What does characterization mean in a genomics context?

What does characterization mean in a genomics context?

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"The resulting libraries of gene sequences allow CDC and other laboratories to compare the genes of currently circulating influenza viruses with the genes of older influenza viruses and viruses used in vaccines. Through this process of comparing genetic sequences, called genetic characterization, CDC can make informed assumptions regarding… "

I'm unsure what genetic characterization means. What do the researchers actually here?

There's no technical definition for "genetic characterization". It is simply a phrase used by authors based on the normal meaning of the words: (1) having to do with genes and heredity, and (2) describing the essential character. Of course the bounds of the genetics and the essence of their nature are entirely in the eye of the author and reader, so no particular definition applies across all papers in genomics (or in other fields).

Review of the Department of Energy's Genomics: GTL Program (2006)


The Genomics: GTL program of the U.S. Department of Energy (DOE) is a fundamental research program to achieve a predictive understanding of microbial systems through systems biology. The goal is to build models of organisms and communities to predict their behavior under different environmental conditions on the basis of their genomes. The program has been funding microbial genomics projects relevant to DOE mission goals since 2002. DOE plans to expand the program and build infrastructure for it. On the basis of the Energy Basic and Applied Sciences Act of 2005, DOE asked the National Research Council to convene an ad hoc committee to review the plans for the Genomics: GTL program, specifically the facilities plans.

Charge to the Committee

The committee was asked to address the following questions:

Is the Genomics: GTL program, as currently designed, scientifically and technically well tailored to the challenges faced by the DOE in energy technology and development and environmental remediation?

Does the proposed Genomics: GTL research and facility investment strategy leverage DOE scientific and technical expertise in the most cost-effective, efficient, and scientifically optimal manner? Specifically, does the business model (i.e., number, scope, scale, order, and user operation plan) for the proposed

Genomics: GTL facilities follow directly from the science case&mdashshould one exist&mdashfor systems biology at DOE? Are there alternate models for some of the proposed effort that could more efficiently deliver the same scientific output?

In an era of flat or declining budgets, which aspects of the proposed Genomics: GTL program are the most meritorious? Which appear to have the highest ratio of scientific benefit to cost?

This report was prepared by the committee in response to that charge. To provide background information, the committee gives a brief introduction on genomics and the scientific advances that genomics has brought and describes DOE&rsquos role in genomics research and its Genomics: GTL program in Chapter 1. In Chapter 2, the committee examines the role that the Genomics: GTL program could play in achieving DOE&rsquos mission goals. The committee reviews the design of the program and its infrastructure plan in the last chapter.


Genomics is the study of the structure, content, and evolution of genomes and the analysis of the expression and function of genes and proteins at the level of the whole cell or organism (Gibson and Muse, 2002). Genomics has many subfields&mdashincluding functional genomics, structural genomics, proteomics, and metagenomics&mdashand it makes use of bioinformatics and other computational tools to study the global properties of genomes. Such genomic tools as high-throughput DNA sequencing, microarrays, and the polymerase chain reaction have revolutionized biomedical science. The first full genome sequence of a free-living organism, Haemophilus influenzae, was determined 10 years ago (Fleischmann et al., 1995). The process was expensive and took years to accomplish, but completion of the sequence established several important principles. It showed that the so-called shotgun assembly technique was workable and effective in sequencing whole genomes. And it became clear that our understanding of the genetic information in a microorganism was much less than expected&mdasha lesson still true 10 years later, when as much as 30 percent of the open reading frames of new microbial genomes are found to have unknown function.

Genome sequencing was quickly applied to microorganisms with larger and more complex genomes, including the yeasts Saccharomyces cerivisiae and Schizosaccharomyces pombe, and then to a series of model organisms, including the nematode, fruit fly, mustard, and mouse. With each new organism came a greater understanding of the organization and function of genomes and the identification of new genes and metabolic pathways. With the completion of the draft human genome sequence in 2003, the basis for rapidly understanding much of the genome information through comparative genomics was in place.

The sequencing of the human genome has provided detailed genetic information about specific genes and pathways in humans and has opened vast possi-

bilities for new therapies. For example, understanding of genetic changes associated with colon cancer has provided a specific basis for new cancer therapies and has been used to guide development of new drugs to treat resistant cases (Mount and Pandey, 2005), and cancer cells that are resistant to treatment can be classified on the basis of a specific gene sequence. Continuing work on the genomics of microbial species is also contributing to the improvement of human health. Scientists at Chiron Corporation, for example, used information from the sequencing of the bacterium Neisseria meningitis group B as the basis of a vaccine against this microorganism (Pizza et al., 2000). And current efforts to develop a vaccine for malaria, supported by the Bill and Melinda Gates Foundation, are based on interpreting genetic information on the malarial parasite (Gates Foundation, 2005).

As experience with sequencing has grown, its cost has fallen from $10 per base pair in 1990 (DOE, 2000), when it would have cost more than $30 billion to sequence the 3 billion base pairs of the human genome, to .001 per base pair in 2005, when the same sequence could be obtained at 1x coverage for about $3 million. The decrease in cost can be represented as a linear log curve and suggests a sequencing version of Moore&rsquos law of computing power. In this analogy, just as the complexity of an integrated circuit doubles about every 18 months, the cost of sequencing a base pair of DNA decreases by a factor of 10 roughly every 4 years. If that rate is sustained, sequencing the genome of an individual human for less than $1,000 may be possible within the next 15 years.

The time required to obtain a gene sequence is also falling rapidly. In 1989, Andre Goffeau set up a consortium to sequence the 12.5-million-base-pair genome of the budding yeast Saccharomyces cerevisiae. The successful effort involved 74 laboratories and took 7 years (Goffeau et al., 1996). Today, only 10 years later, the complete genome of a new strain of Saccharomyces can be sequenced by a single facility in less than a week, and smaller bacterial genomes can be sequenced in less than a day. In fact, the U.S. Department of Energy (DOE) Joint Genome Institute (JGI) is sequencing at a rate of more than 3 billion base pairs of DNA each month&mdashthe equivalent of 1x coverage of the human genome.

Other technologies are also revolutionizing genomic research. Microarray technology (also known as gene chips) allows the transcription level of most of the genes in an organism to be examined in a single experiment. A gene-chip experiment on budding yeast identified a previously uncharacterized gene, YDR533c, as being upregulated when the microorganism went into a quiescent state because of an accumulation of misfolded proteins (Trotter et al., 2002). The human homolog of that gene, DJ-1, was immediately identified in the human genome and was later shown to be a mutated autosomal recessive gene that affects early-onset Parkinson disease (Bonifati et al., 2003). (Parkinson disease is a protein-misfolding disorder that affects neurons, which are quiescent cells in the human body.)

