizer and BioNTech have successfully developed the BNT162b2 mRNA vaccine, which consists of the full-length S glycoprotein with the K986P and V987P mutation sites. Also, Moderna's mRNA-1273 vaccine contains the coding sequence for a S glycoprotein stabilized by a pair of proline substitutions (K986P/V987P), a transmembrane anchor, and an intact S1-S2 cleavage site .

The Electric Consciousness Hypothesis and The Prediction of Consciousness Neuron

What makes us assume that our brain generates consciousness? Has there been an experiment to prove it? This article suggests another way to explore consciousness. It is that consciousness may be fundamental instead of being generated. This article proposes a testable hypothesis to explain how consciousness, as a fundamental force, cooperates with the neurons in our brain. And explain how this hypothesis can solve the problems, including how free will works in a biological machine, how emotion and memory work, even what will happen after you die, etc. Of course, we should be able to answer the death question after understanding the mechanism of consciousness.

Effect of Relative Humidity in Air on the Transmission of Respiratory Viruses

Viral respiratory infections have plagued mankind over its known history. Unfortunately, there has been a lack of meaningful progress in preventing the spread of viral respiratory infections globally. The central dogma appears to be that viruses are the villains. This framing focuses on a viral load balance (VLB) in the air. It follows that physical dilution through various means have been the primary focus of attempts to reduce the spread of infections. The problem of obesity provides a good example of how paradigm blindness can slow down progress in a field. Obesity has been framed as an energy balance disorder that blames overeating and lack of exercise for weight gain. Reframing obesity as a disorder of fat metabolism and storage caused by the quantity and quality of carbohydrates in the diet, referred to as the carbohydrate-insulin model (CIM), opened an alternative line of questioning with a testable hypothesis. Similarly, we postulate an alternative way to frame the spread of viral respiratory infections that would lead to new insights and potentially new ways to prevent infections. It has long been recognized that viral respiratory infections show a pronounced seasonal variation, referred to as seasonal forging, such that they increase in the winter but decrease or virtually disappear in the summer. In temperate regions, people spend over 90% of their time indoors. This is, therefore, where most respiratory infections are expected to occur. Evidence has been accumulating for decades on the strong correlation between variations in indoor relative humidity (RH) and variations in infection rates. Within a RH Goldilocks zone of 40%-60%, encapsulated viruses like influenza and SARS are optimally inactivated outside the infected host. Below 40% and above 80%, viruses can survive for extended periods in the air or on surfaces. This may explain in part the seasonality of infections as the indoor level of RH in winter is typically about 20% and above 40% in summer in temperate regions. However, the mechanism for the inactivation at midrange RH (in summer) is not well understood. This paper offers a hypothesis that could explain these observations. We have demonstrated that H2O2 and other reactive oxygen species (ROS) are formed spontaneously at the water-air interface of pure water microdroplets. Using only water and a nebulizing gas in the presence of oxygen, we have demonstrated the significant disinfectant potential of pure water microdroplets caused by the activity of H2O2 and other ROS. We postulate that spontaneous H2O2 and ROS formation in viruses containing exhaled microdroplets have a similar virucidal effect at mid-range RH. The droplet evaporation rate is sufficient to concentrate the solutes and provide enough time for reactions to occur at significantly higher rates than in bulk solutions. The concentration of H2O2 has also been shown to be positively correlated to RH. In addition, several other ROS/RNS may be present or formed through interactions with H2O2 that may act as even more effective virucide disinfectants to inactivate the virus. Below RH 40% evaporation happens too rapidly for these reactions to make an impact before the droplet is desiccated, and above RH 80% the solutes remain too diluted. Rapid inactivation of viruses at midrange RH may therefore play a greater role in preventing infections than physical dilution of virus load in the air through excessive mechanical ventilation. Similar to obesity, we suggest that a new paradigm that considers virus infectivity outside the host rather than the virus load balance in the air alone could greatly contribute to our understanding of respiratory infections. The proposed new “Relative Humidity Infectivity” RHI paradigm could explain the causal mechanisms underlying seasonal respiratory infections. This can point to better prevention strategies that avoid further distortion of our indoor environment and create conditions within which humans can thrive and be optimally protected. We need more focus on testing the various hypotheses and more data to determine which of the two paradigms will lead us in the right direction or how to use the best of both in an optimal combination. The stakes cannot be higher, and the potential for eradicating future viral respiratory pandemics with nature-based solutions may be right under our noses, literally.

A conserved strategy for inducing appendage regeneration in moon jellyfish, Drosophila, and mice

Can limb regeneration be induced? Few have pursued this question, and an evolutionarily conserved strategy has yet to emerge. This study reports a strategy for inducing regenerative response in appendages, which works across three species that span the animal phylogeny. In Cnidaria, the frequency of appendage regeneration in the moon jellyfish Aurelia was increased by feeding with the amino acid L-leucine and the growth hormone insulin. In insects, the same strategy induced tibia regeneration in adult Drosophila. Finally, in mammals, L-leucine and sucrose administration induced digit regeneration in adult mice, including dramatically from mid-phalangeal amputation. The conserved effect of L-leucine and insulin/sugar suggests a key role for energetic parameters in regeneration induction. The simplicity by which nutrient supplementation can induce appendage regeneration provides a testable hypothesis across animals.

