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Phylodynamics for cell biologists

Science

Advances in experimental approaches for single-cell analysis allow in situ sequencing, genomic barcoding, and mapping of cell lineages within tissues and organisms. Large amounts of data have thus accumulated and present an analytical challenge. Stadler et al. recognized the need for conceptual and computational approaches to fully exploit these technological advances for the understanding of normal and disease states. The authors review ideas taken from phylodynamics of infectious disease and show how similar tree-building techniques can be applied to monitoring changes in somatic cell lineages for applications ranging from development and differentiation to cancer biology. Science , this issue p. [eaah6266][1] ### BACKGROUND The birth, death, and diversification of individuals are events that drive biological processes across all scales. This is true whether the individuals in question represent nucleic acids, cells, whole organisms, populations, or species. The ancestral relationships of individuals can be visualized as branching trees or phylogenies, which are long-established representations in the fields of evolution, ecology, and epidemiology. Molecular phylogenetics is the discipline concerned with the reconstruction of such trees from gene or genome sequence data. The shape and size of such phylogenies depend on the past birth and death processes that generated them, and in phylodynamics, mathematical models are used to infer and quantify the dynamical behavior of biological populations from ancestral relationships. New technological advances in genetics and cell biology have led to a growing body of data about the molecular state and ancestry of individual cells in multicellular organisms. Ideas from phylogenetics and phylodynamics are being applied to these data to investigate many questions in tissue formation and tumorigenesis. ### ADVANCES Trees offer a valuable framework for tracing cell division and change through time, beginning with individual ancestral stem cells or fertilized eggs and resulting in complex tissues, tumors, or whole organisms (see the figure). They also provide the basis for computational and statistical methods with which to analyze data from cell biology. Our Review explains how “tree-thinking” and phylodynamics can be beneficial to the interpretation of empirical data pertaining to the individual cells of multicellular organisms. We summarize some recent research questions in developmental and cancer biology and briefly introduce the new technologies that allow us to observe the spatiotemporal histories of cell division and change. We provide an overview of the various and sometimes confusing ways in which graphical models, based on or represented by trees, have been applied in cell biology. To provide conceptual clarity, we outline four distinct graphical representations of the history of cell division and differentiation in multicellular organisms. We highlight that cells from an organism cannot be always treated as statistically independent observations but instead are often correlated because of phylogenetic history, and we explain how this can cause difficulties when attempting to infer dynamical behavior from experimental single-cell data. We introduce simple ecological null models for cell populations and illustrate some potential pitfalls in hypothesis testing and the need for quantitative phylodynamic models that explicitly incorporate the dependencies caused by shared ancestry. ### OUTLOOK We expect the rapid growth in the number of cell-level phylogenies to continue, a trend enhanced by ongoing technological advances in cell lineage tracing, genomic barcoding, and in situ sequencing. In particular, we anticipate the generation of exciting datasets that combine phenotypic measurements for individual cells (such as through transcriptome sequencing) with high-resolution reconstructions of the ancestry of the sampled cells. These developments will offer new ways to study developmental, oncogenic, and immunological processes but will require new and appropriate conceptual and computational tools. We discuss how models from phylogenetics and phylodynamics will benefit the interpretation of the data sets generated in the foreseeable future and will aid the development of statistical tests that exploit, and are robust to, cell shared ancestry. We hope that our discussion will initiate the integration of cell-level phylodynamic approaches into experimental and theoretical studies of development, cancer, and immunology. We sketch out some of the theoretical advances that will be required to analyze complex spatiotemporal cell dynamics and encourage explorations of these new directions. Powerful new statistical and computational tools are essential if we are to exploit fully the wealth of new experimental data being generated in cell biology. ![Figure][2] Multicellular organisms develop from a single fertilized egg. The division, apoptosis, and differentiation of cells can be displayed in a development tree, with the fertilized egg being the root of the tree. The development of any particular tissue within an organism can be traced as a subtree of the full developmental tree. Subtrees that represent cancer tumors or B cell clones may exhibit rapid growth and genetic change. Here, we illustrate the developmental tree of a human and expand the subtree representing haematopoiesis (blood formation) in the bone marrow. Stem cells in the bone marrow differentiate, giving rise to the numerous blood cell types in humans. The structure of the tree that underlies haematopoiesis and the formation of all tissues is unclear. Phylogenetic and phylodynamic tools can help to describe and statistically explore questions about this cell differentiation process. Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data. [1]: /lookup/doi/10.1126/science.aah6266 [2]: pending:yes