The development of vast amounts of data about genomes and genetic potential defined a new approach in biomedical science of discovery science in contrast with the traditional hypothesis-driven approach. Discovery science aims to develop data resources with no specific vision as to the scientific questions to be approached. The idea is that vast data stores&mdashwhen properly collected, annotated, and stored in accessible databases&mdashare available for intense data mining by members of the scientific community who have specific hypothesis-driven questions. The various genome projects are considered discovery science, and this has proved to be a powerful scientific tool. Recently, the same approach has been extended to other &ldquo-omics&rdquo projects, the most notable being the proteomics projects that aim to define the entire protein library of a genome, including protein-protein interactions and posttranslational protein modifications. Likewise, the definition of all the metabolic pathways of a cell and their regulation (metabolomics) has begun to be an active research approach. The collection of massive data stores in -omics projects is one step in a complex &ldquosystems biology&rdquo approach to science. But although genome sequencing has proved to be a highly effective tool for gaining biological understanding, the other -omics tools have been less immediately productive thus far, because of the biological complexity of cells. Therefore, the complexity of biological systems beyond the information content of DNA&mdashfor example, proteins, metabolites, and molecular interactions, many of which are manifest only under specific developmental or environmental conditions&mdashis not well understood.

To quote David Galas in a commentary in Science (Galas, 2001):

As simple as it sounds, to know that there are no other unknown genetic components that can provide alternative explanations of experimental results is a fundamental shift of perspective. This shift is beginning to transform our approach to science, enabling researchers to face the challenge of identifying all the molecular components of the cell, as well as understanding how they are controlled, interact, and function. From a picture of the &ldquosoftware&rdquo of the single cell, we can look to the future when researchers will begin building, with as fine a degree of resolution, an integrated view of the universe of cell-cell interactions, differentiation, and development from single cell to organism. The availability of complete sequences of Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana is already beginning to revolutionize such studies, and this list may soon include significant sequences from other biological models of metazoan development.


The U.S. federal system of support for science contains no central department or ministry for science. Mission-oriented research and development (R&D)

programs in defense, health, energy, environment, space and aeronautics, oceans and atmosphere, agriculture, transportation, and other fields are, instead, supported by a diverse array of agencies and departments. This pluralistic system of support is regarded as a great strength of the U.S. system and as something to be maintained and safeguarded (NRC, 1995). Under this system, allocation of funding for science is handled mainly by agencies that understand the purpose and content of R&D programs and the value of their results.

DOE is charged with promoting scientific and technological innovation in support of its overarching mission to advance the national, economic, and energy security of the United States (DOE, 2005a). As noted by Martha Krebs, former director of the Office of Energy Research (DOE and NRC, 1998) &ldquoDOE is a science agency and &hellip our science enables us to meet the energy challenges ahead. All too often, DOE is the forgotten science agency, despite its ranking among the top federal supporters of basic, applied, academic, and overall research.&rdquo

Many observers (for example, Kenneth I. Shine in DOE and NRC, 1998) have remarked that while the 20th century was the century of physics and astronomy, the 21st century will be the century of biology in all its ramifications. DOE&rsquos contributions to the life sciences began with health physics and radiation biology but expanded into many other fields of health and environmental research relevant to its missions. Today, DOE&rsquos participation in the pluralistic system of federal research funding means that some non-health-related life-science fields that are unfunded or underfunded by other agencies have become central and essential to DOE&rsquos science portfolio, for example, research in many fields of environmental biology, as typified by the Genomics: GTL program.

DOE has played a critical role in the development of genomics research. Under the leadership of Charles DeLisi, it initiated discussion of the Human Genome Project (HGP) in 1986. Scientists at the DOE national laboratories recognized that their long-term studies of radiation-induced mutation could be fully understood only in the context of the genetic variation that existed normally in the world&rsquos human populations. Therefore, DOE provided $5.3 million to initiate the HGP at its national laboratories. The National Institutes of Health joined DOE in the HGP in 1988 because it recognized that genomic tools could be important in understanding human genetic disorders. DOE, through efforts at Los Alamos National Laboratory (LANL), had been engaged in early DNA sequence analysis. The Genbank DNA sequence database, now operated by the National Center for Biotechnology Information at the National Library of Medicine, began as a project of Walter Goad at LANL. Many of the important tools for sequence analysis (for example, the Smith-Waterman analysis algorithm) were also developed as projects at LANL. Because of the interdisciplinary culture of the national laboratories, pioneering projects of this type were able to flourish.

Applications of Genomics at the Department of Energy

In addition to the HGP, DOE invested in other programs and facilities for genomics. In 1994, DOE began its microbial genome program. In 1996, it established JGI in Walnut Creek, California, to integrate work based at the three major DOE human genome centers. After completion of the HGP, JGI refocused its mission to align with three of DOE&rsquos primary missions: energy production, carbon management, and bioremediation. JGI&rsquos massive sequencing capabilities have served the DOE microbial genome program by sequencing the entire genomes of many microorganisms. In addition, JGI began the Community Sequencing Program, which solicits genome sequencing proposals for organisms that are relevant to DOE missions, and other organisms important to other community dynamics. In 2005, 23 projects executed by JGI will have produced complete draft sequences of genomes of diverse organisms, including plants, insects, and fishes. JGI can be characterized as a production facility that serves a broad community of scientists by providing sequence information on diverse organisms, and it has become one of the largest such facilities in the world. Development of new technology is part of the mission of JGI, and it has resulted in remarkable reductions in the time needed to obtain sequence information.

Over 50 years of nuclear-weapons research and production in the United States at DOE sites has resulted in radionuclide, metal, and organic-chemical contamination that is difficult and expensive to remove with physical decontamination methods. Microorganisms offer a biological alternative to cleaning up DOE wastes. DOE&rsquos Natural and Accelerated Bioremediation Research (NABIR) program, established in 1995, funds research aimed at providing solutions to bioremediation of contaminants in the subsurface at DOE sites. However, not all NABIR projects depend on genomics they also involve molecular biology, microbial physiology, geochemistry, microbial ecology, and mathematical modeling. Research supported by other DOE programs on microbial systems has resulted in sequencing of microorganisms that are important in decontamination, such as geobacters, Shewanella oneidensis, and Desulfovibrio vulgaris (Heidelberg et al., 2002 Methé et al., 2003 Heidelberg et al., 2004). A number of projects use genome-based information on those important microorganisms to elucidate metabolic pathways and their interactions with other members of their ecological community. DOE is also participating in an interagency program in phytoremediation research that supports basic science much of this work focuses on understanding molecular mechanisms of remediation of metals or organic materials by plants.

