15.1: Bacterial Pathogens - Biology

15.1: Bacterial Pathogens - Biology

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15.1: Bacterial Pathogens

Antibiotic resistance: 'Sleeping' bacteria that can survive drug treatment identified

'Sleeper cells', which can survive doses of antibiotics and lie resting in a dormant state, may hold a key to understanding antibiotic resistance, research has found.

Dr Stefano Pagliara, a biophysicist at the University of Exeter, has developed a novel way of identifying cells likely to survive antibiotics, even before the drug treatment.

The research, published in the journal BMC Biology, lays the foundation for understanding the special properties of bacteria that can survive being treated with antibiotics, so that new ways of targeting them can be developed.

Antibiotic resistance is one of the most pressing public health challenges and threatens the ability to effectively fight infectious diseases including pneumonia and tuberculosis.

After dosing bacteria with ampicillin, the Exeter University team found that the vast majority of the 1.3 per cent of cells that survived were live but non growing.

Dr Pagliara has dubbed them 'sleeper cells' because they look dormant and resemble the cells that have been killed by antibiotics, but are potentially dangerous with the ability to 'wake up' and re-infect humans or animals.

The Exeter University research team found that the two types of cells surviving antibiotics, 'sleeper cells' and persister cells, have similar features suggesting the two populations of cells are linked. Their unique fluorescence meant they could both be spotted even before being dosed with antibiotics.

But because 'sleeper cells' are non growing, standard detection methods cannot differentiate them from dead cells, giving the false impression that far fewer cells have survived a course of antibiotics.

The Exeter University team, including Dr Rosie Bamford and Ashley Smith, used a miniaturised device which enabled them to isolate and study single bacteria over time. This device could be used to study any bacteria posing a threat to human or animal health.

Using fluorescence to light up individual cells, they identified the viable but dormant 'sleeper cells', which looked as if they are dead or dying after being treated with antibiotics. The other type of surviving cells known as persister cells -- which accounted for less than one third of surviving cells -- started regrowing after the course of antibiotics ends.

Cells which survive treatment with antibiotics can all eventually divide, leading to a relapse of infection while increasing the risk of antibiotic resistance development.

Dr Pagliara, a senior lecturer in the Living Systems Institute at the University of Exeter, said:

"Antibiotic resistance is one of the serious health challenges of our age. The cells we identified elude antibiotic treatment and pose a serious threat to human health. In fact, unlike persister cells which quickly resume growth after the antibiotic course ends, 'sleeper cells' remain non-growing for prolonged periods of time, and elude detection using traditional methods."

"Our research should make it easier to develop biomarkers to isolate these cells and open up new ways to map the biochemical makeup of bacteria that can escape antibiotics, so we can find ways of targeting them effectively."

Dr Pagliara is planning a programme to identify and isolate individual 'sleeper cells' for a thorough analysis with next-generation sequencing to see how they express genes differently than those that are not resistant to antibiotics.

15.1: Bacterial Pathogens - Biology

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Widespread Signal of Introgression in Immunity-Related Genes

Several genes involved in immunity carry high-frequency archaic variants. For instance, we identify in EAS a cluster of five genes, including three TLRs, which show high rates of introgression. These three TLR genes are actually a cluster of physically linked genes, which are known to have been under positive and balancing selection in Eurasians ( Dannemann et al. 2016 Deschamps et al. 2016 Quach et al. 2016). We also identify two other TLR-associated genes that have an excess of Neanderthal tracts in EAS: IRAK4 and MYD88. These two genes are involved in the immune response to pathogens and are functionally linked to TLR receptors as they code for proteins that are activated by TLR to form a protein complex. We also identify a cluster of three linked genes with an excess of introgressed variants that all belong to the OAS (2′-5′-oligoadenylate synthetase) family in EUR. These genes are also involved in response to pathogens and have been previously described as candidates for adaptive introgression ( Mendez et al. 2013 Sams et al. 2016). Another example of introgressed immunity-related genes is the regions containing STAT2 and ERBB3 in Papuans. It has indeed been shown ( Mendez et al. 2012) that a large 250 kb haplotype of Denisovan ancestry segregates at a high frequency and encompasses these genes. Even though we do not identify in our significant networks an excess of introgressed haplotypes in the STAT2/ERBB3 cluster region, we find closely related genes, for example, JAK1, and numerous other highly introgressed genes that are involved in the immune response ( table 1). It suggests that other functionally related genes might have been targeted by selection, and that adaptive introgression in this case involves more genes than previously thought.

Possible Resistance to Malaria

Interestingly, some of the introgression signals we identify in Papuans can be associated to genes potentially involved in response to malaria. A common strategy of resistance to malaria indeed involves defects in normal red blood cells metabolism and structure ( Williams 2006). It has been observed that several mutations conferring sickle cell anemia (hemoglobin S), thalassemia, Southeast Asian ovalocytosis or Glucose-6-phosphate dehydrogenase (G6PD) deficiency are naturally protective against malaria (reviewed in Williams 2006). Ovalocytosis, a hereditary condition in which most erythrocytes have an oval shape, is a phenotype that occurs in up to 20% or more in the PNG population ( Amato and Booth 1977). Red blood cell membrane defects have several genetic bases, one of them being a mutation in the SPTB gene ( Iolascon et al. 2003 Lelliott et al. 2017), which is one of our genes candidate for being subject to adaptive introgression. Some mutations in the SPTB gene that are found in archaic genomes and that segregate in Papuans, but which are almost absent from the rest of the world have been previously associated to the elliptocytosis phenotype (see ClinVar records in supplementary table S2 , Supplementary Material online). It is also interesting to see that two glucose transporters (GLUT2 and GLUT12) and the G6PD2 gene are also carrying a significant excess of archaic variants. Glucose metabolism in red blood cells has been shown to have a major impact on the survival of Plasmodium parasites ( Williams 2006), and therefore conditions the susceptibility of humans to malaria. We postulate that introgression could have been the source of several adaptive variants increasing the fitness of individuals in an environment where malaria is endemic. Since malaria resistance alleles typically appeared after the Neolithic transition ( Hedrick 2012), these alleles could have been present in archaic populations for a long time, evolving neutrally in the absence of malaria and been positively selected only once the disease appeared ( Laval et al. 2019). Alternatively, archaic populations living in tropical areas could have been exposed to similar endoparasites, which is possible as malaria parasites infect many vertebrates including great apes, which are the most likely origin of the parasite transmission to humans ( Prugnolle et al. 2011). In any case, our results confirm that archaic introgression is widespread in immunity-related genes and that pathogens represent a strong selective pressure which could be one of the major causes of adaptive evolution in humans ( Fumagalli et al. 2011 Daub et al. 2013 Dannemann et al. 2016 Quach et al. 2016 Enard and Petrov 2018).

