Background Anetoderma is a benign condition with focal loss of dermal elastic tissue resulting in localized areas of flaccid or herniated saclike skin. Currently, anetoderma is classified as either primary (idiopathic), or secondary anetoderma (which is associated with a variety of skin conditions, penicillamine use, or neonatal prematurity). Lesions appear on the upper arms, trunk, and thighs.
Background
Anetoderma is a benign condition with focal loss of dermal elastic tissue resulting in localized areas of flaccid or herniated saclike skin. Currently, anetoderma is classified as either primary (idiopathic), or secondary anetoderma (which is associated with a variety of skin conditions, penicillamine use, or neonatal prematurity). Lesions appear on the upper arms, trunk, and thighs.Case presentation We report a 14-year-old boy, which was noticed to have had multiple, white, non-pruritic areas on the acral sites of upper and lower extremities for two years. In physical examination, the patient had normal mental development. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 cm in diameter, and with central protrusion, with distribution on dorsal part of the index finger, forearms, distal portion of thighs and calves. Lesions were detected neither on the trunk nor the proximal areas of extremities. There are no sensory changes associated with the lesions. Otherwise, his general health was good. He did not have any medication consumption history. Family history was negative. Laboratory examinations were within normal limits. Skin biopsy from one of his lesions was done, that confirmed the diagnosis of anetoderma.
Case presentation
We report a 14-year-old boy, which was noticed to have had multiple, white, non-pruritic areas on the acral sites of upper and lower extremities for two years. In physical examination, the patient had normal mental development. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 cm in diameter, and with central protrusion, with distribution on dorsal part of the index finger, forearms, distal portion of thighs and calves. Lesions were detected neither on the trunk nor the proximal areas of extremities. There are no sensory changes associated with the lesions. Otherwise, his general health was good. He did not have any medication consumption history. Family history was negative. Laboratory examinations were within normal limits. Skin biopsy from one of his lesions was done, that confirmed the diagnosis of anetoderma.Conclusions In summary, we report a case of anetoderma on unusual sites of the skin. We could not find similar reports of anetoderma developing on distal extremities without involvement of the upper trunk and proximal arms, in the medical literature.
Conclusions
In summary, we report a case of anetoderma on unusual sites of the skin. We could not find similar reports of anetoderma developing on distal extremities without involvement of the upper trunk and proximal arms, in the medical literature.

Background The term anetoderma (anetos = slack) refers to a circumscribed area of slack skin associated with a loss of dermal substance on palpation and a loss of elastic tissue on histological examination [ 1 ]. In the past, cases of primary anetoderma were divided into the Jadassohn-Pellizari type, in which the lesions are preceded by erythema or urticaria, and the Schweninger-Buzzi type, in which there are no preceding inflammatory lesions. This is now of historical interest only, because in the same patient some lesions may be preceded by inflammation and the others may not, and the prognosis and histology are identical in the two types [ 2 , 3 ]. Anetoderma is a rare disorder that in the most usual form develop on the trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere. The scalp, palms and soles are usually spared. We report a patient with anetoderma whose lesions present on distal extremities consisting of hands and calves.
Background
The term anetoderma (anetos = slack) refers to a circumscribed area of slack skin associated with a loss of dermal substance on palpation and a loss of elastic tissue on histological examination [ 1 ].In the past, cases of primary anetoderma were divided into the Jadassohn-Pellizari type, in which the lesions are preceded by erythema or urticaria, and the Schweninger-Buzzi type, in which there are no preceding inflammatory lesions. This is now of historical interest only, because in the same patient some lesions may be preceded by inflammation and the others may not, and the prognosis and histology are identical in the two types [ 2 , 3 ].Anetoderma is a rare disorder that in the most usual form develop on the trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere.The scalp, palms and soles are usually spared. We report a patient with anetoderma whose lesions present on distal extremities consisting of hands and calves.Case presentation A 14-year-old boy was noticed to have had multiple white, non pruritic area on his distal extremities for two years. The lesions consisted of whitish papules and depressed areas with central protrusion. On clinical examination, the otherwise healthy looking patient's general appearance and mental state had lumbar lordosis, laxity in large joints, tibia vara, high-arched palate, and dental misalignment. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 centimeter in diameter, and with central protrusion, that were distributed on the dorsum of fingers (Fig  1 ), forearms (Fig  2 ), distal portion of the thighs and on the calves (Fig  3 ). No lesions on the trunk or proximal areas of extremities were detected. Palms, soles, dorsum of feet and mucosal membranes were spared. No sensory changes associated with the lesions. He did not have any history of medication consumption. Family history was negative. Figure 1 Solitary lesion over dorsum of left index finger. Figure 2 An anetodermic lesion on forearm. Figure 3 An anetodermic lesion on lateral aspect of left lower leg (before cryotherapy) Figure 4 Anetoderma lesions some minute after cryotherapy Laboratory examinations consisting of complete blood count, urinalysis, and blood chemistry including erythrocyte sedimentation rate and liver function tests were within normal limits. Antinuclear antibody was negative. The patient had no risk factor for AIDS or syphilis, so we did not request HIV or VDRL test. Hepatitis B surface antigens were not detected. Immunological assays consisting of IgM, IgA, IgG, IgE, C3, and C4 levels were normal. An induration of 0.5 centimeter in diameter was observed after tuberculin testing. Chest x-ray film was normal. The skin biopsy was done. Haematoxylin and eosin stained section showed faintly eosinophilic separated collagen fibers in the upper and mid-dermis (Fig  6 ). Verhoeff-vanGieson stained sections showed a marked decrease or in some areas total absence of elastic fibres, in both superficial and mid-dermis. Elastic fibres around the vessels in the affected areas are also fragmented and markedly decreased (Fig  7 , 8 ). We tried to treat the patient with liquid nitrogen cryotherapy by means of cotton-tip applicator for two freeze-thaw cycles (freeze time, 10–15 seconds), in 6–8 sessions weekly; which obtained moderate improvement in some early-onset lesions with no frank atrophy (Fig  4  and  5 ). Figure 5 The same view as Fig. 4 after several sessions of cryotherapy (the 2 lower lesions near completely been resolved). Figure 6 Faintly eosinophilic separated collagen bundles in upper dermis (H&E ×250). Figure 7 Severely decreased elastic fibres in superficial and mid dermis (Verhoeff-van Gieson stain ×250). Figure 8 Severely decreased elastic fibres in superficial dermis (Verhoeff-van Gieson stain ×400).
Case presentation
A 14-year-old boy was noticed to have had multiple white, non pruritic area on his distal extremities for two years. The lesions consisted of whitish papules and depressed areas with central protrusion.On clinical examination, the otherwise healthy looking patient's general appearance and mental state had lumbar lordosis, laxity in large joints, tibia vara, high-arched palate, and dental misalignment. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 centimeter in diameter, and with central protrusion, that were distributed on the dorsum of fingers (Fig  1 ), forearms (Fig  2 ), distal portion of the thighs and on the calves (Fig  3 ). No lesions on the trunk or proximal areas of extremities were detected. Palms, soles, dorsum of feet and mucosal membranes were spared. No sensory changes associated with the lesions. He did not have any history of medication consumption. Family history was negative.Laboratory examinations consisting of complete blood count, urinalysis, and blood chemistry including erythrocyte sedimentation rate and liver function tests were within normal limits. Antinuclear antibody was negative. The patient had no risk factor for AIDS or syphilis, so we did not request HIV or VDRL test. Hepatitis B surface antigens were not detected. Immunological assays consisting of IgM, IgA, IgG, IgE, C3, and C4 levels were normal. An induration of 0.5 centimeter in diameter was observed after tuberculin testing. Chest x-ray film was normal. The skin biopsy was done. Haematoxylin and eosin stained section showed faintly eosinophilic separated collagen fibers in the upper and mid-dermis (Fig  6 ). Verhoeff-vanGieson stained sections showed a marked decrease or in some areas total absence of elastic fibres, in both superficial and mid-dermis. Elastic fibres around the vessels in the affected areas are also fragmented and markedly decreased (Fig  7 , 8 ). We tried to treat the patient with liquid nitrogen cryotherapy by means of cotton-tip applicator for two freeze-thaw cycles (freeze time, 10–15 seconds), in 6–8 sessions weekly; which obtained moderate improvement in some early-onset lesions with no frank atrophy (Fig  4  and  5 ).Discussion Anetoderma, which was first described by Jadassohn in 1892, is characterized by localized areas of loss of substance and elastic tissue with flaccid skin and often leads to a herniation phenomenon [ 2 ]. We could not find similar reports (other than anetoderma-like changes on distal extremities secondary to hamartomatous congenital melanocytic naevi) [ 4 ] of anetoderma developing on distal extremities without involvement of the upper trunk and proximal arms, in the medical literature. This rare disorder occurs mainly in women aged 20–40 years, but is occasionally reported in younger and older patients of both sexes. It is perhaps more frequent in central Europe than elsewhere, which suggests a possible relationship to chronic atrophic acrodermatitis (due to  Borrelia  species) in some cases. In the most usual form, crops of round or oval, pink macules 0.5–1 centimeter in diameter develop on trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere[ 3 ]. The scalp, palms and soles are usually spared. Each macule extends for a week or two to reach the size of 2–3 centimeter [ 3 ]. Sometimes there are larger plaques of erythema, and nodules have also been reported as primary lesion [ 5 ]. The number of lesions varies widely, from less than five to one hundred or more [ 3 ]. The lesions remain unchanged throughout life, and new lesions often continue to develop for many years. If the lesions coalesce, they form large atrophic areas, which are indistinguishable from acquired cutis laxa [ 3 ]. They may become confluent, to cover large areas, especially at the roots of the limbs and on the neck [ 3 ]. Although infrequently reported, anetoderma may occur in families, and patient must be examined for associated systemic abnormalities for thorough assessment of their skin disorders. In familial anetoderma, there were associated ocular, gastrointestinal or orthopedic anomalies in the affected patients or in any other family members, but causes without them