Recent comments

  • Reply to: A Role for Mycobacterium in Alzheimer’s Disease?   2 months 4 weeks ago

    We are grateful to Dr. Broxmeyer’s Letter to the Editor regarding the article by Loupy et al. [1]. The idea that Alzheimer’s disease might have, in some cases, a mycobacterial etiology merits further study. Tuberculosis infection remains one of the most common infections worldwide, with an estimated 2 billion people infected with M. tuberculosis [2]. In the United States, a total of 9,287 new cases of tuberculosis were reported in 2016 [3]. However, based on tuberculin skin tests (TST) in 2011-2012, it was estimated that 4.7% of the US population was TST positive (compared to the point estimate in foreign-born persons of 20.5%) [4]. Also of concern are nontuberculous mycobacterial (NTM) infections, chronic infections that appear to be increasing in the United States, particularly among older age groups [5]. This is of potential interest given the similar patterns of geographic variation of NTM infection [5] and Alzheimer’s disease [6], which could reflect shared environmental risk factors, such as the abundance of NTM, including potential pathogenic M. mucogenicum/phocaicum, M. avium complex, M. fortuitum complex, and M. abscessus complex, in municipal water sources [7]. Of these, M. avium was recently classified as belonging to an emended genus, Mycobacterium (“Tuberculosis-Simiae” clade), whereas M. abscessus was classified as Mycobacteroides gen. nov. (“Abscessus-Chelonae” clade), and M. vaccae, M. mucogenicum, and M. fortuitum were classified as Mycolicibacterium gen. nov. (“Fortuitum-Vaccae” clade), highlighting the phylogenetic diversity of mycobacteria, and foreshadowing potential differences in their impacts on the human host.

    Mycobacteria are intracellular parasites, and, thus, may utilize a well-documented “Trojan horse” mechanism to enter the brain, wherein mycobacteria enter the central nervous system following infection of host immune cells [8-12]. The extent to which NTM can enter the central nervous system in this manner and the effects on host neurophysiology remain to be determined.

    It is also possible that chronic mycobacterial infection increases risk of Alzheimer’s disease through signaling of peripheral inflammation from the periphery to the central nervous system though afferent signaling pathways. We have shown that intratracheal administration of a heat-killed preparation of M. vaccae NCTC 11659 (coupled to nitrocellulose beads in order to localize the immune activation to the airways) in mice acutely activates a subset of serotonergic neurons located within the interfascicular part of the dorsal raphe nucleus (DRI) [13]. This subset of serotonergic neurons projects to the hippocampus and prefrontal cortex and we have suggested previously that it modulates affective, cognitive, and stress resilience functions [14]. Importantly, we have also shown that infection of mice with live, virulent M. tuberculosis (H37Rv) activates DRI serotonergic neurons 3 days and 7 days following infection, but this activation is absent 14, 21, and 28 days after infection, suggesting adaptation of the DRI serotonergic system following chronic infection [15]. In convergence with Dr. Broxmeyer’s Letter to the Editor, we have shown an essential role for microRNA 135 (miR135a) for chronic stress resiliency, antidepressant efficacy, and serotonergic activity [16]. Dysregulation of an miR135a-serotonin signaling pathway following chronic mycobacterial infection could conceivably impact the etiology and pathophysiology of Alzheimer’s disease.

