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شبكة الحياة : فهم علمي جديد للمنظومات الحية
يحاول فريتجوف كابرا في هذا الكتاب طرح تركيب شامل يدمج هذه الاكتشافات الجديدة في سياق واحد ويتيح للقارئ فهمها بأسلوب متسق، يرى كابرا أن الفهم الجديد للحياة ربما يكون طليعة التغير في النماذج الإرشادية والانتقال من النظرة الآلتية إلى العالم نحو نظرة إيكولوجية بدأت تتخلل نسيج البحوث العلمية وتمتد إلى الأوساط العامة.
BOOK
The influence of evolutionary history on human health and disease
Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.
Journal Article
النظرة النظمية إلى الحياة : رؤية موحدة
يتضمن هذا المفهوم الجديد للحياة نوعا جديدا من التفكير-التفكير من حيث العلاقات والأنماط والسياق. في العلم، تعرف طريقة التفكير هذه باسم التفكير النظمي» أو «التفكير المنظومي». ومن ثم، غالبا ما يجري تحديد فهم الحياة الذي تسترشد به من خلال العبارة التي اخترناها لعنوان هذا الكتاب : النظرة النظمية للحياة. يشمل الفهم العلمي الجديد للحياة العديد من المفاهيم والأفكار التي يجري تطويرها من قبل الباحثين المتميزين وفرقهم حول العالم. مع هذا الكتاب، نريد أن نقدم نصا متعدد التخصصات، يدمج هذه الأفكار والنماذج والنظريات في إطار واحد متماسك. نقدم رؤية منهجية موحدة تشمل الأبعاد البيولوجية والمعرفية والاجتماعية والبيئية للحياة، وتدمجها ؛ كما نناقش التداعيات الفلسفية والروحية والسياسية لرؤيتنا الموحدة للحياة.
BOOK
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks
Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used S sym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between S sym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.
Journal Article
الصلات المتبادلة الخفية : رؤية جديدة إلى الحياة
إن الحياة، من أبسط الخلايا البدائية حتى المجتمعات البشرية والشركات الكبرى والدول المتحدة، وصولاً إلى الإقتصاد العالمي، منظمة وفق المبادئ والأنماط الأساسية نفسها. المؤلف في هذا الكتاب يصف الأنظمة التي تنتج الأبعاد البيولوجية والمعرفية والإجتماعية للحياة، ويظهر أن بقاء الإنسانية مرتبط حتماً بفهم هذه الأنظمة. لهذا حين ينطوي القرن الجديد، سيؤثر في الإنسانية تطوران: الرأسمالية العالمية، وإعادة تشكيل المجتمعات النامية، مما سيؤدي حتماً إلى اتجاه تصادمي. تحدينا الكبير هو أن نغير بطانة قيمة النظام الإقتصادي العالمي قبل فوات الأوان، وكتاب الصلات المتبادلة الخفية سيظهر لنا كيف يتم ذلك بشكل واضح وعقلاني وواقعي.
Book
Quantifying the contribution of Neanderthal introgression to the heritability of complex traits
Eurasians have ~2% Neanderthal ancestry, but we lack a comprehensive understanding of the genome-wide influence of Neanderthal introgression on modern human diseases and traits. Here, we quantify the contribution of introgressed alleles to the heritability of more than 400 diverse traits. We show that genomic regions in which detectable Neanderthal ancestry remains are depleted of heritability for all traits considered, except those related to skin and hair. Introgressed variants themselves are also depleted for contributions to the heritability of most traits. However, introgressed variants shared across multiple Neanderthal populations are enriched for heritability and have consistent directions of effect on several traits with potential relevance to human adaptation to non-African environments, including hair and skin traits, autoimmunity, chronotype, bone density, lung capacity, and menopause age. Integrating our results, we propose a model in which selection against introgressed functional variation was the dominant trend (especially for cognitive traits); however, for a few traits, introgressed variants provided beneficial variation via uni-directional (e.g., lightening skin color) or bi-directional (e.g., modulating immune response) effects.
We lack a comprehensive understanding of how Neanderthal ancestry influences human traits. This study finds that regions with Neanderthal ancestry are broadly depleted of trait-associated variation; yet, introgressed variants likely contributed to human adaptation in a few traits, like skin color and immune response modulation.
