We used phylogenetic analyses of LTR sequences to infer evolutionary histories and potential transmission clusters. Questionnaire data was analyzed in conjunction with serological and molecular data. A relatively high proportion of the study population  were WB seropositive: 128 HTLV-1-like, 3 HTLV-2-like, and 34 HTLV-positive but untypeable profiles. 85 persons had HTLV indeterminate WB profiles. HTLV seroreactivity was higher in females, wives, heads of households, and increased with age. HTLV-1 LTR sequences from 109 persons clustered strongly with HTLV-1 and STLV-1 subtype B from humans and simians from DRC, with most sequences more closely related to STLV-1 from Allenopithecus nigroviridis (Allen’s swamp monkey). While 18 potential transmission clusters were identified, most were in different households, villages, and health zones. Three HTLV-1-infected persons were co-infected with simian foamy virus. The mean and median percentage of HTLV-1 pVLs were 5.72% and 1.53%, respectively, but were not associated with age, NHP exposure, village, or gender. We document high HTLV prevalence in DRC likely originating from STLV-1. We demonstrate regional spread of HTLV-1 in DRC with pVLs reported to be associated with HTLV disease, supporting local and national public health measures to prevent spread and morbidity.The Democratic Republic of the Congo (DRC) has a history of nonhuman primate (NHP) consumption and exposure to simian retroviruses yet little is known about the extent of zoonotic simian retroviral infections in DRC. We examined the prevalence of human T-lymphotropic viruses (HTLV), a retrovirus group of simian origin, in a large population of persons with frequent NHP exposures and a history of simian foamy virus infection. We screened plasma from 3,051 persons living in rural villages in central DRC using HTLV EIA and western blot (WB). PCR amplification of HTLV  HTLV-1 is a human retrovirus of zoonotic simian origin that has spread globally causing inflammatory and carcinogenic disease. We previously showed that persons with high nonhuman primate (NHP) exposure in the Democratic Republic of the Congo (DRC) can be infected with another simian retrovirus, simian foamy virus (SFV), suggesting they are also at risk for infection with STLV-1. We conducted follow-up analysis of the same persons from rural villages of central DRC to determine exposure and transmission risks for STLV-1 and HTLV-1 infection. Most persons, especially women, reported high levels of NHP exposure. We identified possible introduction and spread of STLV-1 from a local monkey species across households, villages, and health zones in DRC. Most persons had HTLV-1 levels above those reported in previous studies for persons with disease. Our findings reveal that coordinated public health strategies are needed at both local and national levels to prevent further spread and morbidity of HTLV-1. Human T-cell lymphotropic virus type 1 (HTLV-1), the first discovered human retrovirus, is widespread globally and can be highly oncogenic in some infected persons . With inIn addition to HTLV-1, three other HTLV groups types 2–4) have been identified, all four with varying geographic prevalence, disease potential, and evolutionary history , 9–12. G have beeMolecular characterization of primate T-lymphotropic viruses (PTLVs), which consists of both simian and human T-lymphotropic viruses, reveals that some HTLV subtypes share closer genetic ties to certain simian T-lymphotropic viruses (STLVs) than to other HTLV subtypes, suggesting sustained zoonotic transmission of STLV between nonhuman primates (NHPs) and humans . PhylogeCercopithecus spp. and Cercocebus spp. are preferred protein sources and account for around one third of the bushmeat market  x 100 as described previously , 58AllenIn the BEAST analysis, we found 18 potential transmission clusters that included a total of 50 persons . Three pIt is important to note that we also found several instances  where sequences from different households, and even different villages, clustered together with good phylogenetic support , Table 4Colobus angolensis) endemic to DRC ..Pan paniC. ascanius (red-tailed guenon) monkeys, all three HTLV-1s from these persons were within the large cluster of DRC sequences from our study that are potentially descendent from STLV-1ang. MPX8223 was also infected with the unique HTLV-1 strain identified in our study with an 11-bp deletion in the LTR region though this is likely unrelated to their SFV infection. Recently, it was shown that STLV-1 co-infection is associated with increased blood SFV pVLs and the authors showed that the STLV-1 tax protein can transactivate the SFV LTR to increase its replication  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file). Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Johan Van WeyenberghAssociate EditorPLOS Neglected Tropical DiseasesEdgar CarvalhoDeputy EditorPLOS Neglected Tropical Diseases***********************Reviewer's Responses to QuestionsKey Review Criteria Required for Acceptance?As you describe the new analyses required for acceptance, please consider the following:Methods-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?-Is the study design appropriate to address the stated objectives?