Transcriptome-Wide Analysis of Human Liver Reveals Age-Related Differences in the Expression of Select Functional Gene Clusters and Evidence for a PPP1R10-Governed ‘Aging Cascade’

A transcriptome-wide analysis of human liver for demonstrating differences between young and old humans has not yet been performed. However, identifying major age-related alterations in hepatic gene expression may pinpoint ontogenetic shifts with important hepatic and systemic consequences, provide novel pharmacogenetic information, offer clues to efficiently counteract symptoms of old age, and improve the overarching understanding of individual decline. Next-generation sequencing (NGS) data analyzed by the Mann–Whitney nonparametric test and Ensemble Feature Selection (EFS) bioinformatics identified 44 transcripts among 60,617 total and 19,986 protein-encoding transcripts that significantly (p = 0.0003 to 0.0464) and strikingly (EFS score > 0.3:16 transcripts; EFS score > 0.2:28 transcripts) differ between young and old livers. Most of these age-related transcripts were assigned to the categories ‘regulome’, ‘inflammaging’, ‘regeneration’, and ‘pharmacogenes’. NGS results were confirmed by quantitative real-time polymerase chain reaction. Our results have important implications for the areas of ontogeny/aging and the age-dependent increase in major liver diseases. Finally, we present a broadly substantiated and testable hypothesis on a genetically governed ‘aging cascade’, wherein PPP1R10 acts as a putative ontogenetic master regulator, prominently flanked by IGFALS and DUSP1. This transcriptome-wide analysis of human liver offers potential clues towards developing safer and improved therapeutic interventions against major liver diseases and increased insights into key mechanisms underlying aging.

Bayesian Metamodeling of pancreatic islet architecture and functional dynamics

The pancreatic islet (islet of Langerhans) is a mini-organ comprising several thousand endocrine cells, functioning jointly to maintain normoglycemia. Cellular networks within an islet were shown to influence its function in health and disease, but there are major gaps in our quantitative understanding of such architecture-function relations. Comprehensive modeling of an islet architecture and function requires the integration of vast amounts of information obtained through different experimental and theoretical approaches. To address this challenge, our lab has recently developed Bayesian metamodeling, a general approach for modeling complex systems by integrating heterogeneous input models. Here, we further developed metamodeling and applied it to construct a metamodel of a pancreatic islet. The metamodel relates islet architecture and function by combining a Monte-Carlo model of architecture trained on islet imaging data; and an ordinary differential equations (ODEs) mathematical model of function trained on calcium imaging, hormone imaging, and electrophysiological data. These input models are converted to a standardized statistical representation relying on Probabilistic Graphical Models; coupled by modeling their mutual relations with the physical world; and finally, harmonized through backpropagation. We validate the metamodel using existing data and use it to derive a testable hypothesis regarding the functional effect of varying intercellular connections. Since metamodeling currently requires substantial expert intervention, we also develop an automation tool for converting models to PGMs (step I) using feedforward neural networks. This automation is a first step towards automating the entire metamodeling process, working towards collaborative science through sharing of expertise, resources, data, and models.

CALDERA: Finding all significant de Bruijn subgraphs for bacterial GWAS

Genome wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single nucleotide polymorphisms to mobile genetic elements. Since many bacterial species include genes that are not shared among all strains, this approach avoids the reliance on a common reference genome. However, the same gene can exist in slightly different versions across different strains, leading to diluted effects when trying to detect its association to a phenotype through k-mer based GWAS. Here we propose to overcome this by testing covariates built from closed connected subgraphs of the De Bruijn graph defined over genomic k-mers. These covariates are able to capture polymorphic genes as a single entity, improving k-mer based GWAS in terms of power and interpretability. As the number of subgraphs is exponential in the number of nodes in the DBG, a method naively testing all possible subgraphs would result in very low statistical power due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all closed connected subgraphs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. We illustrate this on both real and simulated datasets and also demonstrate how considering subgraphs leads to a more powerful and interpretable method. Our method integrates with existing visual tools to facilitate interpretation. We also provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_Recomb.

Useful paths for identifying Lean Six Sigma improvement opportunities

PurposeThe goal of this work is to clarify seven useful DMAIC Analyze phase options for developing process improvement opportunities required for successful projects.Design/methodology/approachUsing a scientific method problem solving structure, IO possibilities are shown to be predicted by rejecting a conceptual testable hypothesis.FindingsSeven analysis paths are identified that enable learners to develop multiple IO discovery strategies and to narrow tool selection options. Four benefit areas for identifying analysis paths are given: improved training, continuous improvement foundation, leadership support and framework clarification.Research limitations/implicationsAny starting list of analysis paths for developing IOs would be incomplete. The diversity of application experiences and tools will add to the current list.Practical implicationsLearners participating in LSS activities are aware of management's expectation that they will develop IOs to justify the LSS investment. Tool-focused training may leave some learners unclear about the multiple possible sources for IOs. Identifying useful analysis paths with associated tools for IO discovery will address any learner's Analyze phase uncertainty and facilitate expanded opportunities.Originality/valueAny successful LSS project must discover IOs to develop improvement actions. Clarifying IO discovery alternatives will encourage team brainstorming on Analyze phase investigative options. This framework identifying LSS improvement paths will assist practitioners in training and communicating with leadership and learners the range of approaches for developing improvement actions.