    Christopher A. Lowry, PhD, and
    Kelsey M. Loupy, BA, MS

    [1]     Loupy KM, Lee T, Zambrano CA, Elsayed AI, D'Angelo HM, Fonken LK, Frank MG, Maier SF, Lowry CA (2020) Alzheimer’s disease: protective effects of Mycobacterium vaccae, a soil-derived mycobacterium with anti-inflammatory and anti-tubercular properties, on the proteomic profiles of plasma and cerebrospinal fluid in rats. J Alzheimers Dis, doi: 10.3233/JAD-200568.
    [2]    U.S.Department of Health and Human Services Centers for Disease Control and Prevention (2019) Epidemiology of Tuberculosis. Atlanta, Georgia.
    [3]    Schmit KM, Wansaula Z, Pratt R, Price SF, Langer AJ (2017) Tuberculosis - United States, 2016. MMWR Morb Mortal Wkly Rep 66, 289-294.
    [4]    Miramontes R, Hill AN, Yelk Woodruff RS, Lambert LA, Navin TR, Castro KG, LoBue PA (2015) Tuberculosis infection in the United States: Prevalence estimates from the National Health and Nutrition Examination Survey, 2011-2012. PLoS One 10, e0140881.
    [5]     Winthrop KL, Marras TK, Adjemian J, Zhang H, Wang P, Zhang Q (2020) Incidence and prevalence of nontuberculous mycobacterial lung disease in a large U.S. managed care health plan, 2008-2015. Ann Am Thorac Soc 17, 178-185.
    [6]    Kirson NY, Meadows ES, Desai U, Smith BP, Cheung HC, Zuckerman P, Matthews BR (2020) Temporal and geographic variation in the incidence of Alzheimer's disease diagnosis in the US between 2007 and 2014. J Am Geriatr Soc 68, 346-353.
    [7]    Gebert MJ, Delgado-Baquerizo M, Oliverio AM, Webster TM, Nichols LM, Honda JR, Chan ED, Adjemian J, Dunn RR, Fierer N (2018) Ecological analyses of mycobacteria in showerhead biofilms and their relevance to human health. MBio 9, e01614-18.
    [8]    van Leeuwen LM, Boot M, Kuijl C, Picavet DI, van Stempvoort G, van der Pol SMA, de Vries HE, van der Wel NN, van der Kuip M, van Furth AM, van der Sar AM, Bitter W (2018) Mycobacteria employ two different mechanisms to cross the blood-brain barrier. Cell Microbiol 20, e12858.
    [9]    Schwerk C, Tenenbaum T, Kim KS, Schroten H (2015) The choroid plexus-a multi-role player during infectious diseases of the CNS. Front Cell Neurosci 9, 80.
    [10]    Jain SK, Tobin DM, Tucker EW, Venketaraman V, Ordonez AA, Jayashankar L, Siddiqi OK, Hammoud DA, Prasadarao NV, Sandor M, Hafner R, Fabry Z; NIH Tuberculous Meningitis Writing Group (2018) Tuberculous meningitis: a roadmap for advancing basic and translational research. Nat Immunol 19, 521-525.
    [11]    Cain MD, Salimi H, Diamond MS, Klein RS (2019) Mechanisms of pathogen invasion into the central nervous system. Neuron 103, 771-783.
    [12]    Dando SJ, Mackay-Sim A, Norton R, Currie BJ, St John JA, Ekberg JA, Batzloff M, Ulett GC, Beacham IR (2014) Pathogens penetrating the central nervous system: infection pathways and the cellular and molecular mechanisms of invasion. Clin Microbiol Rev 27, 691-726.
    [13]    Lowry CA, Hollis JH, de Vries A, Pan B, Brunet LR, Hunt JR, Paton JF, van Kampen E, Knight DM, Evans AK, Rook GA, Lightman SL (2007) Identification of an immune-responsive mesolimbocortical serotonergic system: Potential role in regulation of emotional behavior. Neuroscience 146, 756-772.
    [14]    Hale MW, Lowry CA (2011) Functional topography of midbrain and pontine serotonergic systems: implications for synaptic regulation of serotonergic circuits. Psychopharmacology (Berl) 213, 243-264.
    [15]    Hollis JH, Goosen K, Orozco H, Wilkinson A, Lightman SL, Hernández-Pando R, Rook GA, Lowry CA (2009) A murine model of acute and chronic bronchopulmonary infection with Mycobacterium tuberculosis: Delayed and persistent activation of serotonergic neurons within the brainstem raphe complex. Brain Behav Immun 23 (Suppl 2), S40-S41.
    [16]    Issler O, Haramati S, Paul ED, Maeno H, Navon I, Zwang R, Gil S, Mayberg HS, Dunlop BW, Menke A, Awatramani R, Binder EB, Deneris ES, Lowry CA, Chen A (2014) MicroRNA 135 is essential for chronic stress resiliency, antidepressant efficacy, and intact serotonergic activity. Neuron 83, 344-360.