Introgression in the SLC Family

Genes from the SLC family encode for membrane transport proteins and are involved in several distinct biological processes. Neandertal variants at two SLC loci (SLC6A11 and SLC6A13) have previously been associated to behavioral traits (depression, mood disorders, and smoking behavior) and some alleles have been shown to be preferentially expressed in the brain ( Simonti et al. 2016 Dannemann and Kelso 2017). In Papuans, we find genes showing a significant excess of introgression that have been respectively associated to autism susceptibility and attention-deficit/hyperactivity disorder, for example, SLC9A9 ( Lasky‐Su et al. 2008). Here, we report other genes from the same family that have a brain-biased expression and show an excess of introgressed segments in EAS and EUR: SLC6A1 (a GABA transporter), SLC6A5 (a sodium- and chloride-dependent glycine neurotransmitter transporter), and SLC28A1, as well as in PNG: SLC4A10 (controlling intracellular pH of neurons, the secretion of bicarbonate ions across the choroid plexus, and the pH of the brain extracellular fluid). These results suggest that archaic introgression might have also affected behavioral/neuronal traits, even though it is difficult to link these phenotypes to a precise selective pressure. Other SLC genes showing high levels of introgression include genes encoding for two glucose transporters in PNG (SLC2A2 and SLC212), and SLC24A4 in EAS that has been associated to hair and skin pigmentation and adaptive introgression of an archaic variant at this locus has been hypothesized ( Dannemann and Kelso 2017). The rest of the SLC genes have less specific functions and are therefore difficult to associate to a particular biological process.

Olfactory Receptors

A large Neandertal haplotype encompassing several ORs in the centromeric region of chromosome 11 are found at a frequency of about 10% in EUR. A recent study suggests that the whole centromeric region has been introgressed in EUR ( Langley et al. 2019). These genes are short and clustered together, explaining why ORs pathways are significant in enrichment test that assume independence between loci. Interestingly, more than 40% of the 856 OR genes in the human genome are in 28 gene clusters on chromosome 11 ( Taylor et al. 2006). However, even though the enrichment pattern we observe could be due to physical linkage, we observe a very long haplotype overlapping the centromere of chromosome 11 that includes many ORs and which has been recently introgressed in humans from Neandertals. Indeed, the recombination rate of this region is very low, but it is not low enough to make it compatible with ILS since the divergence with archaic humans. However, it is sufficiently low to still observe such a long haplotype tens of thousands years after the introgression event. Even though a case of adaptive introgression is likely for this region, it is difficult to identify the exact selective constraints having promoted this polymorphism in this region, as the relationship between these ORs and a human phenotype remains to be determined.

Comparison to Other Gene Set Enrichment Analyses of Archaic Introgression

Other studies have looked at patterns of introgression enrichment among biological processes. Overall, our results are in agreement with the trends observed in other studies, but some observed differences might be due to differences in data sets, assumptions and specific hypotheses that we tested. Enard and Petrov (2018) defined a manually curated set of 4,534 proteins that interact with viruses. They compared introgression patterns in this set and in other types of proteins, and they performed a Gene Ontology enrichment analysis to test whether the highly introgressed virus-interacting proteins were enriched in specific functions, which revealed to be mostly, as expected, immunity pathways ( Enard and Petrov 2018). Enrichment tests performed in other studies used the Gene Ontology database ( Sankararaman et al. 2014, 2016 Enard and Petrov 2018 Steinrücken et al. 2018). Gene Ontology contains only sets of genes and no information concerning their interactions (hence, no networks), which would prevent us to perform the subnetwork performed in this study. We thus based our analyses on other biological pathway databases, which included different sets of genes and functions, explaining why some processes were not tested with our approach. For instance, the keratin filament formation process contains a set of genes that are highly introgressed in EUR and EAS ( Sankararaman et al. 2014, 2016 Steinrücken et al. 2018), but it is absent from our results simply because we do not have such pathway among our networks. Even though we are losing some information by restricting ourselves to gene networks, the advantage of our approach is that we are able to find directly interacting genes and identify which subset of the pathway might drive adaptation.

A New Test to Detect Cointrogressed Variants

In this study, we have introduced a new approach to detect a nonrandom association of introgressed alleles within individuals at the gene-set level. This approach is similar to a test of LD but 1) it is performed at the individual level rather than at the population level, 2) it is restricted to functionally related, biologically meaningful, sets of genes and 3) we perform a single test per gene set, irrespective of the number of genes present in this gene-set, which avoids the problem of multiple testing. This approach might thus be a good way to identify small groups of genes that have variants with positive epistatic interactions, which would deserve further investigations and functional validation.

When investigating the genomic signal of polygenic selection from genomic data only, it is difficult to distinguish between two possible scenarios: sequential selection of independent genes or simultaneous selection of alleles at different loci within individuals (through positive epistasis). Provided that alleles can be classified into two categories (introgressed and nonintrogressed in our case), our approach allows one to distinguish between these two types of polygenic selection: independent selection at different loci, and epistatic selection within a gene set. By applying this method to archaic introgression in modern humans, we found evidence of these two types of selection. Note however that our test is quite general, as it could be applied to any other distinctions such as two different genetic backgrounds or ancestral/derived states.