Journal Article
الوصلات الخفية : تكامل الأبعاد البيولوجية والمعرفية والاجتماعية للحياة من أجل علم للاستدامة
يدور هذا الكتاب حول البشرية والبيئة المادية والحياتية التى تحتويها، ويبدأ بتصور علمي لنشأة الحياة على هذا الكوكب الذي كان خاليا تماما من أي أثر للحياة قبل أن يبرد ويتطور من الخلية إلى الحيوانات الراقية إلى الإنسان إلى طبيعة العقل والوعي، إلى نشوء اللغة والوحدة الاجتماعية، والتطورات التكنولوجية التي جاء بها العقل البشري، وعلى رأسها شبكات الاتصالات والتعاملات والأداء الوظيفي.
Book
Prediction of gene regulatory enhancers across species reveals evolutionarily conserved sequence properties
Genomic regions with gene regulatory enhancer activity turnover rapidly across mammals. In contrast, gene expression patterns and transcription factor binding preferences are largely conserved between mammalian species. Based on this conservation, we hypothesized that enhancers active in different mammals would exhibit conserved sequence patterns in spite of their different genomic locations. To investigate this hypothesis, we evaluated the extent to which sequence patterns that are predictive of enhancers in one species are predictive of enhancers in other mammalian species by training and testing two types of machine learning models. We trained support vector machine (SVM) and convolutional neural network (CNN) classifiers to distinguish enhancers defined by histone marks from the genomic background based on DNA sequence patterns in human, macaque, mouse, dog, cow, and opossum. The classifiers accurately identified many adult liver, developing limb, and developing brain enhancers, and the CNNs outperformed the SVMs. Furthermore, classifiers trained in one species and tested in another performed nearly as well as classifiers trained and tested on the same species. We observed similar cross-species conservation when applying the models to human and mouse enhancers validated in transgenic assays. This indicates that many short sequence patterns predictive of enhancers are largely conserved. The sequence patterns most predictive of enhancers in each species matched the binding motifs for a common set of TFs enriched for expression in relevant tissues, supporting the biological relevance of the learned features. Thus, despite the rapid change of active enhancer locations between mammals, cross-species enhancer prediction is often possible. Our results suggest that short sequence patterns encoding enhancer activity have been maintained across more than 180 million years of mammalian evolution.
Journal Article
The 3D mutational constraint on amino acid sites in the human proteome
Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation.
Here, Li et al. integrate population genetic and protein structural perspectives to map the landscape of 3D constraint on 80% of human proteins. They show that 3D mutational constraint is pervasive and strongly associated with functional relevance.
Journal Article
Learning and interpreting the gene regulatory grammar in a deep learning framework
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features they learn. Several models of how combinatorial binding of transcription factors, i.e. the regulatory grammar, drives enhancer activity have been proposed, ranging from the flexible TF billboard model to the stringent enhanceosome model. However, there is limited knowledge of the prevalence of these (or other) sequence architectures across enhancers. Here we perform several hypothesis-driven analyses to explore the ability of DNNs to learn the regulatory grammar of enhancers. We created synthetic datasets based on existing hypotheses about combinatorial transcription factor binding site (TFBS) patterns, including homotypic clusters, heterotypic clusters, and enhanceosomes, from real TF binding motifs from diverse TF families. We then trained deep residual neural networks (ResNets) to model the sequences under a range of scenarios that reflect real-world multi-label regulatory sequence prediction tasks. We developed a gradient-based unsupervised clustering method to extract the patterns learned by the ResNet models. We demonstrated that simulated regulatory grammars are best learned in the penultimate layer of the ResNets, and the proposed method can accurately retrieve the regulatory grammar even when there is heterogeneity in the enhancer categories and a large fraction of TFBS outside of the regulatory grammar. However, we also identify common scenarios where ResNets fail to learn simulated regulatory grammars. Finally, we applied the proposed method to mouse developmental enhancers and were able to identify the components of a known heterotypic TF cluster. Our results provide a framework for interpreting the regulatory rules learned by ResNets, and they demonstrate that the ability and efficiency of ResNets in learning the regulatory grammar depends on the nature of the prediction task.
Journal Article