Burning fossil fuels has increased the concentration of atmospheric carbon dioxide (CO2), a heat-trapping greenhouse gas, from the preindustrial 280 ppm to about 375 ppm today (EEA, 2004). Projections are that concentrations will more than double over the next 50 years unless emissions are reduced (IPCC, 2001). Because marine and terrestrial ecosystems play major roles in global carbon

cycling, knowledge of the key feedbacks and sensitivities of those systems are necessary to devise carbon sequestration strategies and alternative response strategies. A current example of DOE carbon-cycle management research is the work of a team of researchers at the Oak Ridge, Pacific Northwest, Argonne, and Sandia National Laboratories, and the University of North Carolina at Chapel Hill. The team is investigating cellular function in Rhodopseudomonas palustris, a metabolically versatile bacterium that converts CO2 into cell material and nitrogen into NH3, and produce hydrogen. In parallel, a team of researchers at Harvard, the Massachusetts Institute of Technology, Brigham and Women&rsquos Hospital (in Boston, Mass.), and Massachusetts General Hospital is studying proteins, protein-protein interactions, and gene regulatory networks of Prochlorococcus marinus, a marine cyanobacterium that is important in global photosynthesis. The group is taking a systems approach to understanding the metabolic activity of this microorganism under various environmental conditions.

Charged with securing the nation&rsquos energy supply, DOE&rsquos Office of Energy Efficiency and Renewable Energy (EERE) has a Biomass Program and a Hydrogen, Fuel Cells, and Infrastructure Technologies Program, both of which substantially involved the National Renewable Energy Laboratory. The Biomass Program aims to develop advanced technologies that transform biomass into biofuels, biopower, and high-value bioproducts (DOE-EERE, 2005a). The hydrogen program supports research on and development of low-cost, highly efficient technologies to produce hydrogen from diverse domestic sources (DOE-EERE, 2005b). Both programs fund research on genomics, but their primary focus is on applied science, so they could benefit from complementary fundamental research aimed at elucidating biological mechanisms.

Current and planned DOE research programs strive to strike a balance between discovery science, exemplified by genomics, and hypothesis-driven science, often identified with single-investigator projects. The benefits of the hybrid approach in subjects related to the DOE mission are apparent in the development of metagenomics. Microbial metagenomics involves the analysis of DNA obtained en masse from environmental samples (Handelsman, 2005a). In a sense, it is &ldquoreverse genomics&rdquo in that the structure or function of individual genomes or genes is deduced from complex mixtures of microbial consortia rather than with the classical purify-first, characterize-second approach. Metagenomics can be divided into two general categories: (1) shotgun sequencing and assembly of environmental DNA (Tringe and Rubin, 2005), typically resulting in fragmentary genome assemblies of the most abundant organisms, and (2) functional analysis of cloned DNA fragments to determine biochemical properties of interest in heterologous systems (for example, Daniels, 2005). Using metagenomics methods, scientists can study the multitude of species in an environmental system without having to culture the organisms under study. Metagenomics constitutes a huge advance over culture-dependent methods because it allows a glimpse into the nature of organisms that are inaccessible by more traditional methods.

Metagenomic analysis has given new insights for our understanding of genetic diversity in a number of environments, notably the world&rsquos oceans, estuaries, and soil communities (Tringe et al., 2005 Venter et al., 2004).

Using Systems Biology to Find Solutions for Carbon Sequestration, Environmental Remediation, and Energy Security

Although scientists often gain insight into microorganisms or microbial processes one at a time, such studies, even when pieced together, do not provide a global picture of how a biological system works. The lack of knowledge of how microbial systems work hinders our ability to harness microbial processes for bioremediation, carbon sequestration, and bioenergy production (Box 1-1). Systems biology has been defined by Ideker et al. (2001) as an approach to studying &ldquobiological systems by systematically perturbing them (biologically, genetically, or chemically) monitoring the gene, protein, and informational

BOX 1-1
Cost and Benefit of Understanding the Systems Biology of an Organism in Bioengineering

Obtaining an understanding of the systems biology of an organism or community of organisms may seem complex, but the cost of ignorance can be enormous. DuPont, in collaboration with Genencor International, recently succeeded in engineering the common bacterium Escherichia coli to produce 1,3-propanediol (PDO), a chemical building block for the new fabric Sorona (also called 3GT), which is softer and more stretchable than polyester. Chemical and biological approaches to make PDO were already known when the project began, but they were not well suited for industrial-scale production, because they were energy-intensive and required expensive starting materials. Thus, there was a need to develop a new process that would use one microorganism with the ability to convert an inexpensive basic carbon source into the desired PDO product. Such a microorganism did not exist, so one was created by inserting genes that code for enzymes that catalyze the missing chemical steps into an easily grown bacterium. The metabolic-pathway engineering could have involved, in theory, the insertion of only four foreign genes, from the bacterium Klebsiella pneumoniae and the yeast Saccharomyces cerevisiae, into E. coli to enable it to make PDO from glucose. However, because scientists did not have a systems biology understanding of how E. coli would respond to the introduction of the new enzyme activities into its metabolic systems, achieving efficient &ldquogreen&rdquo production of PDO actually required modification of more than 70 different genes. Most of the modified genes were from the host organism and were needed to fine-tune critical pathways, eliminate undesired enzymes, and carefully deregulate ancillary metabolic systems in E. coli (Sanford, 2004). The entire process took a team of 40 people more than 7 years.

pathway responses integrating these data and ultimately, formulating mathematical models that describe the structure of the system and its responses to individual perturbations.&rdquo Systems biology uses comparative, high-throughput assays, and mathematical or computational models to generate a picture of systemwide activities. That approach can be applied to studying systems at the subcellular level (multiprotein metabolic processes), the cellular level (integration of various functions within a cell), and the community level (interactions within multispecies communities).

Systems biology focuses on the challenge of understanding at high resolution the interlocking metabolic and molecular context for physiological activity and responses to environmental conditions. Systems biology will realize its full potential only when the properties of individual components are tied to variations at the system level. The recent emergence of synthetic biology (see Box 1-2) also provides a new and powerful approach to understanding biological systems. Synthetic biology combines knowledge from various disciplines&mdashincluding molecular biology, mathematics, engineering, and physics&mdashto develop new cellular components that are based on fundamental design concepts and that will lead to new cellular behaviors. The emerging field of synthetic biology will provide fundamental insights into cellular systems, improve our understanding of natural phenomena, and promote the development of a new engineering discipline focusing on the design and development of complex cell behaviors with predictable and reliable properties.

Using the two complementary approaches to study microorganisms and microbial communities to understand their structure and function, predict their behavior accurately, and manipulate them for desired functions is the key theme of DOE&rsquos Genomics: GTL program. The program seeks to combine discovery science with hypothesis-driven research so that an investigator with a well-formulated research question can mobilize the resources of a high-throughput facility to obtain large amounts of data on genes, gene regulation, gene products, and protein-protein interactions.