Our approach shows that the genomic signal of polygenic selection we observed is most of the time explained by an independent and probably sequential selection of loci involved in related functions (e.g., immune or metabolic functions, table 1). Our results also highlight the fact that physical linkage might be responsible for a large portion of the signal observed in previous enrichment tests. Indeed, physical linkage between loci will artificially inflate the proportion of significant genes in a given gene set. For example, in the case of archaic introgression, ORs have been identified several times in enrichment test ( Sankararaman et al. 2014 Steinrücken et al. 2018), but we observe that highly introgressed ORs (at least 24 of them) are clustered together in the genome, potentially leading to a spurious signal of enrichment when considering these genes as independent. It follows that one should consider linkage between loci when performing enrichment tests.

Interestingly, we find several significant gene sets with signals of epistatic selection of archaic variants in EUR, EAS, and Papuans. Most of these gene sets are involved in immunity, and a few are involved in metabolic functions. Even though the observed signal is relatively weak (see fig. 2 and supplementary fig. S4 , Supplementary Material online), it is still significant after correction for multiple tests. Note that the observation of a strong LD between physically unlinked loci requires extremely strong epistatic selection ( Felsenstein 1965), so that it is still remarkable that we observe signals of epistasis given the relatively small samples sizes on which our test was applied.


In 1902, B. thuringiensis was first discovered in silkworms by Japanese sericultural engineer Ishiwatari Shigetane ( 石渡 繁胤 ) . He named it B. sotto, [8] using the Japanese word sottō ( 卒倒 , 'collapse') , here referring to bacillary paralysis. [9] In 1911, German microbiologist Ernst Berliner rediscovered it when he isolated it as the cause of a disease called Schlaffsucht in flour moth caterpillars in Thuringia (hence the specific name thuringiensis, "Thuringian"). [10] B. sotto would later be reassigned as B. thuringiensis var. sotto. [11]

In 1976, Robert A. Zakharyan reported the presence of a plasmid in a strain of B. thuringiensis and suggested the plasmid's involvement in endospore and crystal formation. [12] [13] B. thuringiensis is closely related to B. cereus, a soil bacterium, and B. anthracis, the cause of anthrax the three organisms differ mainly in their plasmids. [14] : 34–35 Like other members of the genus, all three are anaerobes capable of producing endospores. [1]

Species group placement Edit

B. thuringiensis is placed in the Bacillus cereus group which is variously defined as: seven closely related species: B. cereus sensu stricto (B. cereus), B. anthracis, B. thuringiensis, B. mycoides, B. pseudomycoides, and B. cytotoxicus [15] or as six species in a Bacillus cereus sensu lato: B. weihenstephanensis, B. mycoides, B. pseudomycoides, B. cereus, B. thuringiensis, and B. anthracis. Within this grouping B.t. is more closely related to B.ce. It is more distantly related to B.w., B.m., B.p., and [16]

Subspecies Edit

There are several dozen recognized subspecies of B. thuringiensis. Subspecies commonly used as insecticides include B. thuringiensis subspecies kurstaki (Btk), subspecies israelensis (Bti) and subspecies aizawa. [17] [18] [19] [20] Some Bti lineages are clonal. [16]

Some strains are known to carry the same genes that produce enterotoxins in B. cereus, and so it is possible that the entire B. cereus sensu lato group may have the potential to be enteropathogens. [16]

The proteins that B. thuringiensis is most known for are encoded by cry genes. [21] In most strains of B. thuringiensis, these genes are located on a plasmid (in other words cry is not a chromosomal gene in most strains). [22] [23] [24] [16] If these plasmids are lost it becomes indistinguishable from B. cereus as B. thuringiensis has no other species characteristics. Plasmid exchange has been observed both naturally and experimentally both within B.t. and between B.t. and two congeners, B. cereus and B. mycoides. [16]

plcR is an indispensable transcription regulator of most virulence factors, its absence greatly reducing virulence and toxicity. Some strains do naturally complete their life cycle with an inactivated plcR. It is half of a two-gene operon along with the heptapeptide papR. papR is part of quorum sensing in B. thuringiensis. [16]

Various strains including Btk ATCC 33679 carry plasmids belonging to the wider pXO1-like family. (The pXO1 family being a B. cereus-common family with members of

330kb length. They differ from pXO1 by replacement of the pXO1 pathogenicity island.) The insect parasite Btk HD73 carries a pXO2-like plasmid - pBT9727 - lacking the 35kb pathogenicity island of pXO2 itself, and in fact having no identifiable virulence factors. (The pXO2 family does not have replacement of the pathogenicity island, instead simply lacking that part of pXO2.) [16]

The genomes of the B. cereus group may contain two types of introns, dubbed group I and group II. B.t strains have variously 0-5 group Is and 0-13 group IIs. [16]

There is still insufficient information to determine whether chromosome-plasmid coevolution to enable adaptation to particular environmental niches has occurred or is even possible. [16]

Common with B. cereus but so far not found elsewhere - including in other members of the species group - are the efflux pump BC3663, the N-acyl- L -amino-acid amidohydrolase BC3664, and the methyl-accepting chemotaxis protein BC5034. [16]

Has similar proteome diversity to close relative B. cereus. [16]

Upon sporulation, B. thuringiensis forms crystals of two types of proteinaceous insecticidal delta endotoxins (δ-endotoxins) called crystal proteins or Cry proteins, which are encoded by cry genes, and Cyt proteins. [21]

Cry toxins have specific activities against insect species of the orders Lepidoptera (moths and butterflies), Diptera (flies and mosquitoes), Coleoptera (beetles) and Hymenoptera (wasps, bees, ants and sawflies), as well as against nematodes. [25] [26] Thus, B. thuringiensis serves as an important reservoir of Cry toxins for production of biological insecticides and insect-resistant genetically modified crops. When insects ingest toxin crystals, their alkaline digestive tracts denature the insoluble crystals, making them soluble and thus amenable to being cut with proteases found in the insect gut, which liberate the toxin from the crystal. [22] The Cry toxin is then inserted into the insect gut cell membrane, paralyzing the digestive tract and forming a pore. [27] The insect stops eating and starves to death live Bt bacteria may also colonize the insect, which can contribute to death. [22] [27] [28] Death occurs within a few hours or weeks. [29] The midgut bacteria of susceptible larvae may be required for B. thuringiensis insecticidal activity. [30]