SARS-CoV-2 spillover events

Science

Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 all broke out in recent decades and are caused by different strains of coronavirus (CoV). These viruses are considered to originate from bats and to have been transmitted to humans through intermediate hosts. SARS-CoV was identified in palm civets in wildlife markets and MERS-CoV in dromedary camels ([ 1 ][1]), but the direct source of the COVID-19 causative agent, SARS-CoV-2, is still undetermined. On page 172 of this issue, Oude Munnink et al. ([ 2 ][2]) report an in-depth investigation of SARS-CoV-2 infections in animals and humans working or living in 16 mink farms in the Netherlands. SARS-CoV-2 infections were detected in 66 out of 97 (68%) of the owners, workers, and their close contacts. Some people were infected with viral strains with an animal sequence signature, providing evidence of SARS-CoV-2 spillover back and forth between animals and humans within mink farms. Besides mink, multiple species of wild or domestic animals may also carry SARS-CoV-2 or its related viruses. Experimental infections and binding-affinity assays between the SARS-CoV-2 spike (a surface protein that mediates cell entry) and its receptor, angiotensin-converting enzyme II (ACE2), demonstrate that SARS-CoV-2 has a wide host range ([ 3 ][3]). After the SARS-CoV-2 outbreak, several groups reported SARS-related CoVs in horseshoe bats in China and in pangolins smuggled from South Asian countries, but according to genome sequence comparison, none are directly the progenitor virus of SARS-CoV-2 ([ 4 ][4]). Domestic cats and dogs, as well as tigers in zoos, have also been found to be naturally infected by SARS-CoV-2 from humans, but there is no evidence that they can infect humans, and so they are unlikely to be the source hosts of SARS-CoV-2 ([ 4 ][4], [ 5 ][5]). To date, SARS-CoV-2 infections in mink farms have been reported in eight countries (the Netherlands, Denmark, Spain, France, Sweden, Italy, the United States, and Greece), according to the World Organisation for Animal Health ([ 6 ][6]). In addition to animal-to-human transmission in farms, cold food supplier chains are raising substantial concern. In various cities in China, several small-scale COVID-19 outbreaks caused by virus-contaminated uncooked seafood or pork from overseas countries have been documented. It was found that viral genome signatures in these outbreaks were different from the viral strains present in China ([ 7 ][7], [ 8 ][8]). There is evidence that SARS-CoV-2 can survive up to 3 weeks in meat and on the surface of cold food packages without losing infectivity ([ 7 ][7], [ 8 ][8]). Thus, meat from SARS-CoV-2–infected animals or food packaging contaminated by SARS-CoV-2 could be a source of human infection (see the figure). This raises concerns about public health and agriculture in the prevention and control of SARS-CoV-2. Most SARS-CoV-2–infected animals do not display an obvious clinical syndrome, and infections would be unrecognized without routine diagnosis. The massive mink culling of infected farms is an efficient way to prevent further transmission of the virus. However, it cannot be applied to all domestic animals (if other species are found to be SARS-CoV-2 hosts). Thus, out of caution, extensive and strict quarantine measures should be implemented in all domestic farms with high-density animal populations. Because the virus is able to jump between some animals (such as mink) and humans, similar strategies should be applied to people in key occupations involving animal-human interfaces, such as animal farmers, zookeepers, or people who work in slaughterhouses. Notably, there is limited evidence of animal-to-human transmission of SARS-CoV-2 except for mink. Research on whether other domestic animals carry SARS-CoV-2, whether they can transmit it to humans, and factors related to spillover should be conducted. The RNA genome of SARS-CoV-2 seems relatively stable during transmission within human populations, although accumulated mutations have been detected. It is generally accepted that coronaviruses tend to exhibit rapid evolution when jumping to a different species. To keep the replication error rate low, coronaviruses encode several RNA-processing and proofreading enzymes that are thought to increase the fidelity of viral replication. However, viruses tend to have reduced fidelity in favor of adaptation to a new host species ([ 9 ][9]), although the mechanisms underlying this phenomenon are unclear. The coronaviral spike protein is prone to have more mutations because it is the first virus-host interaction protein and thus faces the strongest selection pressure. This molecular evolution can be observed in SARS-CoV genomes, which were under more adaptive pressure in the early stage of the epidemic (palm civet to human) than in later stages (human to human) ([ 10 ][10]). ![Figure][11] Possible SARS-CoV-2 transmission chains Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spillover likely occurred from bat and/or pangolin (ancestral virus) through unidentified intermediate host animals (direct progenitor virus). Human SARS-CoV-2 strains infect susceptible domestic animals (such as mink) and likely adapt to these species through mutation. The virus can be transmitted from mink back to farm workers and close contacts. SARS-CoV-2 can also be transmitted to humans through contact with contaminated uncooked meat or food packaging. GRAPHIC: MELISSA THOMAS BAUM/ SCIENCE Mutations that occur in SARS-CoV-2 in animals may increase its pathogenesis or transmissibility in humans. Five clusters of SARS-CoV-2 strains were found in mink, each