The Genomics: GTL program was conceptualized in 2000 after Martha Krebs, director of DOE&rsquos Office of Science (formerly Office of Energy Research), charged DOE&rsquos Biological and Environmental Research Advisory Committee (BERAC) to define the agency&rsquos potential scientific roles after the HGP was completed. In response to its charge, BERAC prepared the report Bringing Genomes to Life (BERAC, 2000), which formed the basis of the first roadmap, &ldquoGenomes to Life,&rdquo prepared by the Human Genome Management Information System at the Oak Ridge National Laboratory (ORNL) in April 2001 (Table 1-1). That first roadmap argued that the availability of genomic sequences of entire organisms would enable us to gain &ldquoa new, comprehensive, and profound under-

Population Structure of Pathogenic Bacteria

2.2 Heterogeneity in Recombination

Population genomic studies of hundreds and even thousands of bacterial pathogens reveal a remarkable level of variation in recombination rates and patterns across very closely related lineages. Hence, this variation adds another layer of complexity that may conflict with blanket definitions of bacterial species and populations. Moreover, this variation may be associated with certain clinically relevant phenotypes, such as antibiotic resistance. In the pneumococcus, for example, hyper-recombinant populations have been reported to exhibit higher levels of resistance to multiple antibiotics. 31 In Acinetobacter baumannii, transformation experiments indicate that multidrug resistance (MDR) evolved faster in recombining, functionally diverse populations within only a few generations compared to nonrecombining populations. 32 The authors also show that the average fitness of MDR genotypes and their spread depended on whether they arose by mutation or recombination.

Recombination rates can vary significantly within a species. The genetically promiscuous pneumococcus exhibits a dramatic range in recombination rates. Early studies using MLST detected differences in r/m between capsular serotypes of the same serogroup. 33 Surveys using genome sequences from numerous serotypes of pneumococci have estimated a range of r/m values, from 0.06 to 34.06. 34 Other bacterial pathogens also exhibit such variation. The causative agent of listeriosis, a serious infection caused by eating food contaminated with Listeria monocytogenes, consists of at least four evolutionary lineages. Higher recombination rates are found in lineage II strains, which are widespread in natural and farm environments, and are also commonly isolated from animal listeriosis cases. The higher recombination rates in lineage II may contribute to its adaptation to diverse environments and hosts. In contrast, lineage I, the predominant cause of human listeriosis outbreaks, is largely clonal. 35,36

Different strains also vary in terms of how often they donate or receive recombined DNA, and this may greatly influence the population structure and dynamics of pathogens. These biases, wherein some lineages act as frequent donors while others prefer to receive DNA more often, can create highways of gene exchange. In the pneumococcus, certain sporadically occurring, nonencapsulated lineages (i.e., ST1106) appear to recombine more often than others, potentially forming a hub for gene flow that is an important source of genetic diversity for the wider population. 37 This characteristic is not simply due to the absence of a polysaccharide capsule, which may act as a physical barrier to the entry of exogenous DNA, because other nonencapsulated lineages (i.e., ST344 and ST448) show no significant difference in r/m compared to encapsulated lineages. 34,38 In the emerging opportunistic pathogen Mycobacterium abscessus, which causes lung diseases in immune-compromised individuals, asymmetrical gene flow also occurs among three subspecies subspecies Mycobacterium bolletii appears to donate more often to Mycobacterium massiliense than the other way around. 39 In the human skin commensal and opportunistic pathogen S. epidermidis, one population (i.e., genetic cluster 3) appears to receive DNA from all other clusters but does not donate DNA to these other clusters. 23

Recombination also does not occur at constant rates across the genome, as “recombination hot spots” are consistently found. In the pneumococcus, the common hot spots contain genes encoding cell surface antigens, such as pneumococcal surface protein A (pspA) and pneumococcal surface protein C (pspC), as well as antibiotic resistance, such as penicillin-binding proteins (pbp2x, pbp1a, pbp2b) and dihydrofolate reductase (folA). 37 These hot spots are likely driven by selective pressure due to host immunity and clinical intervention. 37 In C. jejuni genomes, three recombination hot spots have been identified. 40 More than half of the genes in these hot spots are related to membrane proteins that are crucial for host interaction, colonization, and adhesion to intestinal epithelial cells, and are likely under diversifying selection as a response to the host immune system.

In S. aureus, recombination hot spots appearing over a megabase scale have been linked to large chromosomal replacements that have occurred around the origin of replication (e.g., Refs. 18,41 ), and hot spots appearing over a kilobase scale occur at insertion sites of mobile genetic elements. 42 The size of DNA fragments being recombined also varies dramatically in the pneumococcus, with two main types identified: (1) micro-recombination, which involves the frequent replacement of single, short DNA fragments with mean size of 27–580 bp and (2) macro-recombination, though rarer, involves the acquisition of multiple, long fragments with mean size 8800–14,000 bp and is associated with major phenotypic changes. 43 While the mechanism underlying these different import size distributions is unknown, it has been hypothesized to be driven by the saturation of the mismatch repair system. 43

How Do You Fight Antibiotic Resistance? Ask a Virus.

In a paper published today in PLoS Biology, IGI researchers Vivek Mutalik of Lawrence Berkeley National Lab and Adam Arkin of UC Berkeley report advances in understanding phage biology and phage resistance that pave the way towards using these small predators to fight antibiotic-resistant bacteria. In September, Mutalik talked with IGI science writer Hope Henderson about this work.

What does your lab study?

We mainly study synthetic biology and functional genomics of microbes. Primarily, we develop tools to study bacteria, look at them in the microbiome context, and get them to do things. Along the way, I&rsquove become fascinated by bacteriophages.

Who&rsquos not excited about phages? They&rsquore the coolest things on earth &mdash and we don&rsquot understand them.

LBNL Research Scientist Vivek Mutalik.

They are the &ldquoknown unknowns&rdquo in the microbial world. We want to learn more about phages, how they infect bacteria and how bacteria fight back, and ultimately engineer them to kill specific bacteria for applications in human health and agriculture.

What is a phage?

Bacteriophages, or phages for short, are bacterial predators. They are viruses that infect bacteria, and use bacteria to grow and multiply. Phages are found everywhere &mdash they are the most abundant biological entities on earth! Each type of phage has a unique genome, size, shape, and infectivity cycle, of which we have very limited knowledge. But we know that phages are very precise killing machines that each infect specific bacteria. They are not like antibiotics in that when you add antibiotics, a big class of bacteria will be killed. We know that the tail at the bottom of (most) phages determines which bacteria they infect, but we don&rsquot understand exactly how. In addition, even though we are getting better as sequencing phages, we don&rsquot have a good handle on what these phage genomes code for. Essentially, we know who&rsquos there but don&rsquot know what they&rsquore doing.

The ultimate goal is to understand enough phage biology that we can develop approaches for targeted elimination of pathogenic bacteria. Studying phage resistance gives us a window into their inner workings.

What can you do with engineered phages?

The application space is huge: we can use engineered phages with specific &ldquoroles&rdquo in human health, agriculture, food, meat, aquaculture industry, environmental remediation, and more. If we deeply understand how phages work, we can engineer them to eliminate one particular strain of bacteria in the gut microbiome to help treat an infection, or diabetes, or kill a bacterial pathogen that&rsquos attacking tomato crops.

What does &ldquophage resistance&rdquo mean and what does it have to do with engineering phages?