A B. thuringiensis small RNA called BtsR1 can silence the Cry5Ba toxin expression when outside the host by binding to the RBS site of the Cry5Ba toxin transcript to avoid nematode behavioral defenses. The silencing results in an increased of the bacteria ingestion by C. elegans.The expression of BtsR1 is then reduced after ingestion, resulting in Cry5Ba toxin production and host death. [31]

In 1996 another class of insecticidal proteins in Bt was discovered: the vegetative insecticidal proteins (Vip InterPro: IPR022180). [32] [33] Vip proteins do not share sequence homology with Cry proteins, in general do not compete for the same receptors, and some kill different insects than do Cry proteins. [32]

In 2000, a novel subgroup of Cry protein, designated parasporin, was discovered from non-insecticidal B. thuringiensis isolates. [34] The proteins of parasporin group are defined as B. thuringiensis and related bacterial parasporal proteins that are not hemolytic, but capable of preferentially killing cancer cells. [35] As of January 2013, parasporins comprise six subfamilies: PS1 to PS6. [36]

Spores and crystalline insecticidal proteins produced by B. thuringiensis have been used to control insect pests since the 1920s and are often applied as liquid sprays. [37] They are now used as specific insecticides under trade names such as DiPel and Thuricide. Because of their specificity, these pesticides are regarded as environmentally friendly, with little or no effect on humans, wildlife, pollinators, and most other beneficial insects, and are used in organic farming [26] however, the manuals for these products do contain many environmental and human health warnings, [38] [39] and a 2012 European regulatory peer review of five approved strains found, while data exist to support some claims of low toxicity to humans and the environment, the data are insufficient to justify many of these claims. [40]

New strains of Bt are developed and introduced over time [41] as insects develop resistance to Bt, [42] or the desire occurs to force mutations to modify organism characteristics [43] [ clarification needed ] , or to use homologous recombinant genetic engineering to improve crystal size and increase pesticidal activity, [44] or broaden the host range of Bt and obtain more effective formulations. [45] Each new strain is given a unique number and registered with the U.S. EPA [46] and allowances may be given for genetic modification depending on "its parental strains, the proposed pesticide use pattern, and the manner and extent to which the organism has been genetically modified". [47] Formulations of Bt that are approved for organic farming in the US are listed at the website of the Organic Materials Review Institute (OMRI) [48] and several university extension websites offer advice on how to use Bt spore or protein preparations in organic farming. [49] [50]

The Belgian company Plant Genetic Systems (now part of Bayer CropScience) was the first company (in 1985) to develop genetically modified crops (tobacco) with insect tolerance by expressing cry genes from B. thuringiensis the resulting crops contain delta endotoxin. [51] [52] The Bt tobacco was never commercialized tobacco plants are used to test genetic modifications since they are easy to manipulate genetically and are not part of the food supply. [53] [54]

Usage Edit

In 1985, potato plants producing CRY 3A Bt toxin were approved safe by the Environmental Protection Agency, making it the first human-modified pesticide-producing crop to be approved in the US, [56] [57] though many plants produce pesticides naturally, including tobacco, coffee plants, cocoa, and black walnut. This was the 'New Leaf' potato, and it was removed from the market in 2001 due to lack of interest. [58]

In 1996, genetically modified maize producing Bt Cry protein was approved, which killed the European corn borer and related species subsequent Bt genes were introduced that killed corn rootworm larvae. [59]

The Bt genes engineered into crops and approved for release include, singly and stacked: Cry1A.105, CryIAb, CryIF, Cry2Ab, Cry3Bb1, Cry34Ab1, Cry35Ab1, mCry3A, and VIP, and the engineered crops include corn and cotton. [60] [61] : 285ff

Corn genetically modified to produce VIP was first approved in the US in 2010. [62]

In India, by 2014, more than seven million cotton farmers, occupying twenty-six million acres, had adopted Bt cotton. [63]

Monsanto developed a soybean expressing Cry1Ac and the glyphosate-resistance gene for the Brazilian market, which completed the Brazilian regulatory process in 2010. [64] [65]

Bt-transformed aspens - specifically Populus hybrids - have been developed. They do suffer lesser leaf damage from insect herbivory. The results have not been entirely positive however: The intended result - better timber yield - was not achieved, with no growth advantage despite that reduction in herbivore damage one of their major pests still preys upon the transgenic trees and besides that, their leaf litter decomposes differently due to the transgenic toxins, resulting in alterations to the aquatic insect populations nearby. [66]

Safety studies Edit

The use of Bt toxins as plant-incorporated protectants prompted the need for extensive evaluation of their safety for use in foods and potential unintended impacts on the environment. [ citation needed ]

Dietary risk assessment Edit

Concerns over the safety of consumption of genetically-modified plant materials that contain Cry proteins have been addressed in extensive dietary risk assessment studies. While the target pests are exposed to the toxins primarily through leaf and stalk material, Cry proteins are also expressed in other parts of the plant, including trace amounts in maize kernels which are ultimately consumed by both humans and animals. [67]

Toxicology studies Edit

Animal models have been used to assess human health risk from consumption of products containing Cry proteins. The United States Environmental Protection Agency recognizes mouse acute oral feeding studies where doses as high as 5,000 mg/kg body weight resulted in no observed adverse effects. [68] Research on other known toxic proteins suggests that toxicity occurs at much lower doses [ clarification needed ] , further suggesting that Bt toxins are not toxic to mammals. [69] The results of toxicology studies are further strengthened by the lack of observed toxicity from decades of use of B. thuringiensis and its crystalline proteins as an insecticidal spray. [70]

Allergenicity studies Edit

Introduction of a new protein raised concerns regarding the potential for allergic responses in sensitive individuals. Bioinformatic analysis of known allergens has indicated there is no concern of allergic reactions as a result of consumption of Bt toxins. [71] Additionally, skin prick testing using purified Bt protein resulted in no detectable production of toxin-specific IgE antibodies, even in atopic patients. [72]