characterized by a specific mink-related variant. In Denmark, the cluster 5 strain of mink SARS-CoV-2 was less immunogenic to COVID-19 patient serum than was human SARS-CoV-2 because of mutations of the spike proteins in the mink strains ([ 11 ][12]). This cluster 5 strain has infected at least 12 people, and the clinical presentation, severity, and transmission among those infected are similar to those of other circulating human SARS-CoV-2 strains ([ 12 ][13]). Currently, there is no evidence that any mutation from mink strains of SARS-CoV-2 escapes neutralization by antibodies designed to target the prevalent human strains. However, considering the possible risk of spillover of SARS-CoV-2 between humans and some animals, it is imperative to closely monitor mutations in the viral genome from infected animals and humans, particularly the genome regions affecting diagnostic tests, antiviral drugs, and vaccine development. It is anticipated that vaccines will allow control of COVID-19. Vaccines have been developed against the current prevalent viral strains and could face challenges if there is continued spillover from animals. The viral genome mutations likely produced during interspecies transmission between animals and humans raise concerns about whether the current vaccines can protect against emerging strains in the future. The extensive sequencing of viral genomes from animals and humans and worldwide data sharing will be central to efforts to monitor the key mutations that could affect vaccine efficacy. Laboratory-based studies should test whether the observed mutations affect key features of the virus, including pathogenesis, immunogenicity, and cross-neutralization. Moreover, preparedness of vaccines based on newly detected variants should be considered in advance. In the long term, vaccination of animals should also be considered to avoid economic losses in agriculture. There has been debate about whether bats or pangolins, which carry coronaviruses with genomes that are ∼90 to 96% similar to human SARS-CoV-2, were the animal source of the first human outbreak ([ 4 ][4]). Evolutionary analyses of viral genomes from bats and pangolins indicate that further adaptions, either in animal hosts or in humans, occurred before the virus caused the COVID-19 pandemic ([ 13 ][14]). Therefore, an animal species that has a high population density to allow natural selection and a competent ACE2 protein for SARS-CoV-2—mink, for example—would be a possible host of the direct progenitor of SARS-CoV-2. Another debate concerns the source of SARS-CoV-2 that caused the COVID-19 outbreak at the end of 2019. The current data question the animal origin of SARS-CoV-2 in the seafood market where the early cases were identified in Wuhan, China. Given the finding of SARS-CoV-2 on the surface of imported food packages, contact with contaminated uncooked food could be an important source of SARS-CoV-2 transmission ([ 8 ][8]). Recently, SARS-CoV-2 antibodies were found in human serum samples taken outside of China before the COVID-19 outbreak was detected ([ 14 ][15], [ 15 ][16]), which suggests that SARS-CoV-2 existed for some time before the first cases were described in Wuhan. Retrospective investigations of preoutbreak samples from mink or other susceptible animals, as well as humans, should be conducted to identify the hosts of the direct progenitor virus and to determine when the virus spilled over into humans. 1. [↵][17]1. J. Cui, 2. F. Li, 3. Z. L. Shi , Nat. Rev. Microbiol. 17, 181 (2019). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. B. B. Oude Munnink et al ., Science 371, 172 (2020). [OpenUrl][22] 3. [↵][23]1. L. Wu et al ., Cell Discov. 6, 68 (2020). [OpenUrl][24] 4. [↵][25]1. B. Hu, 2. H. Guo, 3. P. Zhou, 4. Z. L. Shi , Nat. Rev. Microbiol. 10.1038/s41579-020-00459-7 (2020). 5. [↵][26]1. D. McAloose et al ., mBio 11, e02220-20 (2020). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]World Organisation for Animal Health, COVID-19 Portal: Events in Animals (2020); [www.oie.int/en/scientific-expertise/specific-information-and-recommendations/questions-and-answers-on-2019novel-coronavirus/events-in-animals/][30]. 7. [↵][31]1. P. Liu et al ., Biosaf. Health 10.1016/j.bsheal.2020.11.003 (2020). 8. [↵][32]1. J. Han, 2. X. Zhang, 3. S. He, 4. P. Jia , Environ. Chem. Lett. 10.1007/s10311-020-01101-x (2020). 9. [↵][33]1. R. L. Graham, 2. R. S. Baric , J. Virol. 84, 3134 (2010). [OpenUrl][34][Abstract/FREE Full Text][35] 10. [↵][36]Chinese SARS Molecular Epidemiology Consortium, Science 303, 1666 (2004). [OpenUrl][37][Abstract/FREE Full Text][38] 11. [↵][39]European Centre for Disease Prevention and Control, “Rapid Risk Assessment: Detection of New SARS-CoV-2 Variants Related to Mink” (2020); [www.ecdc.europa.eu/sites/default/files/documents/RRA-SARS-CoV-2-in-mink-12-nov-2020.pdf][40]. 12. [↵][41]1. R. Lassauniere et al ., “SARS-CoV-2 spike mutations arising in Danish mink and their spread to humans” (2020); . 13. [↵][42]1. K. G. Andersen, 2. A. Rambaut, 3. W. I. Lipkin, 4. E. C. Holmes, 5. R. F. Garry , Nat. Med. 26, 450 (2020). [OpenUrl][43][CrossRef][44][PubMed][45] 14. [↵][46]1. G. Apolone et al ., Tumori J. 10.1177/0300891620974755 (2020). 15. [↵][47]1. S. V. Basavaraju et al ., Clin. Infect. Dis. ciaa1785 (2020). Acknowledgments: Supported by China National Science Foundation for Excellent Scholars award 81822028 (P.Z.) and Strategic Priority Research Program of the Chinese Academy of Sciences awards XDB29010101 (Z.-L.S.) and XDB29010204 (P.Z.). 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Personalized Nutrition Update issue nr. 36/20 - Leading the wave in Personalized nutrition & Wellness