have been reported [ 8 ]. Although isolated and perhaps coincidental, these abnormalities could be related to the same process that produces the lesions of anetoderma [ 3 ]. Primary anetoderma can be inherited, but it has also been described in association with prematurity, lupus erythematosus, antiphospholipid syndrome, and with decreased serum levels of alpha-1-antitrypsin [ 6 , 7 , 9 , 10 ] and [ 11 ]. Secondary anetoderma develops over other dermatoses are shown in Table  1 . Table 1 Dermatoses Associated with Anetoderma Syphilis [3] Sarcoidosis [17] Granuloma annulare [25] Tuberculosis [3] Acne vulgaris [17] Hepatitis B virus immunization [26] Xanthomas [3] Leprosy [17] Primary Sjogren's syndrome [27] Nodular amyloidosis [3] Lupus erythematosus [17, 21] Lichen planus [28] Melanocytic naevi [4] Pilomatricoma [18, 19] Insect bites [28] Low serum level of α-1-antitrypsin [6] Prurigo nodularis [20] Lupus profundus [30, 31] Antiphospholipid syndrome [6, 11, 1nd 24] Cutaneous plasmacytoma [21] Discoid lupus (with herediyary C2 defficiency) [32] Recurrent deep vein thrombosis [7] Benign cutaneous lymphoid hyperplasia [21] Pityriasis versicolor [33] History of Graves' disease [7] Urticaria pigmentosa [22, 23] Dermatofibroma [34] Familial type [8] Perifolliculitis [23] Penicillamine-induced [34, 40] Prematurity [10] Varicella [24] HIV-infection [42] The differential diagnosis of anetoderma includes other focal dermal atrophies and miscellaneous diseases that must be differentiated from the skin herniation phenomenon of anetoderma [ 12 ], are shown in Table  2 . Table 2 Differential Diagnosis of Anetoderma [12-16] Atrophic scars Discoid lupus erythematosus Lichen sclerosus et atrophicus Atrophoderma of Pasini and Pierini Corticosteroid-induced atrophy Perifollicular macular atrophy Morphea Perifollicular atrophoderma Atrophoderma vermiculare Striae distensae Focal dermal hypoplasia Naevus lipomatosus Connective tissue naevus Neurofibromas Cutis laxa postinflammatory elastolysis mid-dermal elastolysis Granulomatous slack skin acrodermatitis chronica atrophicans Atrophoderma of Pasini and Pierini is a major source of confusion both etymologically and clinically. Patients have larger lesions with a sharp peripheral border dropping into a depression with no outpouching. On biopsy, elastin is normal, while collagen may be thickened, but this finding is difficult to quantify [ 12 ]. Perifollicular atrophoderma is most prominent on the dorsa of the hands and often is associated with multiple basal cell carcinomas and hair abnormalities in the Bazex syndrome [ 13 ]. Perifollicular atrophy also has been described in extreme forms of keratosis pilaris, in which large keratin plugs may produce a dilated patulous follicle. This condition usually found on the cheeks of young children. Both of these lesions mimic perifollicular anetoderma but lack elastin changes [ 12 ]. In focal dermal hypoplasia thinning or absence of dermis, rather than changes in elastin fibres, accounts for the proximity of the subcutis to the epidermis [ 12 ]. Cutis laxa, postinflammatory elastolysis [ 14 ], and mid-dermal elastolysis [ 15 ] share with anetoderma the property of cryptogenic loss of elastic fibres. Elastase-producing strains of staphylococcus epidermidis have been held responsible for perifollicular macular atrophy. Anetoderma has also been reported in 5 patients with false-positive syphilis serology, 3 of who also fulfilled the criteria for the antiphospholipid syndrome [ 29 ]. Its pathogenesis is not yet clearly established, but immunological mechanisms could play an important role in dermal elastolysis [ 35 ]. The association of primary anetoderma with decreased levels of alpha-1-antitrypsin may be of significance: Alpha-1-antitrypsin inhibits neutrophil elastase and its reduction may cause increased elastic activity and elastin breakdown. Phagocytosis of elastic fibres by macrophages has been found in primary anetoderma [ 36 ]. No antibodies have been demonstrated against elastic fibres [ 37 ]. Venencie et al. [ 38 ], suggested that the degradation of elastic fibres in patients with anetoderma is caused by enhanced expression of progelatinases A and B and production of the activated form of gelatinase A, and that the lack of control of these enzymes by tissue inhibitors of metalloproteinases is probably a key factor in the development and duration of anetodermic lesions. Ghomrasseni et al. [ 39 ], demonstrated that for the five samples of anetodermic skin, matrix metalloproteinase-1 (MMP-1) levels were significantly higher compared with the uninvolver cultures and the healthy samples. A significant increase of tissue inhibitors of metalloproteinase (TIMP-1) expression was also observed in the affected cultures of explants. The study demonstrated a significant increase in the production of gelatinase A (MMP-2), and no significant production of TIMP-2 in lesional skin compared with the samples from the two healthy donors. Penicillamine-induced anetoderma has also been reported [ 3 , 34 ] and [ 40 ]. Penicillin and the antifibrinolytic drug ε-aminocaproic acid have been advocated [ 41 ], but Venencie et al. [ 3 ] studied 16 patients and found that no treatment was beneficial once the atrophy had developed. However, the wrinkled skin appearance in our patient had been present for 2 years, and his lesions did not show any signs of inflammation or pre-existing conditions like melanocytic naevi.
Discussion
Anetoderma, which was first described by Jadassohn in 1892, is characterized by localized areas of loss of substance and elastic tissue with flaccid skin and often leads to a herniation phenomenon [ 2 ]. We could not find similar reports (other than anetoderma-like changes on distal extremities secondary to hamartomatous congenital melanocytic naevi) [ 4 ] of anetoderma developing on distal extremities without involvement of the upper trunk and proximal arms, in the medical literature.This rare disorder occurs mainly in women aged 20–40 years, but is occasionally reported in younger and older patients of both sexes. It is perhaps more frequent in central Europe than elsewhere, which suggests a possible relationship to chronic atrophic acrodermatitis (due to  Borrelia  species) in some cases. In the most usual form, crops of round or oval, pink macules 0.5–1 centimeter in diameter develop on trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere[ 3 ]. The scalp, palms and soles are usually spared. Each macule extends for a week or two to reach the size of 2–3 centimeter [ 3 ]. Sometimes there are larger plaques of erythema, and nodules have also been reported as primary lesion [ 5 ]. The number of lesions varies widely, from less than five to one hundred or more [ 3 ]. The lesions remain unchanged throughout life, and new lesions often continue to develop for many years. If the lesions coalesce, they form large atrophic areas, which are indistinguishable from acquired cutis laxa [ 3 ]. They may become confluent, to cover large areas, especially at the roots of the limbs and on the neck [ 3 ].BorreliaAlthough infrequently reported, anetoderma may occur in families, and patient must be examined for associated systemic abnormalities for thorough assessment of their skin disorders. In familial anetoderma, there were associated ocular, gastrointestinal or orthopedic anomalies in the affected patients or in any other family members, but causes without them have been reported [ 8 ]. Although isolated and perhaps coincidental, these abnormalities could be related to the same process that produces the lesions of anetoderma [ 3 ].Primary anetoderma can be inherited, but it has also been described in association with prematurity, lupus erythematosus, antiphospholipid syndrome, and with decreased serum levels of alpha-1-antitrypsin [ 6 , 7 , 9 , 10 ] and [ 11 ]. Secondary anetoderma develops over other dermatoses are shown in Table  1 .Dermatoses Associated with AnetodermaThe differential diagnosis of anetoderma includes other focal dermal atrophies and miscellaneous diseases that must be differentiated from the skin herniation phenomenon of anetoderma [ 12 ], are shown in Table  2 .Differential Diagnosis of Anetoderma [12-16]Atrophoderma of Pasini and Pierini is a major source of confusion both etymologically and clinically. Patients have larger lesions with a sharp peripheral border dropping into a depression with no outpouching. On biopsy, elastin is normal, while collagen may be thickened, but this finding is difficult to quantify [ 12 ]. Perifollicular atrophoderma is most prominent on the dorsa of the hands and often is associated with multiple basal cell carcinomas and hair abnormalities in the Bazex syndrome [ 13 ]. Perifollicular atrophy also has been described in extreme forms of keratosis pilaris, in which large keratin plugs may produce a dilated patulous follicle. This condition usually found on the cheeks of young children. Both of these lesions mimic perifollicular anetoderma but lack elastin changes [ 12 ]. In focal dermal hypoplasia thinning or absence of dermis, rather than changes in elastin fibres, accounts for the proximity of the subcutis to the epidermis [ 12 ]. Cutis laxa, postinflammatory elastolysis [ 14 ], and mid-dermal elastolysis [ 15 ] share with anetoderma the property of cryptogenic loss of elastic fibres.Elastase-producing strains of staphylococcus epidermidis have been held responsible for perifollicular macular atrophy. Anetoderma has also been reported in 5 patients with false-positive syphilis serology, 3 of who also fulfilled the criteria for the antiphospholipid syndrome [ 29 ]. Its pathogenesis is not yet clearly established, but immunological mechanisms could play an important role in dermal elastolysis [ 35 ]. The association of primary anetoderma with decreased levels of alpha-1-antitrypsin may be of significance: Alpha-1-antitrypsin inhibits neutrophil elastase and its reduction may cause increased elastic activity and elastin breakdown. Phagocytosis of elastic fibres by macrophages has been found in primary anetoderma [ 36 ]. No antibodies have been demonstrated against elastic fibres [ 37 ].Venencie et al. [ 38 ], suggested that the degradation of elastic fibres in patients with anetoderma is caused by enhanced expression of progelatinases A and B and production of the activated form of gelatinase A, and that the lack of control of these enzymes by tissue inhibitors of metalloproteinases is probably a key factor in the development and duration of anetodermic lesions.Ghomrasseni et al. [ 39 ], demonstrated that for the five samples of anetodermic skin, matrix metalloproteinase-1 (MMP-1) levels were significantly higher compared with the uninvolver cultures and the healthy samples. A significant increase of tissue inhibitors of metalloproteinase (TIMP-1) expression was also observed in the affected cultures of explants. The study demonstrated a significant increase in the production of gelatinase A (MMP-2), and no significant production of TIMP-2 in lesional skin compared with the samples from the two healthy donors.Penicillamine-induced anetoderma has also been reported [ 3 , 34 ] and [ 40 ]. Penicillin and the antifibrinolytic drug ε-aminocaproic acid have been advocated [ 41 ], but Venencie et al. [ 3 ] studied 16 patients and found that no treatment was beneficial once the atrophy had developed. However, the wrinkled skin appearance in our patient had been present for 2 years, and his lesions did not show any signs of inflammation or pre-existing conditions like melanocytic naevi.Conclusions In summary, we report a case of anetoderma with lesions on unusual sites. We did not find similar reports of acral anetoderma in the medical literature. According to this paper liquid nitrogen cryotherapy has moderate efficacy in the treatment of some of the early anetoderma lesions, without frank atrophy.