Digestibility studies Edit

Studies have been conducted to evaluate the fate of Bt toxins that are ingested in foods. Bt toxin proteins have been shown to digest within minutes of exposure to simulated gastric fluids. [73] The instability of the proteins in digestive fluids is an additional indication that Cry proteins are unlikely to be allergenic, since most known food allergens resist degradation and are ultimately absorbed in the small intestine. [74]

Ecological risk assessment Edit

Ecological risk assessment aims to ensure there is no unintended impact on non-target organisms and no contamination of natural resources as a result of the use of a new substance, such as the use of Bt in genetically-modified crops. The impact of Bt toxins on the environments where transgenic plants are grown has been evaluated to ensure no adverse effects outside of targeted crop pests. [75]

Persistence in environment Edit

Concerns over possible environmental impact from accumulation of Bt toxins from plant tissues, pollen dispersal, and direct secretion from roots have been investigated. Bt toxins may persist in soil for over 200 days, with half-lives between 1.6 and 22 days. Much of the toxin is initially degraded rapidly by microorganisms in the environment, while some is adsorbed by organic matter and persists longer. [76] Some studies, in contrast, claim that the toxins do not persist in the soil. [76] [77] [78] Bt toxins are less likely to accumulate in bodies of water, but pollen shed or soil runoff may deposit them in an aquatic ecosystem. Fish species are not susceptible to Bt toxins if exposed. [79]

Impact on non-target organisms Edit

The toxic nature of Bt proteins has an adverse impact on many major crop pests, but ecological risk assessments have been conducted to ensure safety of beneficial non-target organisms that may come into contact with the toxins. Widespread concerns over toxicity in non-target lepidopterans, such as the monarch butterfly, have been disproved through proper exposure characterization, where it was determined that non-target organisms are not exposed to high enough amounts of the Bt toxins to have an adverse effect on the population. [80] Soil-dwelling organisms, potentially exposed to Bt toxins through root exudates, are not impacted by the growth of Bt crops. [81]

Insect resistance Edit

Multiple insects have developed a resistance to B. thuringiensis. In November 2009, Monsanto scientists found the pink bollworm had become resistant to the first-generation Bt cotton in parts of Gujarat, India - that generation expresses one Bt gene, Cry1Ac. This was the first instance of Bt resistance confirmed by Monsanto anywhere in the world. [82] [83] Monsanto responded by introducing a second-generation cotton with multiple Bt proteins, which was rapidly adopted. [82] Bollworm resistance to first-generation Bt cotton was also identified in Australia, China, Spain, and the United States. [84] Additionally, resistance to Bt was documented in field population of diamondback moth in Hawaii, the continental US, and Asia. [85] Studies in the cabbage looper have suggested that a mutation in the membrane transporter ABCC2 can confer resistance to Bt Cry1Ac. [86]

Secondary pests Edit

Several studies have documented surges in "sucking pests" (which are not affected by Bt toxins) within a few years of adoption of Bt cotton. In China, the main problem has been with mirids, [87] [88] which have in some cases "completely eroded all benefits from Bt cotton cultivation". [89] The increase in sucking pests depended on local temperature and rainfall conditions and increased in half the villages studied. The increase in insecticide use for the control of these secondary insects was far smaller than the reduction in total insecticide use due to Bt cotton adoption. [90] Another study in five provinces in China found the reduction in pesticide use in Bt cotton cultivars is significantly lower than that reported in research elsewhere, consistent with the hypothesis suggested by recent studies that more pesticide sprayings are needed over time to control emerging secondary pests, such as aphids, spider mites, and lygus bugs. [91]

Similar problems have been reported in India, with both mealy bugs [92] [93] and aphids [94] although a survey of small Indian farms between 2002 and 2008 concluded Bt cotton adoption has led to higher yields and lower pesticide use, decreasing over time. [95]

Controversies Edit

The controversies surrounding Bt use are among the many genetically modified food controversies more widely. [96]

Lepidopteran toxicity Edit

The most publicised problem associated with Bt crops is the claim that pollen from Bt maize could kill the monarch butterfly. [97] The paper produced a public uproar and demonstrations against Bt maize however by 2001 several follow-up studies coordinated by the USDA had asserted that "the most common types of Bt maize pollen are not toxic to monarch larvae in concentrations the insects would encounter in the fields." [98] [99] [100] [101] Similarly, B. thuringiensis has been widely used for controlling Spodoptera littoralis larvae growth due to their detrimental pest activities in Africa and Southern Europe. However, S. littoralis showed resistance to many strains of B. thuriginesis and were only effectively controlled by a few strains. [102]

Wild maize genetic mixing Edit

A study published in Nature in 2001 reported Bt-containing maize genes were found in maize in its center of origin, Oaxaca, Mexico. [103] In 2002, paper concluded, "the evidence available is not sufficient to justify the publication of the original paper." [104] A significant controversy happened over the paper and Nature ' s unprecedented notice. [105]

A subsequent large-scale study in 2005 failed to find any evidence of genetic mixing in Oaxaca. [106] A 2007 study found the "transgenic proteins expressed in maize were found in two (0.96%) of 208 samples from farmers' fields, located in two (8%) of 25 sampled communities." Mexico imports a substantial amount of maize from the U.S., and due to formal and informal seed networks among rural farmers, many potential routes are available for transgenic maize to enter into food and feed webs. [107] One study found small-scale (about 1%) introduction of transgenic sequences in sampled fields in Mexico it did not find evidence for or against this introduced genetic material being inherited by the next generation of plants. [108] [109] That study was immediately criticized, with the reviewer writing, "Genetically, any given plant should be either non-transgenic or transgenic, therefore for leaf tissue of a single transgenic plant, a GMO level close to 100% is expected. In their study, the authors chose to classify leaf samples as transgenic despite GMO levels of about 0.1%. We contend that results such as these are incorrectly interpreted as positive and are more likely to be indicative of contamination in the laboratory." [110]