You can think of it as similar to antibiotic resistance. When you add antibiotics to bacteria, they don&rsquot like it! They come up with methods to survive and adapt to the conditions. When you add phages to bacteria, the same thing happens &mdash they go through what we call a selection process. There will be a few bacteria with certain genetic changes that allow them to survive the phages. In other words, the host bacterial population mutates, and now the phage can&rsquot infect them. That&rsquos phage resistance. Studying phage resistance is a way to study the fundamentals of how phages infect specific bacteria. If the changed gene is, say, part of a receptor on the bacteria&rsquos surface, then we know the phage interacts with that part of the receptor to get into the bacteria.

Vivek Mutalik in lab.

What did you find in this paper?

We used well-studied phages and E. coli, a lab bacteria, to look at phage resistance. Because it&rsquos a known system, we could make sure our platform is effective by recapitulating what has already been found, and we could identify new things.

We took two strains of E. coli bacteria and built libraries of mutations where genes have increased function, loss of function, or are functionally deleted. These libraries are in a test tube or in a flask, and each one has many different mutants in it. Each mutation has a barcode, so to speak, that lets us know what gene is mutated. So then you add a phage. Most of the bacteria are killed, but a few survive and multiply. We collect the survivors and study, why are they surviving? We can use the barcode to tell what mutations they have. The genes we uncover are essential for the phage infection to process. So, I can start to really understand the landscape of phage resistance.

We did that with 14 phages in the paper. Some of these are very well studied, while some are novel phages. Using this system, we could capture almost all of the data on phage resistance that has been generated over 50 years. In other words, the genes we already know are important came up as hits. That lets us know our system is really working and robust. This has huge implications for understanding how bacteria responds to phages and developing ways to engineer phages! The premise of our proposal to the IGI was to show that our platform works for E. coli, and then scale it up to use with a couple of pathogenic bacteria. So now I&rsquom continuing that work with other human and plant pathogens. It&rsquos really exciting to compare these resistance mechanisms across different phages and different pathogens!

Does your work have implications for COVID-19?

Absolutely. Researchers have found that more than 50% of patients hospitalized for COVID-19 also have bacterial co-infections, like bacterial pneumonia. These patients are getting antibiotics, but they don&rsquot always actually kill the bacteria or help the patient &mdash that&rsquos antibiotic resistance. We aren&rsquot giving phages to these patients yet, but they could be part of the arsenal at some point. COVID-19 has shown us that we are just so unprepared for tackling infectious diseases. This is also very important from the national biosecurity point of view especially as we are clearly not prepared to handle any accidental (or intentional) release of antibiotic resistant pathogens into our food processing, water treatment or our environment. Our national strategy seems to be more reactive than proactive. The antibiotic resistance threat has been looming in front of us for so long, but there are neither new antibiotics in the pipeline nor any new solutions being developed. We need a holistic, proactive approach to solve antibiotic resistance, and phages could be a key component of it. I&rsquove never felt so connected to an application space ever before. I feel very driven.

Whenever I isolate a phage it is with the hope that it is going to save somebody&rsquos life. Maybe not now, but someday. That&rsquos a very satisfying feeling.

Is there anything else you&rsquod like to say?

It was a risky project that is tough to get a funding agency to invest in, and I&rsquom happy that IGI recognized this and took the risk. By investing in this project, IGI has established the expertise, tools and resources needed to do phage characterization and engineering.

I want to say thank you to my team. Specifically, working with postdoc fellow Denish Piya, and graduate scholars Benjamin Adler and Harneet Rishi was super fun. I applied for this IGI grant with Adam Arkin and Adam Deutschbauer as co-PIs &mdash it&rsquos an awesome team to work with these pioneers in functional genomics and synthetic biology! And then having Britt Koskella and Kim Seed on campus, Richard Calender next door, working next to Jennifer Doudna&rsquos lab &mdash it&rsquos just a dream come true.

Vivek Mutalik and lab members Sean Carim (left) and Trenton Owens (right) look at a research specimen.

By Hope Henderson


The plant miRNAs differ from animal miRNAs in several aspects, mainly in the hairpin length and in the nature of complementarity with the star sequence [2]. Although the length of mature sequences largely remains the same, the length of the loop region differs substantially in plants, owing to their recent evolution [19]. These differences strongly influence the statistical features used for their prediction.

The minimum free energy (MFE), a commonly used measure for characterizing secondary structure of different types of RNA [20, 21], is also being used for characterization and/or prediction of miRNAs [3, 4, 22–24]. For screening miRNA candidates, the majority of previous studies have either used a fixed MFE threshold (for example, -18 kcal/mol) [13], or a variable threshold [25]. The miRDeep, however, involves comparison of (posterior probabilities of) MFE of real and background hairpins, enabling a more robust discrimination between them. This comparison in plants, however, did not prove to be so straightforward, as diversity in miRNA hairpin length results in multiple distributions of MFE.

Although, the effect of hairpin length on MFE in plants has been reported earlier [25], these reports did not give a systematic evaluation of potential impacts of this relationship on prediction of plant miRNAs. In the present study, we observed that the MFE distributions become length-free by normalizing the MFE of precursor with its length, which renders the MFE of hairpins, of different length, comparable.

We further observed only minor differences between the MFE distributions of real and background precursors of comparable length. This suggested that MFE alone may not be a good discriminator between real and background miRNAs, and more weight should be placed on other measures besides MFE. These findings however do not hold for dicot species, as they do show substantial differences between real and background, rendering MFE a more important discriminating feature.

Differences between the secondary structures of candidate precursors and their shuffled counterparts is another important feature exploited for miRNA prediction [9]. The real precursors generally display substantial difference in the nature of folding (as well as in MFE) from their shuffled counterparts [16]. This difference is quantified by the p-value, which is the fraction of shuffled sequences with MFE lower than original precursors a candidate with p-value ≤0.05 can be statistically considered as stable. Although, plant and animal miRNA precursors, of the same length, are expected to have a similar frequency of stable precursors, due to the diversity in length of plant miRNA precursors, the parameter estimated for animals becomes inapplicable to plants. In the latter case, while p-values of real precursors remains almost the same even with increased length, that of background precursors declined substantially with length. These criteria become more effective when it comes to predicting longer candidate precursors, which is often the case with plants.

Furthermore, the conservation of mature miRNAs is yet another important feature for miRNA discovery, exploited by miRDeep and several other tools, where the former takes into account the conservation in the nucleus region of mature miRNAs [9]. The positions which constitute the nucleus in animals are 7-8 nt in length, starting from position 2 [27–29], whereas in plants, there is near-perfect complementarity all along its length. We examined the nature of the positional conservation pattern in plants, and found a relatively longer conserved motif, wherein two conservation blocks were apparent: positions 2-13, and 16-19, with position 4 completely conserved. Implementation of positional conservation pattern in plants has improved the specificity of miRNA homolog prediction.