Conclusions
In summary, we report a case of anetoderma with lesions on unusual sites. We did not find similar reports of acral anetoderma in the medical literature.According to this paper liquid nitrogen cryotherapy has moderate efficacy in the treatment of some of the early anetoderma lesions, without frank atrophy.Competing interest None declared.
Competing interest
None declared.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Background While the pathogenesis and epidemiology of tuberculosis are well studied, relatively little is known about the evolution of the infectious agent  Mycobacterium tuberculosis , especially at the within-host level. The insertion sequence IS 6110  is a genetic marker that is widely used to track the transmission of tuberculosis between individuals. This and other markers may also facilitate our understanding of the disease within patients.
Background
While the pathogenesis and epidemiology of tuberculosis are well studied, relatively little is known about the evolution of the infectious agent  Mycobacterium tuberculosis , especially at the within-host level. The insertion sequence IS 6110  is a genetic marker that is widely used to track the transmission of tuberculosis between individuals. This and other markers may also facilitate our understanding of the disease within patients.Mycobacterium tuberculosis6110Results This article presents three lines of evidence supporting the action of positive selection on  M. tuberculosis  within patients. The arguments are based on a comparison between empirical findings from molecular epidemiology, and population genetic models of evolution. Under the hypothesis of neutrality of genotypes, 1) the mutation rate of the marker IS 6110  is unusually high, 2) the time it takes for substitutions to occur within patients is too short, and 3) the amount of polymorphism within patients is too low.
Results
This article presents three lines of evidence supporting the action of positive selection on  M. tuberculosis  within patients. The arguments are based on a comparison between empirical findings from molecular epidemiology, and population genetic models of evolution. Under the hypothesis of neutrality of genotypes, 1) the mutation rate of the marker IS 6110  is unusually high, 2) the time it takes for substitutions to occur within patients is too short, and 3) the amount of polymorphism within patients is too low.M. tuberculosis6110Conclusions Empirical observations are explained by the action of positive selection during infection, or alternatively by very low effective population sizes. I discuss the possible roles of antibiotic treatment, the host immune system and extrapulmonary dissemination in creating opportunities for positive selection.
Conclusions
Empirical observations are explained by the action of positive selection during infection, or alternatively by very low effective population sizes. I discuss the possible roles of antibiotic treatment, the host immune system and extrapulmonary dissemination in creating opportunities for positive selection.

Background How actively do populations of  Mycobacterium tuberculosis  cells undergo adaptive evolution on the spatial and temporal scales of individual infections? On the one hand, the long generation time and limited sequence diversity of this organism might suggest a slow pace of adaptive evolution. On the other hand, the rapidity and ease with which antibiotic resistance is generated during infection suggests otherwise. The physiology and immunology of tuberculosis pathogenesis have been well studied. The infectious agent  M. tuberculosis  is known to invade and replicate within alveolar macrophages. There is a spectrum of responses by the immune system, corresponding to the relative involvement of  Thl  and  Th2  immune cells, which respectively stimulate the cytotoxic response (more effective against infected cells), and the humoral/antibody response (more effective against extracellular pathogens) [ 1 , 2 ]. Some progress has been made in describing the population dynamics of mycobacterial infection quantitatively [ 3 - 5 ]. At the wider spatial and temporal scales of populations, the molecular epidemiology and the evolution of  M. tuberculosis  have been carefully studied. Genotypic data are rapidly accumulating in the molecular epidemiology of infectious diseases. These are usually compiled and summarised to make inferences about the state of an epidemic in a given geographic location, or at the global level. For example, epidemiologists seek to identify risk factors for infection, and to locate particular strains that are especially transmissible or pathogenic [ 6 - 8 ]. The evolutionary history of  M. tuberculosis  has also been characterised. For example, it has been argued that the limited variation at the nucleotide is due to a recent population bottleneck [ 9 ], and that the common ancestor of  Mycobacterium bovis  and  M. tuberculosis  may well have been a human rather than bovine pathogen [ 10 ]. Less understood is the evolution of  M. tuberculosis  at the cellular level  inside  bodies. There has been little integration of the genetic information from markers with the population genetics of the bacterial population within hosts. In this article, I examine data collected for the purposes of molecular epidemiology to present three lines of evidence supporting the action of positive selection on  M. tuberculosis . The data come from the marker IS 6110 , which is currently the standard method of typing tuberculosis isolates. These genotypic data will be considered under assumptions of neutrality, and then under the assumption that positive selection is acting. The case for the action of selection is based on the following three arguments. • Under the assumption of neutrality, the observed mutation (or transposition) rate of the genetic marker IS 6110  is unusually high; the estimated mutation rate is lower if selection is acting. • The observed times associated with change are too low to be explained by neutrality; positive selection lowers the expected substitution time. • The observed level of polymorphism is too low to be explained by neutrality.
Background
How actively do populations of  Mycobacterium tuberculosis  cells undergo adaptive evolution on the spatial and temporal scales of individual infections? On the one hand, the long generation time and limited sequence diversity of this organism might suggest a slow pace of adaptive evolution. On the other hand, the rapidity and ease with which antibiotic resistance is generated during infection suggests otherwise. The physiology and immunology of tuberculosis pathogenesis have been well studied. The infectious agent  M. tuberculosis  is known to invade and replicate within alveolar macrophages. There is a spectrum of responses by the immune system, corresponding to the relative involvement of  Thl  and  Th2  immune cells, which respectively stimulate the cytotoxic response (more effective against infected cells), and the humoral/antibody response (more effective against extracellular pathogens) [ 1 , 2 ]. Some progress has been made in describing the population dynamics of mycobacterial infection quantitatively [ 3 - 5 ]. At the wider spatial and temporal scales of populations, the molecular epidemiology and the evolution of  M. tuberculosis  have been carefully studied. Genotypic data are rapidly accumulating in the molecular epidemiology of infectious diseases. These are usually compiled and summarised to make inferences about the state of an epidemic in a given geographic location, or at the global level. For example, epidemiologists seek to identify risk factors for infection, and to locate particular strains that are especially transmissible or pathogenic [ 6 - 8 ]. The evolutionary history of  M. tuberculosis  has also been characterised. For example, it has been argued that the limited variation at the nucleotide is due to a recent population bottleneck [ 9 ], and that the common ancestor of  Mycobacterium bovis  and  M. tuberculosis  may well have been a human rather than bovine pathogen [ 10 ].Mycobacterium tuberculosisM. tuberculosisThlTh2M. tuberculosisM. tuberculosisMycobacterium bovisM. tuberculosisLess understood is the evolution of  M. tuberculosis  at the cellular level  inside  bodies. There has been little integration of the genetic information from markers with the population genetics of the bacterial population within hosts. In this article, I examine data collected for the purposes of molecular epidemiology to present three lines of evidence supporting the action of positive selection on  M. tuberculosis . The data come from the marker IS 6110 , which is currently the standard method of typing tuberculosis isolates. These genotypic data will be considered under assumptions of neutrality, and then under the assumption that positive selection is acting. The case for the action of selection is based on the following three arguments.M. tuberculosisinsideM. tuberculosis6110• Under the assumption of neutrality, the observed mutation (or transposition) rate of the genetic marker IS 6110  is unusually high; the estimated mutation rate is lower if selection is acting.6110• The observed times associated with change are too low to be explained by neutrality; positive selection lowers the expected substitution time.• The observed level of polymorphism is too low to be explained by neutrality.Results: Models and observations In each of the following sections a comparison is made between the strictly neutral model and a generalised model including selection through a single parameter  s  (described in the Appendix). Although the analyses start with strict neutrality ( s  = 0) in each argument, alleles for which  s  < 1/ N , where  N  is the effective population size, can be considered  nearly neutral , in that the effects of drift outweigh the force of selection [ 11 ]. In each case, explaining observations in this range of selective coefficients requires very low effective population sizes. Transposition rates of IS6110 When genetic mutations are selectively neutral, the substitution rate is equal to the mutation rate [ 11 ]. In the present case, the  within-host  substitution process is of interest. Rosenberg  et al.  [ 12 ] determined the within-host substitution rate of the  IS6110  marker to be around 0.00184 to 0.0390 events per copy per year, with the maximum likelihood estimate at 0.0287. Under neutrality, therefore, this rate corresponds to a  per insertion  mutation rate of  μ i  ~ 7.9 × 10 -5  events per site per generation, assuming a generation time of 1 day in active infections. This figure comes from a measured doubling time of close to 24 hours, based on clinical isolates grown in human monocyte cultures and in culture media [ 13 - 15 ]. Rates of point mutation (events  per nucleotide  per generation) are usually in the vicinity of 10 -9 . In mutator strains, that is, genomes in which the DNA repair machinery is damaged, leading to elevated mutation rates, the mutation rate rises orders of magnitude, up to ~10 -7  – 10 -6  [ 16 ]. The mutation rate of IS 6110  under neutrality therefore seems suspiciously high, although this is only "circumstantial evidence", since it is not inherently problematic. Indeed, mutation rates as high as 10 -4  per element per generation have been measured for IS 10 in vitro  [ 17 ]. Nevertheless, if positive selection is allowed the estimated mutation rate decreases. Leaving aside the complicating influence of clonal interference [ 18 ], the rate of substitution is K  =  uN μ i      (1) where  u  is the probability of fixation of a mutant,  μ i  is the mutation rate and  N  is the population size [ 11 ]. An estimate of the mutation rate when mutants have advantage  s  is   =  K /( uN ). The diffusion model of drift provides an expression for  u  as a function of the population size  N  and selective coefficient  s  (see the Appendix). Figure  1  plots   over  s  for a few different values of  N . In each curve the estimated mutation rate decreases as the selective coefficient rises. According to this analysis, lower mutation rates are possible when there is some selection and a large population size, or when selection is strong and the population size is small. Note that the estimated mutation rate remains high if mutations are nearly neutral. Figure 1 Estimate of mutation rate when positive selection is acting.  