Colony collapse disorder Edit

As of 2007, a new phenomenon called colony collapse disorder (CCD) began affecting bee hives all over North America. Initial speculation on possible causes included new parasites, pesticide use, [111] and the use of Bt transgenic crops. [112] The Mid-Atlantic Apiculture Research and Extension Consortium found no evidence that pollen from Bt crops is adversely affecting bees. [98] [113] According to the USDA, "Genetically modified (GM) crops, most commonly Bt corn, have been offered up as the cause of CCD. But there is no correlation between where GM crops are planted and the pattern of CCD incidents. Also, GM crops have been widely planted since the late 1990s, but CCD did not appear until 2006. In addition, CCD has been reported in countries that do not allow GM crops to be planted, such as Switzerland. German researchers have noted in one study a possible correlation between exposure to Bt pollen and compromised immunity to Nosema." [114] The actual cause of CCD was unknown in 2007, and scientists believe it may have multiple exacerbating causes. [115]

Some isolates of B. thuringiensis produce a class of insecticidal small molecules called beta-exotoxin, the common name for which is thuringiensin. [116] A consensus document produced by the OECD says: "Beta-exotoxins are known to be toxic to humans and almost all other forms of life and its presence is prohibited in B. thuringiensis microbial products". [117] Thuringiensins are nucleoside analogues. They inhibit RNA polymerase activity, a process common to all forms of life, in rats and bacteria alike. [118]

Opportunistic pathogen of animals other than insects, causing necrosis, pulmonary infection, and/or food poisoning. How common this is is unknown because these are always taken to be B. cereus infections and are rarely tested for the Cry and Cyt proteins that are the only factor distinguishing .B thuringiensis from B. cereus. [16]

Revised Estimates for the Number of Human and Bacteria Cells in the Body

Reported values in the literature on the number of cells in the body differ by orders of magnitude and are very seldom supported by any measurements or calculations. Here, we integrate the most up-to-date information on the number of human and bacterial cells in the body. We estimate the total number of bacteria in the 70 kg "reference man" to be 3.8·1013. For human cells, we identify the dominant role of the hematopoietic lineage to the total count (≈90%) and revise past estimates to 3.0·1013 human cells. Our analysis also updates the widely-cited 10:1 ratio, showing that the number of bacteria in the body is actually of the same order as the number of human cells, and their total mass is about 0.2 kg.

Conflict of interest statement

The authors have declared that no competing interests exist.


Fig 1. Back of the envelope estimate…

Fig 1. Back of the envelope estimate of the number of cells in an adult…

Fig 2. The distribution of the number…

Fig 2. The distribution of the number of human cells by cell type.

Fig 3. Distribution of cell number and…

Fig 3. Distribution of cell number and mass for different cell types in the human…

Materials and Methods

Reagents and Tools table

Reagent/Resource Reference or source Identifier or catalog number
Experimental models
BW25113 (E. coli) CGSC 7636
JEN202 (E. coli) Luciano lab (Bikard et al, 2013 PMID: 23761437)
Serovar Typhi Ty2 (S. enterica) ATCC 700931
M5A1 (K. oxytoca) ATCC 7342
Recombinant DNA
pdCas9-bacteria Addgene 44249
pgRNA-bacteria Addgene 44251
pWJ89 Luciano lab (Bikard et al, 2013 PMID: 23761437)
pWJ96 Luciano lab (Bikard et al, 2013 PMID: 23761437)
pWJ97 Luciano lab (Bikard et al, 2013 PMID: 23761437)
pEB2-mScarlet-I Addgene 104007
RS7003 promoter library Johns et al, 2018 PMID: 30052624
pOSIP-Kan St-Pierre et al, 2013 PMID: 24050148
pdCas9-linker This study
pdCas9-AsiA This study
pdCas9-AsiA_m1.1 This study
pdCas9-AsiA_m2.1 This study
pHH39 This study
Additional plasmids and more information This study Appendix Table S2
Oligonucleotides and other sequence-based reagents
guide RNAs_N20 This study Appendix Table S5
Q_RT_PCR primers This study Appendix Table S6
Chemicals, enzymes and other reagents
Q5 ® High-Fidelity 2X Master Mix New England Biolabs M0492S
NEBuilder ® HiFi DNA Assembly Master Mix New England BioLabs E2621
T4 DNA ligase New England BioLabs M0202
T4 Polynucleotide kinase New England BioLabs M0201
SuperScript III Reverse Transcriptase Invitrogen 18080-093
KAPA SYBR FAST qPCR Master Mix Kapa Biosystems KK4602
Maxima reverse transcriptase Thermo Scientific EP0742
Geneious v11.1
Python 3.6.0
Bowtie 2 (Langmead & Salzberg, 2012 PMID:22388286)
HTseq (Anders et al, 2015 PMID:25260700)
BBmerge (Bushnell et al, 2017 PMID:29073143)
DRAFTS (Yim et al, 2019 PMID:31464371)
GeneMorph II EZClone Domain Mutagenesis Kit Agilent Technologies 200552
RNA Clean & Concentrator Kits Zymo Research R1030
DNA Clean & Concentrator Kits Zymo Research D4013
Ribo-Zero rRNA Removal Kit (Bacteria) Illumina
NEBNext Ultra Directional RNA Library Prep Kit New England BioLabs E7760
Illumina NextSeq 500/550 mid output kit v2/v2.5 (150/300 cycles) Illumina Cat #20024904/20024905
PrepGem bacteria kit MicroGEM PBA0100
BD FACS Aria II BD Biosciences
Guava ® InCyte MilliporeSigma
Synergy H1 plate reader BioTek
CFX96 Touch Real-Time PCR machine Bio-Rad

Methods and Protocols

Strains and culturing conditions

E. coli strains and other bacterial species used in the study are listed in Appendix Table S1, and all E. coli strains are derived from the MG1655 parental background. Cells were grown in rich LB medium at 37°C with agitation unless stated otherwise. For plasmid transformation, general protocols were followed, and plasmids were maintained under antibiotics selection at all times. For constructing genomic insertions, the GFP expression cassette amplified from pWJ89 (Bikard et al, 2013 ) was cloned between multiple cloning sites of pOSIP-Kan and inserted chromosomally following the clonetegration method (St-Pierre et al, 2013 ). For the antibiotic selection and induction of target genes, the following concentrations were used: carbenicillin (Carb) 50 μg/ml, chloramphenicol (Cam) 20 μg/ml, kanamycin (Kan) 50 μg/ml, spectinomycin (Spec) 50 μg/ml, Bleocin (Bleo) 5 μg/ml, and anhydrotetracycline (aTc) 100 ng/ml. For induction of target genes, aTc was added to the culture at the exponential growth phase for 4 h before cells were harvested for characterization.