#artificialintelligence

This review aims to present the current knowledge of how genetic fingerprints contribute to an individual's nutritional status. The results show: "Based on the outcomes of candidate genes, genome-wide-association studies, and meta-analyses thereof, we have identified several single nucleotide polymorphisms (SNPs) involved in the vitamins' metabolic pathways. Polymorphisms in genes encoding proteins involved in vitamin metabolism and transport are reported to have an impact on vitamin D status; while genetic variants of vitamin D receptors were most frequently associated with health outcomes. Genetic variations that can influence vitamin E status include SNPs involved in its uptake and transport, such as in SCAR-B1 gene, and in lipoprotein metabolism. Variants of the genes encoding the sodium-dependent vitamin C transport proteins are greatly associated with the body's status on vitamin C. Regarding the vitamins of the B-complex, special reference is made to the widely studied variant in the MTHFR gene".


How to build trust with Trusts on artificial intelligence

#artificialintelligence

Dr Venkat Reddy, consultant neurodevelopmental paediatrician, senior clinical adviser and AI lead at Future Perfect, discusses how AI-enabled analysis of healthcare data can both help clinicians, and encourage patients to be more engaged in their own care. In general, and as a clinician myself, I believe there is a lack of trust between clinicians and the use of AI. Aside from the few clinicians with an interest in clinical informatics and digital health, views are still largely shaped by newspaper headlines about killer robots. Unfortunately, there has been concern over the use of algorithms due to recent events. Not to mention the negative press about the use, or misuse, of AI by social media giants to gather information and'snoop on people'.