The estimate   is plotted on a logarithmic scale in base 10. Solid curve:  N  = 10; Dashed;  N  = 1000; Dotted:  N  = 10 5 . Fixation times Various studies have measured the stability of IS 6110  as a genetic marker by examining genotypes of serial isolates from patients with persistent infection. A small number of changes in the genotypes between serial isolates indicates a stable marker. Differences in genotypes due to exogeneous reinfection by unrelated strains are excluded from consideration. In the data of Niemann  et al.  [ 19 ] and Rosenberg  et al.  [ 12 ], the median time interval associated with changes in IS 6110  genotypes from serial samples of  M. tuberculosis  is 212 days, and the maximum is 683 days. Because the second sample is taken some time after fixation of the mutant, the actual substitution times are unknown, but they were clearly all under 683 days. I will now show that the expected substitution times under strict neutrality are well in excess of this value.  Let us start with the assumption that the expected time for substitution to occur is the average time taken for the successful mutant to appear plus the time taken for that mutant to reach fixation conditional on its eventual fixation. (I will later drop the assumption about waiting for the mutant to appear). The average appearance time is 1/( μNu ) = 1/ μ  since  u  = 1/ N  under strict neutrality. The average time for a successful neutral mutant to reach fixation is 4 N  generations. The mutation rate of interest in this context is the rate  per genome  per generation, since what is of concern is whether any of the elements in a given genome produce change. For simplicity, assume that the genomic mutation rate scales linearly with copy number. (At the resolution of this analysis, this is a reasonable approximation.) Considering a typical strain has 10 copies of the IS element, the relevant mutation rate here is  μ  =  μ i  × 10 = 7.9 × 10 -4 . Therefore, for  N  = 10, 10 3 , 10 5 , the expected substitution times are roughly 1300, 5300, 4 × 10 5  generations, respectively. With the generation time set to one day, the upper bound of observed substitution times was 683 generations, which is well below theoretical expectations. Now consider the possibility of positive selection under two alternative conservative assumptions. The earlier assumption that there are no successful mutants at the time of the first sample is favourable to the parental strain. A more conservative approach (favouring mutants) would be to say that the mutant destined to reach fixation appears exactly at the time of the first sample. We can then ask how long it takes on average for this mutant to reach fixation if it is positively selected. An even more conservative model would be that not only is the successor strain present at the time of the first sample, but is present at a frequency of 30%. Furthermore, let us say the subdominant strain only needs to be at 70% at the time of the second sample to be considered to have replaced the parental strain. A model of the sojourn times of alleles in populations conditional on fixation must now be specified. Again using the diffusion model of drift (see Appendix), the mean time spent by a mutant in the range of frequencies ( a, b ) (provided  a  is greater than the initial frequency), conditional on fixation, was found by Ewens [ 20 ] and by Maruyama [ 21 ] to be Figure  2  shows the two conservative models, corresponding to two different boundary values for ( a, b ). Even in the extremely conservative model shown in the right-hand plot, the effective population size must be below 400 in order to explain the observed substitution times under strict neutrality. The data are difficult to account for even in terms of nearly neutral mutations ( s  < 1/ N ) and an effective population size of  N  = 1000. The alternative explanation is that the effective population size is larger, but positive selection is acting to make changes sweep through the population faster. Figure 2 Mean sojourn times as functions of selective coefficient  s , for different values of  N .  Left: from  a  = 1/ N  to  b  = 1 - 1/ N ; Right: from  a  = 0.3 to  b  = 0.7. Polymorphism Many analyses of pathogen genotypes assume isolated strains to be clonal, that is, to be monomorphic. This assumption has been scrutinised by De Boer  et al.  [ 22 ], who showed that, in fact, a large proportion (93%) of  M. tuberculosis  isolates are monomorphic using IS 6110  as the marker. They also show that the limits of detection of a second strain are around frequencies of 0.1 to 0.3. More sensitive instruments and refined genotyping procedures are likely to reveal greater polymorphism. The current information can be used, however, to study the population of the organism in hosts by using ranges of  detectable polymorphism . In this section, two ranges will be considered in examining predictions from models: first, 0.1 to 0.9, and second, 0.3 to 0.7. The polymorphism argument rests on the assumption that the isolates reported in [ 22 ] can be viewed as a random sample from a set of populations in mutation-drift equilibrium. It should be noted that because the isolate represents a sample of cells from the patient, it presumably does not always reflect the diversity of cells in the greater within-host population. Thus the polymorphism or heterogeneity observed from isolates is an underestimate of the actual levels. Wright [ 23 ] found the stationary probability distribution of allele frequencies under the diffusion model with mutation and two alleles. Let  f ( x ) be the probability density function of this distribution and  F(x)  be the cumulative probability function  F(x)  (see Appendix). The probability that a given population (patient) is between frequencies  a  and  b  (where  a  < b ) is This quantity can be alternatively interpreted as the proportion of populations observed to be polymorphic according to the detection limits set by ( a, b ). First consider the neutral case. When there is no selection ( s  = 0), the distribution described by  f(x)  is a Beta distribution. Figure  3  shows the probability of an isolate being scored as a polymorphic population, using two alternative detectable polymorphism ranges ( a, b ) = (0.1, 0.9) and (0.3, 0.7), and a mutation rate of  μ  = 7.9 × 10 -4  per cell per generation. Figure 3 Probability of detecting polymorphism in the absence of selection, as a function of  N .  Two different ranges of detectable polymorphism were used. Dashed curve: (0.1, 0.9); dotted: (0.3, 0.7). We use  μ  = 7.9 × 10 -4 . The horizontal bar indicates the observed fraction of polymorphic populations (0.074) from de Boer  et al . [22]. Next, consider the model that includes selection. For the two detectable polymorphism ranges, Figure  4  shows how selective coefficient  s  and effective population size  N  are related to the probability of observing polymorphism. As  s  increases, the predicted polymorphism decreases dramatically, particularly for large  N . Again, an explanation of the observed level of polymorphism is only possibly by setting  N  to be extremely low. Figure 4 Probability of polymorphism as a function of  s .  Left: the detection threshold is set at 0.3; Right: the detection threshold is set at 0.1. The mutation rate is set to  μ  = 7.9 × 10 -4
Results: Models and observations
In each of the following sections a comparison is made between the strictly neutral model and a generalised model including selection through a single parameter  s  (described in the Appendix). Although the analyses start with strict neutrality ( s  = 0) in each argument, alleles for which  s  < 1/ N , where  N  is the effective population size, can be considered  nearly neutral , in that the effects of drift outweigh the force of selection [ 11 ]. In each case, explaining observations in this range of selective coefficients requires very low effective population sizes.sssNNnearly neutralTransposition rates of IS6110 When genetic mutations are selectively neutral, the substitution rate is equal to the mutation rate [ 11 ]. In the present case, the  within-host  substitution process is of interest. Rosenberg  et al.  [ 12 ] determined the within-host substitution rate of the  IS6110  marker to be around 0.00184 to 0.0390 events per copy per year, with the maximum likelihood estimate at 0.0287. Under neutrality, therefore, this rate corresponds to a  per insertion  mutation rate of  μ i  ~ 7.9 × 10 -5  events per site per generation, assuming a generation time of 1 day in active infections. This figure comes from a measured doubling time of close to 24 hours, based on clinical isolates grown in human monocyte cultures and in culture media [ 13 - 15 ]. Rates of point mutation (events  per nucleotide  per generation) are usually in the vicinity of 10 -9 . In mutator strains, that is, genomes in which the DNA repair machinery is damaged, leading to elevated mutation rates, the mutation rate rises orders of magnitude, up to ~10 -7  – 10 -6  [ 16 ]. The mutation rate of IS 6110  under neutrality therefore seems suspiciously high, although this is only "circumstantial evidence", since it is not inherently problematic. Indeed, mutation rates as high as 10 -4  per element per generation have been measured for IS 10 in vitro  [ 17 ]. Nevertheless, if positive selection is allowed the estimated mutation rate decreases. Leaving aside the complicating influence of clonal interference [ 18 ], the rate of substitution is K  =  uN μ i      (1) where  u  is the probability of fixation of a mutant,  μ i  is the mutation rate and  N  is the population size [ 11 ]. An estimate of the mutation rate when mutants have advantage  s  is   =  K /( uN ). The diffusion model of drift provides an expression for  u  as a function of the population size  N  and selective coefficient  s  (see the Appendix). Figure  1  plots   over  s  for a few different values of  N . In each curve the estimated mutation rate decreases as the selective coefficient rises. According to this analysis, lower mutation rates are possible when there is some selection and a large population size, or when selection is strong and the population size is small. Note that the estimated mutation rate remains high if mutations are nearly neutral. Figure 1 Estimate of mutation rate when positive selection is acting.  The estimate   is plotted on a logarithmic scale in base 10. Solid curve:  N  = 10; Dashed;  N  = 1000; Dotted:  N  = 10 5 .