Construction of plasmids

The dCas9 fusion library was constructed based on the pdCas9-bacteria plasmid (Addgene #44249). Linker sequences (SAGGGGSGGGGS) and fusion candidates were either amplified from DNA synthesized de novo (IDT gBlocks ® ) or E. coli genomic DNA and subcloned after the dCas9 sequence in the pdCad9-bacteria plasmid (Addgene #44249). All guide RNA plasmids (pgRNA-H1 to pgRNA-H21) were constructed from the pgRNA-bacteria plasmid (Addgene #44251), using inverted PCR and blunt-end ligation to modify the N20 seed sequences. For dual gRNA plasmids (pgRNA-H4H5, pgRNA-H4H11), each gRNA was built separately and jointed subsequently. GFP reporter plasmids (pWJ89, pWJ96, pWJ97) were gifts from the Marraffini lab at Rockefeller University (Bikard et al, 2013 ). The promoter region upstream of the GFP reporter in pWJ89 was amplified for constructing other antibiotic reporter plasmids (pHH34-37). The GFP-mScarlet reporter plasmid (pHH39) was constructed by cloning the mScarlet gene from pEB2-mScarlet-I (Addgene #104007) under the WJ97 promoter and joined with the weak GFP expression cassette from pWJ89. For screening the inducible metagenomic promoter library (RS7003) (Johns et al, 2018 ), gRNA-H22 and gRNA-H23 expression cassettes were jointed with dCas9-AsiA_m2.1 separately, resulting pHH40 and pHH41. Cloning was done by Gibson assembly if not otherwise noted in all cases. Plasmids used and associated details are listed in Appendix Table S2.

Development of CasTA screening platform

The dCas9 fusion library, gRNAs, and reporter genes were built on 3 different compatible plasmids (dCas9: p15A, Cam resistance gRNA: ColE1, Carb resistance reporter: SC101, Kan resistance), so they can be transformed and propagated within the same cell (Appendix Fig S1). To use a antibiotic resistance gene as a reporter, we tested different antibiotic genes and modulated degradation rate (fusion with ssrA tag: AANDENYALAA) for selective stringency (Appendix Fig S2A and B). Dual selective reporters (Kan and Bleo) were constructed, which decrease the escape rate by 10 fold (Appendix Fig S2C and D).

Directed evolution of dCas9-AsiA using CasTA screening platform

We mutagenized the wild-type AsiA region of dCas9-AsiA using the GeneMorph II EZClone Domain Mutagenesis Kit (Agilent Technologies), following the manufacture's protocol.

  • In brief, 50 ng of parental template DNA was used for amplification with error-prone DNA polymerase (Mutazyme II). Under this condition, the AsiA region contains on average ˜2 nucleotides changes per variant after PCR mutagenesis (Appendix Fig S4).
  • In the first round of directed evolution, the dCas9-AsiA mutant library was transformed to the cells expressing gRNA-H4 and dual selective reporters (pHH37 and pHH38). ˜5 × 10 8 transformants were grown under 0.2× regular Kan concentration and 2× regular Bleo concentration.
  • Grown colonies were harvested and propagated together with Cam selection to maintain solely the dCas9-AsiA variant plasmids.
  • The dCas9-AsiA plasmids were subsequently extracted and transformed to cells containing pgRNA-H4 and pWJ89.
  • Individual colonies were Sanger sequenced to identify the mutations in AsiA and characterized based on GFP intensity (Appendix Table S4). The background fluorescence was measured using the parental strain (BW25113), and auto-fluorescence was subtracted from the fluorescence readings of all samples. Fold change of fluorescence was normalized to cells expressing the GFP only plasmid (pWJ89).
  • The dCas9-AsiA_m1.1 plasmid from the most abundant mutant variant was extracted and transformed to the GFP reporter strain (containing pgRNA-H4 and pWJ89) again to verify fluorescent intensity (Fig 2C).
  • In the second round of directed evolution, the dCas9_AsiA_m1.1 variant was used as a template to generate additional variants following the same conditions.
  • The second generation of the dCas9-AsiA mutant library was transformed to GFP reporter cells, containing pgRNA-H4 and pWJ89 as described above.
  • We enriched the top 0.1% of highest GFP expression from the population of 1x10 7 transformants using fluorescence activated cell sorting (BD FACS Aria II).
  • Post-sorted cell population was plated on selective LB again to obtain clonal colonies, and individual colonies were picked for Sanger sequencing and measurement of GFP intensity.

Quantification of gene expression induced by CasTA

To examine CRISPRa on genomic targets, pdCas9-AsiA_m2.1 was transformed along with gRNA constructs (gRNA-H12 to gRNA-H21, Appendix Table S5) designed for each gene (Appendix Table S6). Cells expressing dCas9-AsiA_m2.1 and a non-specific gRNA (gRNA-H4) were used as controls.

  • After CRISPRa induction with 100 ng/ml aTc, cells were harvested for RNA extraction following the RNAsnap protocol (Stead et al, 2012 ).
  • After column purification (RNA Clean & Concentrator Kits, Zymo Research), total RNA was reverse-transcribed into cDNA using random hexmers (SuperScript III Reverse Transcriptase, Invitrogen).
  • Quantitative PCR was performed on each sample with gene-specific primers (Appendix Table S6) using the KAPA SYBR FAST qPCR Master Mix (Kapa Biosystems). The rrsA gene was selected as the housekeeping gene to normalize expression between samples.