  • Reply to: Plasma amyloid-β oligomerization tendency levels are increased with age in healthy subjects   4 months 1 week ago

    Blood-based biomarkers will be the holy grail in the field of Alzheimer’s disease (AD) research and practice, once the adequacy of their sensitivity and specificity is proven. In addition, an ideal diagnostic biomarker for AD should detect the fundamental features of the molecular pathogenesis of AD. Therefore, reliable blood-based biomarkers of AD should be derived from amyloid-β (Aβ).

    According to recent studies, peripheral Aβ-related markers are suitable for the evaluation of cerebral amyloidopathy even at the preclinical stage of AD. There are three types of blood-based, amyloid-targeting AD markers. The first method involves measuring Aβ related molecules (Aβ1-42, Aβ1-40, or other APP fragments) and calculate their ratios. Highly sensitive ELISA techniques or specific mass spectrometry are being used for this. The second is measuring the ratio of α-form to β-form amyloid in the peripheral circulation. The last technique is observing increasing tendencies of oligomeric forms of Aβ in plasma after spiking synthetic Aβ peptides.

    The comment written by Dr. Minn & Dr. Kim noted that this oligomerization tendency is shows a slight increase with age in healthy normal subjects in his study. This is a very important finding we should keep in mind. As glycated hemoglobin (HbA1C) for diabetes mellitus, this oligomerization tendency of Aβ in plasma measured by MDS-OAβ could be used as a monitoring marker for preclinical AD pharmacotherapy in the near future. In this situation, Dr. Minn & Dr. Kim’s findings are very important to establish the management guideline according to age.

    There are two possible explanations for the increased oligomerization tendencies by age; one is the possibility that there were some preclinical AD patients in the older healthy normal subject group. Because cerebral amyloidopathy may begin 15~20 years before the onset of clinical symptoms, and amyloid PET studies also found some positive cases without clinical symptoms at older population group.

    Another possibility is that there may be physiological increases of Aβ oligomerization tendencies according to age. We may explain this increasing tendency by increased production of Aβ or many factors related to oligomerization, or decreased clearance of Aβ or decreased inhibiting factors of oligomerization. However, at the moment, there isn’t enough evidence to definitively explain the mechanism; more time is needed for additional research regarding this.

    In conclusion, this comment including good data showing the increased oligomerization tendency of Aβ in plasma by age is a good reference upon which to establish diagnosis or treatment guidelines using MDS-OAβ.

    SangYun Kim1, Young Chul Youn2

    1 Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
    Clinical Neuroscience Center of Seoul National University Bundang Hospital, Seongnam-si, Korea
    2Department of Neurology, Chung-Ang University College of Medicine, Seoul, Republic of Korea

  • Reply to: Letter to the Editor: Comment on “Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer’s Disease-like Pathophysiology in APP/PS1 Mice"   3 months 1 week ago

    Dear Sir;

    Our article which was published by Plos ONE in 2010 presented that homocysteic acid (HCA) was a pathogen of 3xTg-AD model mice (1). And also we published that blood HCA was the pathogen of human AD (2). This HCA is the strong agonist of glutamate, which suggests MSG is the same glutamate.


    1. Hasegawa T, Mikoda N, Kitazawa M, LaFerla FM (2010) Treatment of Alzheimer’s Disease with Anti-Homocysteic Acid Antibody in 3xTg-AD Male Mice. PLoS ONE 5(1): e8593. doi:10.1371/journal.pone.0008593

    2. Tohru Hasegawa, Masayoshi Ichiba, Shin-ei Matsumoto, Koji Kasanuki, Taku Hatano, Hiroshige Fujishiro, Eizo Iseki, Nobutaka Hattori, Tatsuo Yamada, Takeshi Tabira (2012) Urinary Homocysteic Acid Levels Correlate with Mini-Mental State Examination Scores in Alzheimer’s Disease Patients. Journal of Alzheimer’s Disease 31, 59–64.