Transposition rates of IS6110
When genetic mutations are selectively neutral, the substitution rate is equal to the mutation rate [ 11 ]. In the present case, the  within-host  substitution process is of interest. Rosenberg  et al.  [ 12 ] determined the within-host substitution rate of the  IS6110  marker to be around 0.00184 to 0.0390 events per copy per year, with the maximum likelihood estimate at 0.0287. Under neutrality, therefore, this rate corresponds to a  per insertion  mutation rate of  μ i  ~ 7.9 × 10 -5  events per site per generation, assuming a generation time of 1 day in active infections. This figure comes from a measured doubling time of close to 24 hours, based on clinical isolates grown in human monocyte cultures and in culture media [ 13 - 15 ]. Rates of point mutation (events  per nucleotide  per generation) are usually in the vicinity of 10 -9 . In mutator strains, that is, genomes in which the DNA repair machinery is damaged, leading to elevated mutation rates, the mutation rate rises orders of magnitude, up to ~10 -7  – 10 -6  [ 16 ]. The mutation rate of IS 6110  under neutrality therefore seems suspiciously high, although this is only "circumstantial evidence", since it is not inherently problematic. Indeed, mutation rates as high as 10 -4  per element per generation have been measured for IS 10 in vitro  [ 17 ]. Nevertheless, if positive selection is allowed the estimated mutation rate decreases. Leaving aside the complicating influence of clonal interference [ 18 ], the rate of substitution iswithin-hostet al.IS6110per insertionμiper nucleotide611010 in vitroK  =  uN μ i      (1)KuN μiwhere  u  is the probability of fixation of a mutant,  μ i  is the mutation rate and  N  is the population size [ 11 ]. An estimate of the mutation rate when mutants have advantage  s  is   =  K /( uN ). The diffusion model of drift provides an expression for  u  as a function of the population size  N  and selective coefficient  s  (see the Appendix). Figure  1  plots   over  s  for a few different values of  N . In each curve the estimated mutation rate decreases as the selective coefficient rises. According to this analysis, lower mutation rates are possible when there is some selection and a large population size, or when selection is strong and the population size is small. Note that the estimated mutation rate remains high if mutations are nearly neutral.uμiNsKuNuNssNFixation times Various studies have measured the stability of IS 6110  as a genetic marker by examining genotypes of serial isolates from patients with persistent infection. A small number of changes in the genotypes between serial isolates indicates a stable marker. Differences in genotypes due to exogeneous reinfection by unrelated strains are excluded from consideration. In the data of Niemann  et al.  [ 19 ] and Rosenberg  et al.  [ 12 ], the median time interval associated with changes in IS 6110  genotypes from serial samples of  M. tuberculosis  is 212 days, and the maximum is 683 days. Because the second sample is taken some time after fixation of the mutant, the actual substitution times are unknown, but they were clearly all under 683 days. I will now show that the expected substitution times under strict neutrality are well in excess of this value.  Let us start with the assumption that the expected time for substitution to occur is the average time taken for the successful mutant to appear plus the time taken for that mutant to reach fixation conditional on its eventual fixation. (I will later drop the assumption about waiting for the mutant to appear). The average appearance time is 1/( μNu ) = 1/ μ  since  u  = 1/ N  under strict neutrality. The average time for a successful neutral mutant to reach fixation is 4 N  generations. The mutation rate of interest in this context is the rate  per genome  per generation, since what is of concern is whether any of the elements in a given genome produce change. For simplicity, assume that the genomic mutation rate scales linearly with copy number. (At the resolution of this analysis, this is a reasonable approximation.) Considering a typical strain has 10 copies of the IS element, the relevant mutation rate here is  μ  =  μ i  × 10 = 7.9 × 10 -4 . Therefore, for  N  = 10, 10 3 , 10 5 , the expected substitution times are roughly 1300, 5300, 4 × 10 5  generations, respectively. With the generation time set to one day, the upper bound of observed substitution times was 683 generations, which is well below theoretical expectations. Now consider the possibility of positive selection under two alternative conservative assumptions. The earlier assumption that there are no successful mutants at the time of the first sample is favourable to the parental strain. A more conservative approach (favouring mutants) would be to say that the mutant destined to reach fixation appears exactly at the time of the first sample. We can then ask how long it takes on average for this mutant to reach fixation if it is positively selected. An even more conservative model would be that not only is the successor strain present at the time of the first sample, but is present at a frequency of 30%. Furthermore, let us say the subdominant strain only needs to be at 70% at the time of the second sample to be considered to have replaced the parental strain. A model of the sojourn times of alleles in populations conditional on fixation must now be specified. Again using the diffusion model of drift (see Appendix), the mean time spent by a mutant in the range of frequencies ( a, b ) (provided  a  is greater than the initial frequency), conditional on fixation, was found by Ewens [ 20 ] and by Maruyama [ 21 ] to be Figure  2  shows the two conservative models, corresponding to two different boundary values for ( a, b ). Even in the extremely conservative model shown in the right-hand plot, the effective population size must be below 400 in order to explain the observed substitution times under strict neutrality. The data are difficult to account for even in terms of nearly neutral mutations ( s  < 1/ N ) and an effective population size of  N  = 1000. The alternative explanation is that the effective population size is larger, but positive selection is acting to make changes sweep through the population faster. Figure 2 Mean sojourn times as functions of selective coefficient  s , for different values of  N .  Left: from  a  = 1/ N  to  b  = 1 - 1/ N ; Right: from  a  = 0.3 to  b  = 0.7.