Since plant miRNA precursors show a relatively broader distribution of length compared to animals, this in turn, necessitates a different choice of excision length(s) for candidate prediction. While Sunkar et al. [13] considered 200 nt as a threshold at which 90% of the real precursors in rice were covered, Jones-Rhoades et al. [30] took a higher precursor length (500 nt) for prediction in A. thalina/O. sativa. In another study by Adai et al.[25], in A. thaliana again, the maximum precursor size was set to 400 nt. Based on the distribution of length of plant miRNA precursors from miRBase database (release 14), length(s) which covered the maximum number of miRNAs, and at the same time, had an adequate number of precursors (30, for instance, which have properties of a normally distributed population) for parameter estimation, were chosen. We observed two thresholds satisfying the above constraints, 276 and 336, covering 96% and 98% of the population, respectively.

To show how much the new parameterization improves the prediction accuracy, we used the miRDeep with the default parameters to predict miRNA candidates. Results suggested that a major fraction of miRNAs, predicted using the default parameters, did not match with experimentally identified miRNAs. The observed values of key features, such as number of total mismatches, bulges, nucleus conservation, excision length, etc., of the predicted candidates were atypical for plant miRNAs. Moreover, shorter nucleus size in default miRDeep led to identification of several false miRNA homologs. However, prediction using new parameters on the same dataset showed very high prediction accuracy, with good sensitivity and even better specificity.

Notably, any proposed improvement in plant miRNA discovery must meet the criteria laid out for miRNA annotation in plants [12, 31]. Despite the parameter adjustments in the miRDeep algorithm, the primary criterion for miRNA annotation, namely precise excision of mature miRNA from the stem of a stem-loop precursor, is implemented faithfully. The parameterization doesn't interfere in miRDeep's core method. Further, two of the miRDeep's statistical features, namely characterization of stem-loop and mapping of reads onto precursors, are enough to prevent a siRNA being misclassified as miRNA. Besides, there have been recent reports of few plant miRNAs being processed by riboendonucleases other than DCL1 [32], therefore, the predictive methods should also be capable of their identification. This however does not pose much problem to the tools based on deep-sequencing reads, as their methods are guided primarily by the sequences. So, a DCL3 processed miRNA, for instance, will be analyzed just like the DCL1 generated miRNAs, despite the longer product size of the former. Furthermore, there have been rare reports of multi-functional stem-loops [12], which poses challenges to the tools available for miRNA discovery. We are skeptical about the ability of the current form of miRDeep algorithm to handle such complexity.

This study also brings forward some issues that can be studied in the future. Increasing the number of plant genomes can allow researchers to further test whether MFE distributions of monocots and dicots truly differ and if so, study the underlying mechanisms. Furthermore, improved genome annotation will also improve the discovery of miRNAs missed out due to overlap with an otherwise incorrectly annotated CDS. Other desired advancements include modules for identification of other kinds of sRNA and the ability to characterize multi-functional stem-loops.

Genomics Assignment & Homework Help

Genome is the set of all genes, regulative series, and other details consisted within the non-coding areas of an organism’s DNA. Moreover, genomics is the end result of quickly building up details about large varieties of genes and DNA series from ratings of organisms.

Different article explain different genomics-based approaches for the research study of

Genomics Assignment Help

hereditary variation, liking microarrays and array-based relative genomic hybridization. Other articles analyze the value of genomic information in areas as varied as medication, systematic, and preservation biology. Appropriately, the short articles here attempt to relate some of the characters and concepts that have actually formed genomics, liking the continuous face-off in between openly moneyed and business genome sequencing issues.

Genomics is an area within genes that relates the sequencing and analysis of an organism’s genome.

The genome is the whole DNA material that exists within one cell of an organism. Experts in genomics aim to identify full DNA series and carry out hereditary mapping to help in order to comprehend illness.

Genomics also includes the research of intragenomic procedures such as pleiotropy, epistasis and heterosis in addition to the interactions in between loci and alleles within the genome. The fields of molecular biology and genes are generally interested in the research of the function of single genes, a significant topic in today’s biomedical research study. By contrast, genomics does not include single gene research study unless the function is to comprehend a single gene’s impacts in context of the whole genome.

As per the meaning from the United States Environmental Protection Agency, genomics worries a larger line of clinical query and associated methods than it did. Genomics includes the research of all genes at the Proteome, MRNA, and DNA level along with the cellular or tissue level.

Genomics is a principle that was first established by Fred Sanger who initially sequenced the total genome of a virus and of a mitochondrion. He started the practice of sequencing and genome mapping in addition to establish information and bioinformatics storage in the 1970s and 1980s.

The understanding about genes that has actually up until now been collected has actually caused the development of practical genomics, a field worried about aiming to comprehend the pattern of gene expression, particularly throughout differentenvironmental conditions.

The term genomics was introduced in 1986 by Tom Roderick, a geneticist at the Jackson Laboratory in Maine throughout a conference about the mapping of the human genome.

Genomics consist of genome tasks, genome sequencing, and unique techniques and genomic innovations.

– Functional genomics liking transcriptional profiling, MRNA analysis, MicroRNA analysis, and analysis of other and non-coding RNAs using well established and newly-emerging innovations (such as digital gene expression).

– Evolutionary and relative genomics, consisting of phylogenomics.

– Genomic innovation and approach advancement with a concentrate on amazing and new applications with capacity for substantial effect in the field and emerging innovations

– Computational biology, biostatistics and bioinformatics, liking integrative techniques, network biology, and the advancement of unique devices and methods

– Modern genes on a genomic scale consist of complex gene research studies, population genomics, association researches, structural variation, and gene-environment interactions

– Epigenomics, consisting of DNA methylation, histone adjustment, chromatin structure, inscribing, and chromatin improvement

– Genomic regulative analysis such as DNA aspects, locus control areas, insulators, enhancers, silencers, and systems of gene policy

– Genomic methods to comprehend the system of condition pathogenesis and its relationship to hereditary aspects consist of meta-genomic and the mode and pace of gene and genome series development.

– Medical Genomics, Personal Genomics, and other applications to human health

– Application of Genomic strategies in design organisms that might be of interest to a broad audience.

According to the Centers for Disease Control and Prevention (CDC), Genomics is the research of all the genes in the human genomethat double-stranded DNA helix that specifies who we are and what we are made from. Structure on classical genes, it concentrates on gene variations, the hereditary code we acquire, the environment we stay in, and the variety of illness we establish.

The guarantee of genomics is big. It might one day help us take full advantage of individual health and find the best treatment for any condition. It might help in the advancement of new treatments that modify the human genome and avoid (and even reverse) issues from the conditions we acquire.

Genomics contributes in 9 of the 10 leading causes of death, consist of:

For individuals who are at risk for genetic bust and ovarian or genetic colorectal cancer, hereditary tests might lower their risk by directing evidence-based interventions.1, 2 Genetic tests for other leading causes of death and impairment are appearing. New suggestions are anticipated as the clinical proof on which interventions and tests have health advantages is enhanced.

HCA 35 that three BRCA1 patent claims held by Myriad Genetics, Inc. under Australian Patent 686,004 was void. While Myriad’s patent had actually ended on August 11, 2015, the court decision set vital precedent appropriate to intellectual building in genetics/omics and accuracy medication.