  • Reply to: Letter to the Editor: Comment on “Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer’s Disease-like Pathophysiology in APP/PS1 Mice"   7 months 1 week ago

    We have carefully read the comments of Dr. Shinora on our article. We thank the editor for publishing our response alongside.

    Dr. Yoshida cites various studies that found no significant effect on plasma glutamate levels after MSG ingestion. It should be stated in this context that there is, however, much controversy in the literature over this topic. Other studies did report an increase in plasma glutamate levels after ingestion of MSG in humans [1-3] and rodents [4]. It is therefore possible that MSG supplementation does increase plasma glutamate levels, at least under certain circumstances. However, in our study we did not address this question, but focused on long-term effects of sustained MSG ingestion several weeks after MSG was administered. Indeed, in our paper we stated that glutamate may have entered the brain through the blood-brain barrier (BBB) as a possible explanation for our findings, but this is subject to future research. Since breakdown of the BBB has been described in the aging brain [5], in early Alzheimer’s disease [6], and even in people with the ApoE4 allele [7], we still regard this as the most likely hypothesis. In any case, whether future findings will or will not support this hypothesis, does not invalidate any of the major conclusion of the present paper. Analyzing BBB permeability to glutamate was not the main aim of this paper.

    Dr. Yoshida also deems it as ‘surprising’ that only APP/PS1 with 1% MSG and not in other experimental conditions—that is, 0.5% MSG—show impairments after treatment. We cannot follow the logic of the argumentation why it would be surprising that only the higher concentration should in fact induce impairments in APP/PS1 animals, while the lower MSG concentration would not.

    Furthermore, Dr. Yoshida raises concerns about the sample sizes we used in this study, given that pathophysiological development can vary between individuals in the APP/PS1 mouse model. The statistical tests that were applied demonstrated that the observed effects were significant, meaning that they were above chance level. Let us point out that is in fact the very essence of statistical parametric testing such as that resting upon Student’s t- or Snedecor’s F-distributions: by merely assuming normality and by taking into account within-sample variability, these tests allow for valid statistical inferences however little the sample size may be. A fortiori, the consistency and prominence of the mean difference exhibited between the groups (e.g., there was 4 times more Aβ present in the MSG-supplemented group, Fig. 2), turned out for our sample sizes to be sufficient. Besides, for some experiments larger sample sizes were indeed used (over 10 animals in each group), and we observed consistent effects in the MSG-1%-group, which correlated between different sets of experiments carried out by different researchers. Therefore, from a scientific standpoint, having applied well established statistical testing in a careful and responsible way, we are very much confident that our conclusions are reliable and consistent under the significance levels reported.

    Genetic variability and several environmental factors can influence the pathophysiology of AD, making it therefore difficult to relate our findings directly to epidemiological tendencies. We agree with Dr. Yoshida that more studies will be required to address whether MSG could have an effect in a certain subpopulation in humans (like carriers of the ApoE4 mutation, for example) and—as suggested in our discussion—this will be an interesting topic for further research.

    Tanja Fuchsberger, Jose Viña and Ana Lloret

    [1] Marina M, Graham TE (2002) Glutamate ingestion and its effects at rest and during exercise in humans. J Appl Physiol 93, 1251–1259.
    [2] Graham TE, Sgro V, Friars D, Gibala MJ (2000) Glutamate ingestion: the plasma and muscle free amino acid pools of resting humans. Am J Physiol Endocrinol Metab 278, E83–E89.
    [3] Stegink LD, Filer LJ Jr, Baker GL, Bell EF (1986) Plasma glutamate concentrations in 1-year-old infants and adults ingesting monosodium L-glutamate in consommi. Pediatr Res 20, 53-58.
    [4] McLaughlan JM, Neel FJ, Botting HG, Knipfel JE (1970) Blood and brain levels of glutamic acid in young rats given monosodium glutamate. Nutr Rep Int 1, 131-138.
    [5] Montagne A, Barnes SR, Sweeney MD, Halliday MR, Sagare AP, Zhao Z, Toga AW, Jacobs RE, Liu CY, Amezcua L, Harrington MG, Chui HC, Law M, Zlokovic BV (2015) Blood-brain barrier breakdown in the aging human hippocampus. Neuron 85, 296-302.
    [6] van de Haar HJ, Burgmans S, Jansen JF, van Osch MJ, van Buchem MA, Muller M, Hofman PA, Verhey FR, Backes WH (2016) Blood–brain barrier leakage in patients with early Alzheimer disease. Radiology 281, 527–535.
    [7] Montagne A, Nation DA, Sagare AP, Barisano G, Sweeney MD, Chakhoyan A, Pachicano M, Joe E, Nelson AR, D'Orazio LM, Buennagel DP, Harrington MG, Benzinger TLS, Fagan AM, Ringman JM, Schneider LS, Morris JC, Reiman EM, Caselli RJ, Chui HC, Tcw J, Chen Y, Pa J, Conti PS, Law M, Toga AW, Zlokovic BV (2020) APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature 581, 71-76.