Fixation times
Various studies have measured the stability of IS 6110  as a genetic marker by examining genotypes of serial isolates from patients with persistent infection. A small number of changes in the genotypes between serial isolates indicates a stable marker. Differences in genotypes due to exogeneous reinfection by unrelated strains are excluded from consideration. In the data of Niemann  et al.  [ 19 ] and Rosenberg  et al.  [ 12 ], the median time interval associated with changes in IS 6110  genotypes from serial samples of  M. tuberculosis  is 212 days, and the maximum is 683 days. Because the second sample is taken some time after fixation of the mutant, the actual substitution times are unknown, but they were clearly all under 683 days. I will now show that the expected substitution times under strict neutrality are well in excess of this value.6110et al.et al.6110M. tuberculosisLet us start with the assumption that the expected time for substitution to occur is the average time taken for the successful mutant to appear plus the time taken for that mutant to reach fixation conditional on its eventual fixation. (I will later drop the assumption about waiting for the mutant to appear). The average appearance time is 1/( μNu ) = 1/ μ  since  u  = 1/ N  under strict neutrality. The average time for a successful neutral mutant to reach fixation is 4 N  generations. The mutation rate of interest in this context is the rate  per genome  per generation, since what is of concern is whether any of the elements in a given genome produce change. For simplicity, assume that the genomic mutation rate scales linearly with copy number. (At the resolution of this analysis, this is a reasonable approximation.) Considering a typical strain has 10 copies of the IS element, the relevant mutation rate here is  μ  =  μ i  × 10 = 7.9 × 10 -4 . Therefore, for  N  = 10, 10 3 , 10 5 , the expected substitution times are roughly 1300, 5300, 4 × 10 5  generations, respectively. With the generation time set to one day, the upper bound of observed substitution times was 683 generations, which is well below theoretical expectations.μNuμuNNper genomeμμiNNow consider the possibility of positive selection under two alternative conservative assumptions. The earlier assumption that there are no successful mutants at the time of the first sample is favourable to the parental strain. A more conservative approach (favouring mutants) would be to say that the mutant destined to reach fixation appears exactly at the time of the first sample. We can then ask how long it takes on average for this mutant to reach fixation if it is positively selected. An even more conservative model would be that not only is the successor strain present at the time of the first sample, but is present at a frequency of 30%. Furthermore, let us say the subdominant strain only needs to be at 70% at the time of the second sample to be considered to have replaced the parental strain.A model of the sojourn times of alleles in populations conditional on fixation must now be specified. Again using the diffusion model of drift (see Appendix), the mean time spent by a mutant in the range of frequencies ( a, b ) (provided  a  is greater than the initial frequency), conditional on fixation, was found by Ewens [ 20 ] and by Maruyama [ 21 ] to bea, baFigure  2  shows the two conservative models, corresponding to two different boundary values for ( a, b ). Even in the extremely conservative model shown in the right-hand plot, the effective population size must be below 400 in order to explain the observed substitution times under strict neutrality. The data are difficult to account for even in terms of nearly neutral mutations ( s  < 1/ N ) and an effective population size of  N  = 1000. The alternative explanation is that the effective population size is larger, but positive selection is acting to make changes sweep through the population faster.a, bsNNPolymorphism Many analyses of pathogen genotypes assume isolated strains to be clonal, that is, to be monomorphic. This assumption has been scrutinised by De Boer  et al.  [ 22 ], who showed that, in fact, a large proportion (93%) of  M. tuberculosis  isolates are monomorphic using IS 6110  as the marker. They also show that the limits of detection of a second strain are around frequencies of 0.1 to 0.3. More sensitive instruments and refined genotyping procedures are likely to reveal greater polymorphism. The current information can be used, however, to study the population of the organism in hosts by using ranges of  detectable polymorphism . In this section, two ranges will be considered in examining predictions from models: first, 0.1 to 0.9, and second, 0.3 to 0.7. The polymorphism argument rests on the assumption that the isolates reported in [ 22 ] can be viewed as a random sample from a set of populations in mutation-drift equilibrium. It should be noted that because the isolate represents a sample of cells from the patient, it presumably does not always reflect the diversity of cells in the greater within-host population. Thus the polymorphism or heterogeneity observed from isolates is an underestimate of the actual levels. Wright [ 23 ] found the stationary probability distribution of allele frequencies under the diffusion model with mutation and two alleles. Let  f ( x ) be the probability density function of this distribution and  F(x)  be the cumulative probability function  F(x)  (see Appendix). The probability that a given population (patient) is between frequencies  a  and  b  (where  a  < b ) is This quantity can be alternatively interpreted as the proportion of populations observed to be polymorphic according to the detection limits set by ( a, b ). First consider the neutral case. When there is no selection ( s  = 0), the distribution described by  f(x)  is a Beta distribution. Figure  3  shows the probability of an isolate being scored as a polymorphic population, using two alternative detectable polymorphism ranges ( a, b ) = (0.1, 0.9) and (0.3, 0.7), and a mutation rate of  μ  = 7.9 × 10 -4  per cell per generation. Figure 3 Probability of detecting polymorphism in the absence of selection, as a function of  N .  Two different ranges of detectable polymorphism were used. Dashed curve: (0.1, 0.9); dotted: (0.3, 0.7). We use  μ  = 7.9 × 10 -4 . The horizontal bar indicates the observed fraction of polymorphic populations (0.074) from de Boer  et al . [22]. Next, consider the model that includes selection. For the two detectable polymorphism ranges, Figure  4  shows how selective coefficient  s  and effective population size  N  are related to the probability of observing polymorphism. As  s  increases, the predicted polymorphism decreases dramatically, particularly for large  N . Again, an explanation of the observed level of polymorphism is only possibly by setting  N  to be extremely low. Figure 4 Probability of polymorphism as a function of  s .  Left: the detection threshold is set at 0.3; Right: the detection threshold is set at 0.1. The mutation rate is set to  μ  = 7.9 × 10 -4
Polymorphism
Many analyses of pathogen genotypes assume isolated strains to be clonal, that is, to be monomorphic. This assumption has been scrutinised by De Boer  et al.  [ 22 ], who showed that, in fact, a large proportion (93%) of  M. tuberculosis  isolates are monomorphic using IS 6110  as the marker. They also show that the limits of detection of a second strain are around frequencies of 0.1 to 0.3. More sensitive instruments and refined genotyping procedures are likely to reveal greater polymorphism. The current information can be used, however, to study the population of the organism in hosts by using ranges of  detectable polymorphism . In this section, two ranges will be considered in examining predictions from models: first, 0.1 to 0.9, and second, 0.3 to 0.7.et al.M. tuberculosis6110detectable polymorphismThe polymorphism argument rests on the assumption that the isolates reported in [ 22 ] can be viewed as a random sample from a set of populations in mutation-drift equilibrium. It should be noted that because the isolate represents a sample of cells from the patient, it presumably does not always reflect the diversity of cells in the greater within-host population. Thus the polymorphism or heterogeneity observed from isolates is an underestimate of the actual levels.Wright [ 23 ] found the stationary probability distribution of allele frequencies under the diffusion model with mutation and two alleles. Let  f ( x ) be the probability density function of this distribution and  F(x)  be the cumulative probability function  F(x)  (see Appendix). The probability that a given population (patient) is between frequencies  a  and  b  (where  a  < b ) isfxF(x)F(x)ababThis quantity can be alternatively interpreted as the proportion of populations observed to be polymorphic according to the detection limits set by ( a, b ).a, bFirst consider the neutral case. When there is no selection ( s  = 0), the distribution described by  f(x)  is a Beta distribution. Figure  3  shows the probability of an isolate being scored as a polymorphic population, using two alternative detectable polymorphism ranges ( a, b ) = (0.1, 0.9) and (0.3, 0.7), and a mutation rate of  μ  = 7.9 × 10 -4  per cell per generation.sf(x)a, bμNext, consider the model that includes selection. For the two detectable polymorphism ranges, Figure  4  shows how selective coefficient  s  and effective population size  N  are related to the probability of observing polymorphism. As  s  increases, the predicted polymorphism decreases dramatically, particularly for large  N . Again, an explanation of the observed level of polymorphism is only possibly by setting  N  to be extremely low.sNsNNDiscussion The three lines of evidence presented in this article suggest positive selection on  M. tuberculosis  within hosts. There are, however, limitations to these analyses. In the first argument there is no inherent problem with finding transposition rates that are high. In the second argument, it is possible to lower the effective population size far enough to explain the speed of substitution. In the third argument, 1) the bacterial populations sampled in [ 22 ] might not be close to mutation-drift equilibrium, 2) the sampled cells might not reflect the true diversity of the bacterial population in a patient, and 3) the levels of polymorphism again may be explained by very low effective population sizes. Consistency with observations nevertheless requires  N  values of around 100 or lower, which seems grossly at odds with the usually large census population sizes of bacteria. In mouse models of TB infection, for instance, bacterial loads reach around 10 5  – 10 7  colony forming units per lung [ 2 , 24 ]. It has been noted, however, that effective population sizes of bacteria can be much lower than actual sizes [ 11 , 25 ]. I will also comment on why I have not attempted to statistically fit the model to data to estimate  N  and  s . First, from the plots shown here, it is clear that different combinations of the two parameters can explain the observations. This would make it difficult to locate the best fit. Second, although the model can be used to assess the possibility of neutrality in the current context, it cannot adequately serve as a framework for estimation given the intricacies of host-pathogen interactions. Further, adding more parameters to the model would increase the complexity of the analysis beyond what can be sustained by the resolution of the currently available data. Taken together, the results suggest positive selection, although the evidence is not conclusive. A possible alternative is that the effective population sizes of  M. tuberculosis  within patients are very low due to population structure, background selection, or other factors. If there is indeed detectable adaptive evolution of tuberculosis within patients, what are the sources of selection? Two important candidates are antibiotic treatment and the host immune system. Studies using serial isolates have found no correlation between IS 6110  genotype instability and (a) drug resistance/susceptibility of the isolate [ 26 , 27 ], (b)  change  in drug resistance status [ 19 ] or (c) drug adherence by the patient [ 26 ]. It is still possible, however, that the collection of observed changes involve a variety of different genetic loci, with at least some conferring drug resistance, although such events may not be statistically detectable. Further, mutation in drug resistance loci will not necessarily be revealed by a marker. Genetic analysis of isolates of  M. tuberculosis  from the lung lesions of six patients has shown heterogeneity in resistance-associated alleles, but not with respect to IS 6110  [ 28 ]. Alternatively, fingerprint changes may reflect (evolutionary) escape from the immune system. The analysis here hints at low effective population sizes – perhaps the immune system induces a heavy decline in population sizes of  M. tuberculosis  within patients, i.e., bottlenecks – which is overcome by survivors with new genotypes. If the observed patterns are to be explained by severe bottlenecks, the surviving cells are not necessarily better adapted to residing in the host than the parental cells that were eliminated by the immune response. It is noteworthy that Yeh  et al.  [ 26 ] found no relationship between HIV status of the patient and genotype instability. This suggests that genetic changes in  M. tuberculosis  are not primarily driven by the immune system. However, the extraordinary ability of  M. tuberculosis  to manipulate the T cell response [ 2 ] suggests the role of adaptation to the immune system in the deeper evolutionary history of the organism. Interestingly, de Boer  et al.  [ 27 ] found an association between IS 6110  change and extrapulmonary disease or pulmonary+extrapulmonary disease and extrapulmonary origin of isolates. Dissemination is a major factor in the pathogenesis of tuberculosis. Since the lungs are the preferred environment of the organism, the new environments outside lungs may create opportunities for adaptive evolution. Adaptive evolution leading to specialisation to tissue types is to be expected. A recent article [ 29 ], for example, has found the occurrence of tissue-specific adaptations in  Streptococcus pyogenes  by examining ratios of non-synonymous to synonymous substitution rates ( d n / d s ). Is IS 6110  directly responsible for adaptive mutations? On one hand, the apparently strict asexuality of  M. tuberculosis  implies that all genes good, bad or neutral are tightly linked to each other. It is likely then that IS-induced changes hitchhike to fixation with other mutations that confer advantage to the genome. On the other hand, it has recently been demonstrated that IS 6110  carries a promoter that can modify the the expression of neighbouring genes, raising the possibility of a direct role for the element in adaptive evolution [ 30 ]. Note that changes caused by IS 6110  can be not only beneficial, but also neutral or deleterious [ 31 ]. At the within-patient level, the best studied pathogen is perhaps HIV. While  M. tuberculosis  shares with viruses the characteristic of replicating within cells, a major difference is that mutation rates in viruses are much higher, particularly in retroviruses, which depend on reverse transcriptase (a low-fidelity enzyme) to copy their genomes. Hence, the extent of nucleotide variation of  M. tuberculosis  is not expected to be the same as is commonly observed for example in HIV [ 32 ]. There is ongoing controversy among HIV researchers about the role of stochasticity due to low effective population sizes in the evolution of the virus [ 33 - 35 ]. In any case, investigating the ratio of non-synonymous to synonymous substitutions ( d n / d s ) has established the action of positive selection on HIV within patients [ 32 , 36 ]. In  M. tuberculosis , the level of polymorphism at synonymous sites has been noted to be extremely low [ 9 ]. It would be of interest to measure the ratio of non-synonymous to synonymous polymorphisms in key genes, such as loci conferring resistance to drugs, or those implicated in interactions with the immune system. These  d n / d s  ratios may provide further insight into the nature of positive selection in  M. tuberculosis .