The D’Arcy case itself, along with other lawsuits in the U.S. including Myriad’s gene patents, has actually been gone over formerly on Genomics Law Report. Anomalies in the BRCA1 gene provide enhanced risk of bust and ovarian cancer. The Myriad researchers were initially to clone and series BRCA1, the gene that Mary-Claire King had actually connected to cancer vulnerability in a landmark paper in Science in 1990.

We are available 24/7 in order to provide help for Genomics Assignment help& Genomics homeworkhelp. Our Genomics Online experts are readily available online to offer online help for intricate Genomics assignment & homework within the due date. Genomics help is readily available by skilled experts round the clock.


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Author information


Department of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 7, 1870, Frederiksberg C, Denmark

Prashanth Suravajhala, Lisette J. A. Kogelman & Haja N. Kadarmideen

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Intestinal organoids, derived from intestinal stem cells (ISCs) and composed of ISCs, Paneth cells (PCs), enteroendocrine cells (EECs), goblet cells, and absorptive enterocytes, have been invaluable to the study of intestinal biology [1]. Recent advances in massively parallel single-cell RNA-sequencing (scRNA-seq) [2] have enabled the cataloging of cell types and states of the murine small intestinal epithelium [3] and intestinal organoids [4], offering extensive insight into tissue heterogeneity, specifically within subsets of rare secretory cell populations. However, there have been no formal comparisons of how the in vitro intestinal organoid condition recapitulates the defined in vivo cell types. While the generation of comprehensive cellular atlases has become a major focus of a global effort to map tissues in humans, model organisms, and derived organoids at single-cell resolution [5], the challenge of how to functionally investigate key insights from cell types in vivo, or even more simply to confirm the high-fidelity representation of these states in existing model systems remains [6, 7].

Intestinal organoids are a compelling system with which to study specialized cells of the epithelium. They are self-organizing, stem cell-derived structures, which, to a reasonable degree, resemble their in vivo counterpart, can be rapidly grown, and are amenable to many biochemical and genetic perturbations [8]. Recent work has demonstrated the utility of organoids in assessing bulk phenotypes that are readily observed and easily selected for, such as phenotypes of cystic fibrosis and the study of cancer-associated mutational signatures [9, 10]. However, the application of organoid models to the study of complex disease, such as polygenic inflammatory disease, has been limited. In such instances, the subtler phenotypes, such as those present in inflammatory bowel disease (IBD), may not manifest if the originating cell state present in vivo is not accurately represented within an organoid model. This challenge is particularly clear in IBD [11], where loci identified through genome wide association studies (GWAS) have proven difficult to efficiently examine through the use of in vivo animal models.

For instance, PC dysfunction is implicated in Crohn’s disease, a subset of IBD typically afflicting the small bowel [12]. Co-localized with LGR5 + ISCs of the small intestinal crypts, long-lived PCs [13] support maintenance of the ISC niche, producing the Wnt and Notch signaling ligands WNT3 and DLL4 [14], and are potent modulators of the gut microflora through secretion of multiple antimicrobials including lysozyme (LYZ), phospholipase A2 group 1B (PLA2G1B), angiogenin ribonuclease A family member 5 (ANG5), and alpha-defensins (DEFAs), amongst others [15]. Multiple allelic variants of NOD2, ATG16L1, and XBP1, are associated with ileal Crohn’s disease [16,17,18,19] and have been identified in animal models to cause clear PC phenotypes, including lower DEFA expression [20], defects in autophagy, granule formation, and secretion [19, 21], and uncompensated ER stress [22]. While in vivo models currently provide the most physiologically representative system to probe PC biology, they are inherently complex and poorly scaled, hindering basic research into molecular mechanisms of disease and the potential for scalable therapeutic screening. Recently, conventional intestinal organoids were used to describe the dynamics of PC degranulation in response to multiple agonists [23] and to assess PC suppression of enteric pathogens [24]. While these organoid studies are arguably more representative than other in vitro systems, the question of physiological fidelity of this heterogeneous system remains unanswered, especially given that the timescales to derive conventional organoids is typically less than a week, while in vivo the lifespan of a PC is on the order of several weeks.

To improve the representation of specific cell types in intestinal organoids, investigators have utilized cellular engineering approaches starting with ISCs to derive multiple enriched or specialized models. These include enterocytes with improved intestinal ion transport [25], epithelial monolayers capable of secretion and IgA transcytosis [26], and organoids enriched for the rare secretory EEC population [27]. However, in each instance, there has been no global comparison of the extent to which intestinal organoids, or further specialized derivatives, recapitulate defined in vivo cell types and states. Moving beyond the generation of in vivo tissue maps towards mechanistic insights, particularly in disease settings, will require an understanding of how the in vitro organoid models utilized for such studies represent the cell types and states identified beyond single marker genes.

Here, we provide a global comparison between the in vivo cell states of the murine small intestinal epithelium and the in vitro conventional intestinal organoid, and establish a systematic workflow for improving the physiological representation of stem cell-derived cell states to enable the creation of high-fidelity in vitro models. Taking the PC as a test case, we utilize massively parallel single-cell transcriptomics (scRNA-seq Seq-Well) [28] to benchmark the conventional organoid model against its in vivo counterpart and identify differences in developmental pathway signaling between in vitro and in vivo cell states. Single-cell transcriptomic approaches were key in enabling this study as epithelial cell types are challenging to reliably and prospectively isolate by fluorescence-activated cell sorting (FACS) due to the absence of robust surface markers and the spectrum of differentiation states present. This profiling guides the rational augmentation of signaling pathway activity during stem cell differentiation with a small molecule chemical induction method we previously validated to enhance global Lyz gene expression [29]. We validate our approach by generating an enhanced in vitro physiological mimic of the in vivo PC and provide a detailed characterization of the derived cell state through morphologic, proteomic, transcriptomic, and functional assays based on known signatures of in vivo PCs. Furthermore, we use our enhanced model and findings from its transcriptomic and proteomic characterization to identify Nupr1 as a potential stress-response factor that facilitates the survival of PCs, demonstrating the improved ability to examine gene function in vitro within a more representative cell type.

10x Genomics Unlocks Whole Transcriptome Analysis in FFPE Tissues With New Visium Assay, Now Shipping

June 10, 2021 08:00 ET | Source: 10x Genomics, Inc. 10x Genomics, Inc.

Pleasanton, California, UNITED STATES

PLEASANTON, Calif., June 10, 2021 (GLOBE NEWSWIRE) -- 10x Genomics, Inc. (Nasdaq: TXG) today announced it is shipping its new Visium Spatial Gene Expression for FFPE (formalin-fixed and paraffin-embedded) assay. The offering gives researchers access to the whole transcriptome across their entire tissue, enabling true unbiased discovery in FFPE samples for the first time.