  • Reply to: Tau Biology, Tauopathy, Traumatic Brain Injury, and Diagnostic Challenges.   8 months 2 weeks ago

    Very important question in the field

  • Reply to: Comment on Reliability and validity of the Chinese version of the Mild Behavioral Impairment Checklist for screening for Alzheimer’s disease   1 year 4 months ago

    We have carefully read the Letter to the Editor by Toni T. Saari, who made some proposals for our recently published article. We have checked the calculation method of internal consistency reliability and construct validity respectively, and provide the following feedback.

    As the authors say, alpha increases with the number of items, and the great Cronbach’s alpha coefficient of the whole scale may be related to the big number of items. We have also calculated the alpha of each subscale and got nice results in most of the dimensionalities. Only one coefficient is just fair (greater than 0.6 and less than 0.7), and the reasons for this are clearly explained in the discussion. There are indeed some studies showing that there are many better alternative methods to replace the Cronbach’s alphas to calculate the reliability of the scale; in particular, omega is more suitable for the scale with multidimensional and tau non-equivalence. However, it is also mentioned in many studies that, due to the lack of tau equivalence, compared with omega, Cronbach’s alpha tends to underestimate rather than overestimated the reliability of the scale [1-3]. Nowadays, the Cronbach’s coefficient is still the most widely used method of reliability calculation.

    In addition, we have always acknowledged that the construct validity of the study results is not ideal, which may be related to the selection of the subjects and cultural differences. We also hope to expand the sample size to calculate confirmatory factor analysis in the further study, for optimizing the scale items. Although the content validity and criterion validity of the scale are good, the conclusion that the Mild Behavioral Impairment-Checklist (MBI-C) has high reliability and validity is still not accurate and easy to cause misunderstanding. Because the construct validity is not ideal, it is indeed unprecise to draw this conclusion directly.

    We appreciate Professor Toni T. Saari’s attention to our research and for making useful suggestions. This study aims to explore whether MBI, as a new scale for testing behavioral impairment, can replace the Neuropsychiatric Inventory Questionnaire to be an effective tool for screening patients with Alzheimer's disease (AD). Although the small sample is the limitation of this study, MBI-C still demonstrates its superiority of screening. Considering that the internal consistency of each dimensionality of the scale and the structural validity are not ideal enough, we would like to accept the professor's suggestion sincerely, and modify the research conclusion as "This study showed that the Chinese version of the MBI-C has good reliability and validity, and could be used as an alternative scale to the NPI-Q for AD dementia screening in the Chinese population, but further large sample studies to inspect its construct validity is necessary". We hope to make up for the shortcomings of this study in further studies, and still believe that the MBI-C has a good implementation prospect in the screening of patients with AD in China.

    Yue Cui, Fang Li, and Liyong Wu


    [1] Deng L, Chan W (2017) Testing the difference between reliability coefficients alpha and omega. Educ Psychol Meas 77, 185-203.

    [2] Peterson RA, Kim Y (2013) On the relationship between coefficient alpha and composite reliability. J Appl Psychol 98, 194-198.

    [3] Tavakol M, Dennick R (2011) Making sense of Cronbach's alpha. Int J Med Educ 2, 53-55.