Discussion
The three lines of evidence presented in this article suggest positive selection on  M. tuberculosis  within hosts. There are, however, limitations to these analyses. In the first argument there is no inherent problem with finding transposition rates that are high. In the second argument, it is possible to lower the effective population size far enough to explain the speed of substitution. In the third argument, 1) the bacterial populations sampled in [ 22 ] might not be close to mutation-drift equilibrium, 2) the sampled cells might not reflect the true diversity of the bacterial population in a patient, and 3) the levels of polymorphism again may be explained by very low effective population sizes. Consistency with observations nevertheless requires  N  values of around 100 or lower, which seems grossly at odds with the usually large census population sizes of bacteria. In mouse models of TB infection, for instance, bacterial loads reach around 10 5  – 10 7  colony forming units per lung [ 2 , 24 ]. It has been noted, however, that effective population sizes of bacteria can be much lower than actual sizes [ 11 , 25 ].M. tuberculosisNI will also comment on why I have not attempted to statistically fit the model to data to estimate  N  and  s . First, from the plots shown here, it is clear that different combinations of the two parameters can explain the observations. This would make it difficult to locate the best fit. Second, although the model can be used to assess the possibility of neutrality in the current context, it cannot adequately serve as a framework for estimation given the intricacies of host-pathogen interactions. Further, adding more parameters to the model would increase the complexity of the analysis beyond what can be sustained by the resolution of the currently available data.NsTaken together, the results suggest positive selection, although the evidence is not conclusive. A possible alternative is that the effective population sizes of  M. tuberculosis  within patients are very low due to population structure, background selection, or other factors. If there is indeed detectable adaptive evolution of tuberculosis within patients, what are the sources of selection? Two important candidates are antibiotic treatment and the host immune system. Studies using serial isolates have found no correlation between IS 6110  genotype instability and (a) drug resistance/susceptibility of the isolate [ 26 , 27 ], (b)  change  in drug resistance status [ 19 ] or (c) drug adherence by the patient [ 26 ]. It is still possible, however, that the collection of observed changes involve a variety of different genetic loci, with at least some conferring drug resistance, although such events may not be statistically detectable. Further, mutation in drug resistance loci will not necessarily be revealed by a marker. Genetic analysis of isolates of  M. tuberculosis  from the lung lesions of six patients has shown heterogeneity in resistance-associated alleles, but not with respect to IS 6110  [ 28 ].M. tuberculosis6110changeM. tuberculosis6110Alternatively, fingerprint changes may reflect (evolutionary) escape from the immune system. The analysis here hints at low effective population sizes – perhaps the immune system induces a heavy decline in population sizes of  M. tuberculosis  within patients, i.e., bottlenecks – which is overcome by survivors with new genotypes. If the observed patterns are to be explained by severe bottlenecks, the surviving cells are not necessarily better adapted to residing in the host than the parental cells that were eliminated by the immune response. It is noteworthy that Yeh  et al.  [ 26 ] found no relationship between HIV status of the patient and genotype instability. This suggests that genetic changes in  M. tuberculosis  are not primarily driven by the immune system. However, the extraordinary ability of  M. tuberculosis  to manipulate the T cell response [ 2 ] suggests the role of adaptation to the immune system in the deeper evolutionary history of the organism.M. tuberculosiset al.M. tuberculosisM. tuberculosisInterestingly, de Boer  et al.  [ 27 ] found an association between IS 6110  change and extrapulmonary disease or pulmonary+extrapulmonary disease and extrapulmonary origin of isolates. Dissemination is a major factor in the pathogenesis of tuberculosis. Since the lungs are the preferred environment of the organism, the new environments outside lungs may create opportunities for adaptive evolution. Adaptive evolution leading to specialisation to tissue types is to be expected. A recent article [ 29 ], for example, has found the occurrence of tissue-specific adaptations in  Streptococcus pyogenes  by examining ratios of non-synonymous to synonymous substitution rates ( d n / d s ).et al.6110Streptococcus pyogenesdndsIs IS 6110  directly responsible for adaptive mutations? On one hand, the apparently strict asexuality of  M. tuberculosis  implies that all genes good, bad or neutral are tightly linked to each other. It is likely then that IS-induced changes hitchhike to fixation with other mutations that confer advantage to the genome. On the other hand, it has recently been demonstrated that IS 6110  carries a promoter that can modify the the expression of neighbouring genes, raising the possibility of a direct role for the element in adaptive evolution [ 30 ]. Note that changes caused by IS 6110  can be not only beneficial, but also neutral or deleterious [ 31 ].6110M. tuberculosis61106110At the within-patient level, the best studied pathogen is perhaps HIV. While  M. tuberculosis  shares with viruses the characteristic of replicating within cells, a major difference is that mutation rates in viruses are much higher, particularly in retroviruses, which depend on reverse transcriptase (a low-fidelity enzyme) to copy their genomes. Hence, the extent of nucleotide variation of  M. tuberculosis  is not expected to be the same as is commonly observed for example in HIV [ 32 ]. There is ongoing controversy among HIV researchers about the role of stochasticity due to low effective population sizes in the evolution of the virus [ 33 - 35 ]. In any case, investigating the ratio of non-synonymous to synonymous substitutions ( d n / d s ) has established the action of positive selection on HIV within patients [ 32 , 36 ].M. tuberculosisM. tuberculosisdndsIn  M. tuberculosis , the level of polymorphism at synonymous sites has been noted to be extremely low [ 9 ]. It would be of interest to measure the ratio of non-synonymous to synonymous polymorphisms in key genes, such as loci conferring resistance to drugs, or those implicated in interactions with the immune system. These  d n / d s  ratios may provide further insight into the nature of positive selection in  M. tuberculosis .M. tuberculosisdndsM. tuberculosisAppendix: Bacteria and the Wright-Fisher process The analyses here rely on the commonly used diffusion model of drift and selection in a population, based on the Wright-Fisher process [ 37 , 38 ]. It is also possible to use the Moran model, in which at each time step an individual is chosen randomly to reproduce, and then another individual is chosen to die. The individual to die may be the same as the individual that reproduced, but not the offspring. Selection can be incorporated by including differential probability of birth or death for different genotypes. As noted by Ewens [ 38 ], the Moran model closely resembles the Wright-Fisher model; the critical difference between the two models arises from differences in the distribution of offspring number. The theory is usually discussed in relation to a diploid population of size  N e  in which there are 2 N e  copies of the (autosomal) gene in question. The diffusion model is used here with minor adjustments to describe bacterial populations. Let the number of bacterial cells in a population be  N . Each mutant appears in the population at frequency 1/ N . Realistically, population sizes fluctuate and only a subset of cells actively divide. The number  N  should therefore be considered to be the effective rather than the actual population size, which may be much larger than  N . Mean and variance of change It can be shown that the deterministic dynamics of selection in a haploid model are well approximated by a logistic model. The mean change in frequency  x  of an allele per generation is  m ( x ) =  sx (1- x ), which is identical to the diploid model with additive fitnesses (no dominance) if each copy of the advantageous allele adds  s  to the fitness (see [[ 37 ], p. 192]). That is, heterozygotes enjoy a fitness advantage  s  and homozygotes have advantage  2s . The variance component  v(x) , the variance in change of allele frequency per generation, can also be taken from diploid theory. Replacing the diploid model of the random union of gametes with choosing cells randomly from each generation to the next, the effective population size is adjusted according to the distribution of offspring number under a given model of cellular division. Binary fission There are numerous ways to model drift in populations of organisms that reproduce by dividing to produce two daughters [ 39 ]. Here, cells are assumed to undergo fission synchronously and daughter cells are chosen randomly at each generation. In the absence of selective effects, the offspring distribution is  p 0  = 1/4,  p 1  = 1/2,  p 2  = 1/4, where  p i  is the probability of producing  i  offspring. The variance in offspring number here is 1/2 and the variance-effective population size equals  N /  = 2 N . Thus in this case, the diploid theory can be directly used as far as  v(x)  is concerned (replacing 2 N e  with 2 N ). Johnson and Gerrish [ 39 ] consider alternative models. These alternatives are associated with different rates at which drift proceeds in a population, and would not affect the qualitative conclusions drawn here. Fixation probability Diffusion models of genetic drift have shown [ 11 ] that the probability of fixation of an allele at frequency  p  in a randomly mating diploid population of size  N e  (with 2 N e  copies of the gene in question) is Therefore, using  p  = 1/ N  rather than the usual  p  = l/(2 N e ) the probability of fixation of a mutant bacterial cell with selective advantage  s  is Note that when 4 N s >> 1,  u  ~ 4 s . This agrees with a result of Gerrish and Lenski [ 18 ], using a branching process model (rather than the diffusion model) to find the fixation probability under this same model of binary fission. See [ 39 ] for discussion of  u  for alternative models. Steady state distribution Let the mutation rates from any genotype to any other be equal ( μ ). As stated above, selection is additive. As shown by Wright [ 23 ], the steady state distribution of allele frequency is then given by the density function See also [ 37 , 38 ] for further details.