The product addresses customer demand for whole transcriptome spatial profiling of FFPE tissue blocks, especially in the area of translational research. FFPE processing can cause significant damage to nucleic acids, such as RNA, making it challenging to perform transcriptomic analyses. To address this, 10x Genomics developed groundbreaking new chemistry for Visium to be applied to FFPE tissues with similarly high sensitivity and the same spatial resolution as fresh frozen samples. The result is a product that combines the benefits of histological techniques with the massive throughput and discovery power of next generation sequencing, advancing discoveries in clinical research.

Researchers at institutions including the Keck School of Medicine at University of Southern California, UPMC Hillman Cancer Center and Europe’s Wellcome Sanger Institute are already evaluating Visium for FFPE for their research in immuno-oncology and developmental neuroscience.

“This is the Century of Biology, and at 10x, we continue developing tools that can accelerate the mastery of biology to advance human health at an unprecedented pace,” said Ben Hindson, 10x Genomics Co-founder and Chief Scientific Officer. “Our new Visium Spatial Gene Expression for FFPE is trailblazing technology that will help scientists gain a better understanding of biological processes and diseases.”

“The availability of a tool that will allow spatial whole transcriptome sequencing on archival tissue sections is a game changer,” said Dr. John Carpten, Professor and Chair of Translational Genomics at the Keck School of Medicine at University of Southern California, Los Angeles. “This will provide us with the opportunity to assess cellular states in samples that are linked to treatment response and outcomes, and to do so in an unbiased way. 10x Genomics has been an amazing partner in providing commercial-grade innovative technologies to study the molecular features of tumors at the level of the whole transcriptome and with spatial resolution.”

The expanded reach of whole transcriptome analysis on FFPE samples holds promise for a new wave of high-impact studies using biobanked samples across disease areas ranging from cancer to neurodegenerative diseases to inflammatory conditions.

Key features of Visium Spatial Gene Expression for FFPE include:

  • Unbiased whole transcriptome analysis that enables true discovery
  • Full tissue section coverage that removes analysis boundaries associated with predefined ROIs
  • High cellular resolution with 1 to 10 cells per spot depending on tissue type
  • Compatibility with histological analyses, allowing for morphological context overlaid with transcriptomic analysis
  • Ready-to-use assay kit, eliminating the need for specialized instrumentation

The Visium for FFPE assay kit, which contains all the reagents and consumables needed for whole transcriptome analysis in entire human FFPE tissue sections (with options for 4 or 16 reactions per kit), is shipping today and is available for pre-order for mouse transcriptome. To learn more about Visium Spatial Gene Expression for FFPE, visit

About 10x Genomics
10x Genomics is a life science technology company building products to interrogate, understand and master biology to advance human health. The company’s integrated solutions include instruments, consumables and software for analyzing biological systems at a resolution and scale that matches the complexity of biology. 10x Genomics products have been adopted by researchers around the world including in all of the top 100 global research institutions as ranked by Nature in 2019 based on publications and all of the top 20 global pharmaceutical companies by 2019 research and development spend, and have been cited in over 2,500 research papers on discoveries ranging from oncology to immunology and neuroscience. The company’s patent portfolio comprises more than 1,100 issued patents and patent applications.

Forward Looking Statements
This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 as contained in Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections. All statements, other than statements of historical facts, may be forward-looking statements. Forward-looking statements generally can be identified by the use of forward-looking terminology such as “may,” “might," "will,” “should,” “expect,” “plan,” “anticipate,” “could,” “intend,” “target,” “project” “contemplate,” “believe,” “estimate,” “predict,” “potential” or “continue” or the negatives of these terms or variations of them or similar terminology, but the absence of these words does not mean that a statement is not forward-looking. These forward-looking statements include statements regarding 10x Genomics product performance, configuration and capabilities and adoption. These statements are based on management’s current expectations, forecasts, beliefs, assumptions and information currently available to management, and actual outcomes and results could differ materially from these statements due to a number of factors, including the potential impact of the COVID-19 pandemic. Other risks and uncertainties that could affect 10x Genomics, Inc.’s financial and operating results and cause actual results to differ materially from those indicated by the forward-looking statements made in this press release include those discussed under the captions “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations” and elsewhere in the documents 10x Genomics, Inc. files with the Securities and Exchange Commission from time to time. Although 10x Genomics, Inc. believes that the expectations reflected in the forward-looking statements are reasonable, it cannot provide any assurance that these expectations will prove to be correct nor can it guarantee that the future results, levels of activity, performance and events and circumstances reflected in the forward-looking statements will be achieved or occur. The forward-looking statements in this press release are based on information available to 10x Genomics, Inc. as of the date hereof, and 10x Genomics, Inc. disclaims any obligation to update any forward-looking statements provided to reflect any change in its expectations or any change in events, conditions, or circumstances on which any such statement is based, except as required by law. These forward- looking statements should not be relied upon as representing 10x Genomics, Inc.’s views as of any date subsequent to the date of this press release.

The interplay between genomics and evolution

If life had not evolved from a single origin, genomics would lack much of its power. The conservation of sequences allows the function of proteins to be implied from their sequence and critical regulatory sites to be identified in DNA and RNA sequences. Just as thinking about evolution increases the knowledge we can derive from genome sequences, genomics is revolutionizing the study of evolution. The first level of this revolution comes from sequence information, which has already revealed that horizontal gene transfer within prokaryotes has been so extensive that we must think of family mazes as opposed to family trees [22], and the unexpected finding that there have been duplications of the entire genome during the evolution of vertebrates [23]. The second is likely to come from comparing information on gene expression, protein-protein interactions, and genetic interactions between organisms. Such studies will reveal how some pathways have diverged in function while keeping a similar overall structure, while others have evolved from very different structures to have a similar function. For example, only 40% of the predicted proteins in the nematode genome have clear homology to other eukaryotic proteins. Are the other 60% specific to nematodes, or has their primary sequence, but not their function, diverged beyond our ability to detect it? The third, and most important level will be the ability of genomics to enhance experiments on evolution in the laboratory and in the field. For example, it will be possible to determine how selection for a given phenotype has influenced gene expression, and to use genome-wide methods of detecting sequence polymorphisms to map the mutations that produce the phenotype.

Such studies should illuminate the fundamental question in evolution: how do organisms balance robustness, the ability to minimize the impact of short-term genetic variability on their phenotype in favorable environments, with evolvability, the need to respond to long-term exposure to unfavorable environments by producing adaptive phenotypic change? Possible mechanisms include the ability of environmental stress to increase mutation rates and the phenotypic change due to given mutations (reduced phenotypic buffering) [24], as well as the possibility that some mutations that would be deleterious in an optimal environment are advantageous in a difficult one. Using genomics to study how organisms evolve under clearly defined selective pressures should help us solve this riddle. I believe that work on evolution and functional modules will mutually stimulate each other. Understanding how evolutionary pressures constrain modules may help us to decide whether historical chance or the need to mingle robustness with evolvability is the main explanation of why such a small fraction of the pathways an engineer could design for a particular purpose are found in nature. If necessity, rather than chance, largely determines how modules function and interact with each other, experimentation and evolutionary analysis may discover design principles that can be exploited to turn genome-wide data into biological knowledge.

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