For whole-transcriptome analysis of CRISPRa specificity,

  • we extracted total RNA from the samples as described above and depleted rRNAs using Ribo-Zero rRNA removal-Bacteria kit (Illumina).
  • RNA libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit (New England BioLabs) and sequenced on the Illumina NextSeq platform (Mid-Output Kit, 150 cycles).
  • The raw reads were aligned to the reference genome (BW25113) using Bowtie 2 (Langmead & Salzberg, 2012 ), and the read counts of each gene were quantified by HTseq (Anders et al, 2015 ). Expression level of individual genes was normalized by total read counts within each sample.

Screening for CRISPRa-mediated inducible promoters

The Metagenomic promoter library (RS7003) was derived from Johns et al ( 2018 ).

  • About 8,000 regulatory elements were transformed to cells expressing dCas9-AsiA_m2.1 and either gRNA-H22, gRNA-H23, or genomic targeting gRNA-H24 (Appendix Table S5).
  • After CRISPRa induction, four biological replicates were harvested to measure promoter activity. A constitutive promoter without CRISPRa induction (Appendix Table S7) was spiked in the cell populations for normalizing expression levels between samples.
  • Total RNA was extracted and purified as previously described. Gene-specific primers were used for cDNA generation (Maxima reverse transcriptase, Thermo Scientific), and RNA sequencing library was prepared by ligation with the common adaptor primer for downstream sequencing (Yim et al, 2019 ).
  • To quantify abundance of each promoter in the library, plasmid DNA from each sample was also extracted using PrepGem bacteria kit (MicroGEM) and used to generate a DNA amplicon sequencing library.
  • Both RNA and DNA libraries were sequenced on the Illumina NextSeq platform (Mid-output kit, 300 cycles).
  • Sequencing reads from DNA and RNA libraries were merged by BBmerge and filtered out low-quality reads (Bushnell et al, 2017 ).
  • Custom pipeline that was previously described (Yim et al, 2019 ) was adopted to identify sequencing reads corresponding to each promoter. Expression level of each promoter was quantified by determining the ratio of RNA abundance over DNA abundance. To compare across samples, expression levels were normalized to the same spiked-in control promoter in each sample. Fold change in CRISPRa induced gene expression was calculated by dividing by the reporter expression of control cells containing dCas9-AsiA_m2.1 and a genomic targeting gRNA-H24.

Biology - Bacteria

Bacteria normally comprises a large number of prokaryotic microorganisms.

Bacteria most probably were among the first life that formed to appear on the Earth.

Bacteria belong to Monera kingdom.

Bacteria usually inhabit in all range of environments, such as soil, water, acidic hot springs, radioactive waste, and the deep portions of Earth's crust.

The study of bacteria is known as bacteriology.

Bacteria play an important role in many stages of the nutrient cycle by recycling nutrients including the fixation of nitrogen from the atmosphere.

Bacteria grow to a fixed size and after maturity reproduce through asexual reproduction i.e. basically binary fission.

Under favorable conditions, bacteria can grow and divide very swiftly, and the bacterial populations can double merely in every 9.8 minutes.

When viruses that infect bacteria is known as Bacteriophages.

In order to modify themselves (to survive in the adverse environment), Bacteria frequently secrete chemicals into their environment.


Bacteria utilize diffusible signals to regulate population density-dependent coordinated gene expression in a process called quorum sensing (QS). While the intracellular regulatory mechanisms of QS are well-understood, the effect of spatiotemporal changes in the population configuration on the sensitivity and robustness of the QS response remains largely unexplored. Using a microfluidic device, we quantitatively characterized the emergent behavior of a population of swimming E. coli bacteria engineered with the lux QS system and a GFP reporter. We show that the QS activation time follows a power law with respect to bacterial population density, but this trend is disrupted significantly by microscale variations in population configuration and genetic circuit noise. We then developed a computational model that integrates population dynamics with genetic circuit dynamics to enable accurate (less than 7% error) quantitation of the bacterial QS activation time. Through modeling and experimental analyses, we show that changes in spatial configuration of swimming bacteria can drastically alter the QS activation time, by up to 22%. The integrative model developed herein also enables examination of the performance robustness of synthetic circuits with respect to growth rate, circuit sensitivity, and the population’s initial size and spatial structure. Our framework facilitates quantitative tuning of microbial systems performance through rational engineering of synthetic ribosomal binding sites. We have demonstrated this through modulation of QS activation time over an order of magnitude. Altogether, we conclude that predictive engineering of QS-based bacterial systems requires not only the precise temporal modulation of gene expression (intracellular dynamics) but also accounting for the spatiotemporal changes in population configuration (intercellular dynamics).

Vector Biology and Bacterial Pathogens

The Laboratory for Vector Biology and Bacterial Pathogens has two primary focus areas:

Utilizing Molecular Tools, Functional Genomics, and Animal Models to Investigate Vector-Pathogen Interactions

This project builds on recent work reporting tick transmission of relapsing fever spirochetes and genome sequencing of both the pathogen and tick vector. We have utilized both next generation and third generation sequencing technologies to investigate a number of outstanding questions in the field. We have also performed transcriptional studies on the tick to further understand how relapsing fever spirochetes adapt to the arthropod vector. Our goal by utilizing whole genome and transcriptomic analyses is to better understand vector-pathogens interactions and identify novel areas of intervention for both the vector and pathogen.

Defining the Ecology of Pathogens Transmitted by Argasid Ticks

Argasid (soft bodied) ticks not only transmit relapsing fever spirochetes but also African swine fever virus, an emerging and highly contagious pathogen with high mortality rates in domestic pigs. Through multi institutional collaborations, we utilize diagnostic assays to evaluate mammalian exposure to soft ticks and the pathogens they transmit. Moreover, we are investigating the distribution of argasids through population genetic studies and their maintenance in nature. These projects will define the disease burden and ecology in regions of the globe where the pathogens are ignored.