Appendix: Bacteria and the Wright-Fisher process
The analyses here rely on the commonly used diffusion model of drift and selection in a population, based on the Wright-Fisher process [ 37 , 38 ]. It is also possible to use the Moran model, in which at each time step an individual is chosen randomly to reproduce, and then another individual is chosen to die. The individual to die may be the same as the individual that reproduced, but not the offspring. Selection can be incorporated by including differential probability of birth or death for different genotypes. As noted by Ewens [ 38 ], the Moran model closely resembles the Wright-Fisher model; the critical difference between the two models arises from differences in the distribution of offspring number. The theory is usually discussed in relation to a diploid population of size  N e  in which there are 2 N e  copies of the (autosomal) gene in question. The diffusion model is used here with minor adjustments to describe bacterial populations. Let the number of bacterial cells in a population be  N . Each mutant appears in the population at frequency 1/ N . Realistically, population sizes fluctuate and only a subset of cells actively divide. The number  N  should therefore be considered to be the effective rather than the actual population size, which may be much larger than  N .NeNeNNNNMean and variance of change It can be shown that the deterministic dynamics of selection in a haploid model are well approximated by a logistic model. The mean change in frequency  x  of an allele per generation is  m ( x ) =  sx (1- x ), which is identical to the diploid model with additive fitnesses (no dominance) if each copy of the advantageous allele adds  s  to the fitness (see [[ 37 ], p. 192]). That is, heterozygotes enjoy a fitness advantage  s  and homozygotes have advantage  2s . The variance component  v(x) , the variance in change of allele frequency per generation, can also be taken from diploid theory. Replacing the diploid model of the random union of gametes with choosing cells randomly from each generation to the next, the effective population size is adjusted according to the distribution of offspring number under a given model of cellular division.
Mean and variance of change
It can be shown that the deterministic dynamics of selection in a haploid model are well approximated by a logistic model. The mean change in frequency  x  of an allele per generation is  m ( x ) =  sx (1- x ), which is identical to the diploid model with additive fitnesses (no dominance) if each copy of the advantageous allele adds  s  to the fitness (see [[ 37 ], p. 192]). That is, heterozygotes enjoy a fitness advantage  s  and homozygotes have advantage  2s .xmxsxxss2sThe variance component  v(x) , the variance in change of allele frequency per generation, can also be taken from diploid theory. Replacing the diploid model of the random union of gametes with choosing cells randomly from each generation to the next, the effective population size is adjusted according to the distribution of offspring number under a given model of cellular division.v(x)Binary fission There are numerous ways to model drift in populations of organisms that reproduce by dividing to produce two daughters [ 39 ]. Here, cells are assumed to undergo fission synchronously and daughter cells are chosen randomly at each generation. In the absence of selective effects, the offspring distribution is  p 0  = 1/4,  p 1  = 1/2,  p 2  = 1/4, where  p i  is the probability of producing  i  offspring. The variance in offspring number here is 1/2 and the variance-effective population size equals  N /  = 2 N . Thus in this case, the diploid theory can be directly used as far as  v(x)  is concerned (replacing 2 N e  with 2 N ). Johnson and Gerrish [ 39 ] consider alternative models. These alternatives are associated with different rates at which drift proceeds in a population, and would not affect the qualitative conclusions drawn here.
Binary fission
There are numerous ways to model drift in populations of organisms that reproduce by dividing to produce two daughters [ 39 ]. Here, cells are assumed to undergo fission synchronously and daughter cells are chosen randomly at each generation. In the absence of selective effects, the offspring distribution is  p 0  = 1/4,  p 1  = 1/2,  p 2  = 1/4, where  p i  is the probability of producing  i  offspring. The variance in offspring number here is 1/2 and the variance-effective population size equals  N /  = 2 N . Thus in this case, the diploid theory can be directly used as far as  v(x)  is concerned (replacing 2 N e  with 2 N ). Johnson and Gerrish [ 39 ] consider alternative models. These alternatives are associated with different rates at which drift proceeds in a population, and would not affect the qualitative conclusions drawn here.ppppiiNNv(x)NeNFixation probability Diffusion models of genetic drift have shown [ 11 ] that the probability of fixation of an allele at frequency  p  in a randomly mating diploid population of size  N e  (with 2 N e  copies of the gene in question) is Therefore, using  p  = 1/ N  rather than the usual  p  = l/(2 N e ) the probability of fixation of a mutant bacterial cell with selective advantage  s  is Note that when 4 N s >> 1,  u  ~ 4 s . This agrees with a result of Gerrish and Lenski [ 18 ], using a branching process model (rather than the diffusion model) to find the fixation probability under this same model of binary fission. See [ 39 ] for discussion of  u  for alternative models.
Fixation probability
Diffusion models of genetic drift have shown [ 11 ] that the probability of fixation of an allele at frequency  p  in a randomly mating diploid population of size  N e  (with 2 N e  copies of the gene in question) ispNeNeTherefore, using  p  = 1/ N  rather than the usual  p  = l/(2 N e ) the probability of fixation of a mutant bacterial cell with selective advantage  s  ispNpNesNote that when 4 N s >> 1,  u  ~ 4 s . This agrees with a result of Gerrish and Lenski [ 18 ], using a branching process model (rather than the diffusion model) to find the fixation probability under this same model of binary fission. See [ 39 ] for discussion of  u  for alternative models.NusuSteady state distribution Let the mutation rates from any genotype to any other be equal ( μ ). As stated above, selection is additive. As shown by Wright [ 23 ], the steady state distribution of allele frequency is then given by the density function See also [ 37 , 38 ] for further details.
Steady state distribution
Let the mutation rates from any genotype to any other be equal ( μ ). As stated above, selection is additive. As shown by Wright [ 23 ], the steady state distribution of allele frequency is then given by the density functionμSee also [ 37 , 38 ] for further details.


Because males and females possess different numbers of the two sex chromosomes (for instance, in mammals, XX in females versus XY in males), the potential “dose” of each gene differs. Without some compensating mechanism, female mammals would express twice the quantity of an X-linked gene as males. The same holds true in the fruitfly  Drosophila , in which the female carries two X chromosomes, while the male carries only one.In mammals, dosage compensation is achieved by silencing one of the X's in the female.  Drosophila  takes the opposite tack, doubling the output from the single male X chromosome. It does so through the creation of “compensasomes,” protein–RNA complexes that bind to the X chromosome and boost gene transcription. One model of compensasome activity has posited a two-step mechanism, in which the complexes form only at 35–40 specific “entry sites” along the X, and then spread out to the surrounding regions. In this issue, Delphine Fagegaltier and Bruce Baker test this model and show that its predictions do not match experimental results.The compensasome complex includes half a dozen proteins collectively known as MSLs (for “male-specific lethal”), along with two pieces of RNA,  roX1  and  roX2 . Fagegaltier and Baker reasoned that, according to the entry-site model, if a piece of the X not containing one of the entry sites was transposed to an autosome (non-sex chromosome), it should be unable to recruit MSLs and therefore be unable to form compensasomes. To test this prediction, they used autosomes into which various pieces of the X had been transposed. Contrary to prediction, they found that even the smallest pieces could recruit MSLs, whether or not they contained entry sites. Furthermore, the pattern of MSL binding was exactly the same as if the fragment of the X was still on its native chromosome, suggesting that each of the hundreds of sites at which compensasomes are found function autonomously to recruit them.roX1roX2Another prediction of the entry-site model is that compensasomes should spread out from the entry site, along the chromosome. And here again, the model does not hold up—Fagegaltier and Baker found that even when entry sites from the X chromosome are put close to an autosomal region, compensasomes never spread from the X onto these regions. These results suggest that spreading is not an innate function of the compensasome, and further strengthens the case for autonomous recruitment all along the X. In place of the two-step “entry site plus spreading” model, the authors propose a model based on differential affinity for compensasome components. They suggest that the 35–40 “entry sites” are simply high-affinity sites that recruit MSLs first, based on intrinsic differences that allow them to bind and hold MSLs more strongly than other sites. Once these sites are occupied, additional compensasome components can bind to lower-affinity sites. This mechanism can account for observed compensasome activity without the restriction to a limited number of entry sites and the requirement for spreading.Fagegaltier and Baker note that while compensasome spreading does not normally occur during dosage compensation on the X chromosome, it has nonetheless been documented for some  roX  transgenes. They propose that the additional binding observed specifically around  roX  transgenes results from a mass action of compensasomes, as  roX  transgenes would act as assembly sites for compensasomes, just as ribosomal RNA genes do for ribosomes. Once formed, compensasomes may bind locally to other neighboring sites.roXroXroXWhile the details of dosage compensation and the dosage compensation complexes now clearly differ between mammals and flies, there are broad similarities, including the widespread modification of chromatin structure and the use of RNA components in the compensation machinery. A deeper understanding of the process in flies may help shed light on the details of compensation in other organisms as well.