Amazon is cozying up in all corners of the healthcare ecosystem--AI is its next frontier

#artificialintelligence

Amazon Web Services (AWS) launched Amazon HealthLake--a new HIPAA-eligible platform that lets healthcare organizations seamlessly store, transform, and analyze data in the cloud. The platform standardizes unstructured clinical data (like clinical notes or imaging info) by in a way that makes it easily accessible and unlocks meaningful insights--an otherwise complex and error-prone process. For example, Amazon HealthLake can match patients to clinical trials, analyze population health trends, improve clinical decision-making, and optimize hospital operations. Amazon already has links in different parts of the healthcare ecosystem--now that it's taking on healthcare AI, smaller players like Nuance and Notable Health should be worried. Amazon has inroads in everything from pharmacy to care delivery: Amazon Pharmacy was built upon its partnerships with payers like Blue Cross Blue Shield and Horizon Healthcare Services, Amazon Care was expanded to all Amazon employees in Washington state this September, and it launched its Amazon Halo wearable in August.


Free Python Tutorial - Basic Python/Machine Learning in Bioinformatics

#artificialintelligence

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.


Artificial Intelligence, Machine Learning To Play an important Role In Fight Against COVID, Say Experts

#artificialintelligence

Artificial intelligence (AI) and machine learning are helping analyse enormous amounts of data around the human genome and drug molecules, and these new-age technologies can play an important role in the battle against COVID-19, industry experts said on Saturday. Speaking at KnowDis Machine Learning Day, Avantika Lal – Senior Scientist (Deep Learning and Genomics) at NVIDIA – stated bigger data sets on genome sequences (DNA arrangement) are being obtained, and this data is being studied for multiple parameters. "As the cost of sequencing goes down, more and more people can get their genome sequence and in actuality, governments, research institutes and public health organisations around the world are attempting to sequence many thousands of people so as to be develop an idea of the genomes of the inhabitants of the countries," she said. Lal added that enormous data sets are collected that are extremely complicated and contain many different related sorts of information. These data sets may also help understand the mechanisms by which a specific disorder arises in people, or how does one identify patients who may respond differently or become more sensitive to a particular kind of medication or treatment, she further said.


AI, machine learning to play key role in fight against Covid-19, say experts

#artificialintelligence

Artificial intelligence (AI) and machine learning are helping analyse massive amounts of data around the human genome and drug molecules, and these new-age technologies can play an important role in the fight against Covid-19, industry experts said on Saturday. Speaking at KnowDis Machine Learning Day, Avantika Lal - Senior Scientist (Deep Learning and Genomics) at NVIDIA - said larger data sets on genome sequences (DNA arrangement) are being acquired, and this data is being studied for multiple parameters. "As the cost of sequencing goes down, more and more people can get their genome sequence and in fact, governments, research institutes and public health organisations around the world are trying to sequence many thousands of people in order to be build up an idea of the genomes of the populations of their countries," she said. Lal added that massive data sets are collected that are very complicated and contain many different related kinds of information. "...the size and richness of the data sets that we're now getting in this field makes it really essential to use machine learning and deep learning to analyze this data in order to answer complicated questions like, for example, how do we identify people who are more at risk of developing various diseases before they actually develop signs of those diseases," she said.


AI, machine learning to play key role in fight against COVID, say experts

#artificialintelligence

Speaking at KnowDis Machine Learning Day, Avantika Lal - Senior Scientist (Deep Learning and Genomics) at NVIDIA - said larger data sets on genome sequences (DNA arrangement) are being acquired, and this data is being studied for multiple parameters. "As the cost of sequencing goes down, more and more people can get their genome sequence and in fact, governments, research institutes and public health organisations around the world are trying to sequence many thousands of people in order to be build up an idea of the genomes of the populations of their countries," she said. Lal added that massive data sets are collected that are very complicated and contain many different related kinds of information. "...the size and richness of the data sets that we''re now getting in this field makes it really essential to use machine learning and deep learning to analyze this data in order to answer complicated questions like, for example, how do we identify people who are more at risk of developing various diseases before they actually develop signs of those diseases," she said. These data sets can also help understand the mechanisms by which a certain disease arises in people, or how does one identify patients who might respond differently or be more sensitive to a particular kind of drug or treatment, she further said.


Align-gram : Rethinking the Skip-gram Model for Protein Sequence Analysis

arXiv.org Artificial Intelligence

Background: The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the `language of life', has been analyzed for a multitude of applications and inferences. Motivation: Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. Results: We propose a novel $k$-mer embedding scheme, Align-gram, which is capable of mapping the similar $k$-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.