-Is the population clearly described and appropriate for the hypothesis being tested?-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?-Were correct statistical analysis used to support conclusions?-Are there concerns about ethical or regulatory requirements being met?Reviewer #1: see Summary and General CommentsReviewer #2: The authors clearly stated the hypotheses and objectives to be achieved in this study. To this end, they have described the study population, whose size in numbers ensures that the conclusions drawn are sufficiently powerful. Meanwhile, The authors should clearly specify what was the original purpose of this population-based study and why they chose this particular region of the DRC and specifically these two villages? Are there any previously published results (with the exception of those concerning SFVs) from this population-based study?Regulatory requirements appear to have been met and clarification regarding the procedure for anonymizing the samples was sought from the authors.--------------------Results-Does the analysis presented match the analysis plan?-Are the results clearly and completely presented?-Are the figures  of sufficient quality for clarity?Reviewer #1: see Summary and General CommentsReviewer #2: It is important to describe the study population in more detail . The presentation of the results should be modified to make it more understandable and comparable to studies that are conducted using a comparable methodology: detection of specific antibodies by ELISA and confirmation by WB and PCR. Therefore some tables will have to be modified to be more clearly presented.--------------------Conclusions-Are the conclusions supported by the data presented?-Are the limitations of analysis clearly described?-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?-Is public health relevance addressed?Reviewer #1: see Summary and General CommentsReviewer #2: The conclusions drawn meet in part the objectives that have been defined. Indeed, some of the results could be analyzed and presented differently in a more rigorous and in-depth manner.--------------------Editorial and Data Presentation Modifications?Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”. Reviewer #1: see Summary and General CommentsReviewer #2: We recommend that the authors make significant modifications .--------------------Summary and General CommentsUse this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.Reviewer #1: The authors present a very interesting work on HTLV spread at a regional level in a rural area in the DRC. They combine several types of epidemiological data and methods to provide insight into the spread of HTLV-1 within and across distant villages. Most of my concerns relate to the phylogenetic analyses and their interpretation, which to my opinion merit some additional checks and better framing in the discussion section. #########Abstract:#########- "Questionnaire data was analyzed with serological and molecular data."=> Questionnaire data was analyzed in conjunction with serological and molecular data.- "with most sequences sharing an ancestral history with STLV-1 "=> not correct formulation###############Author summary:###############lines 99-100: "Divergent STLV-1, STLV-2, and STLV-3 have been reported in monkeys and apes in DRC, increasing the likelihood of exposure to these viruses [30-33]. " => this sentence is not correct: the presence by itself does not increase the likelihood of exposure. Please adjust. ###############Methods: ###############- line 128: I am surprised to see such a high level of geographical clustering of the villages, which to me seems unexpected under a random sampling scheme. Can the authors comment on this please - was the selection truly random? - can the authors comment on the quality of the samples? I am not a wet-lab expert, but I can imagine that it is not easy at all to preserve samples in high quality conditions in remote areas, in that climate, and over such long time frames. Was a quality check on the sample integrity performed? Could sample degradation underlie for example that the reported estimates represent lower end estimates of the true HTLV prevalence? If so, this should be discussed.http://www.ncbi.nlm.nih.gov/BLAST/) and added to the analyses for comparison along with HTLV references"- "Genetically related sequences were identified using BLAST searches (=> the authors should be more clear as to how the dataset was compiled. How many BLAST hits were retained for each of the query sequences? Why was there an apparent need to separately add reference sequences and other HTLV sequence data from the DRC? - why did the authors choose for a constant-rate BD tree prior instead of a (non-parametric) coalescent model? Also, which priors were specified on the parameters of the BD-process? this should be specified, as well as the rationale for each. - fig 2b: The branch lengths seem quite close to a multiple of 1 - this suggests that there was very little diversity in the alignment, and that the temporal aspect may be unreliable. The authors should evaluate whether or not there is significant temporal signal in the data to calibrate the clock model. This can be done as in Trovao et al Virus Evolution, 2015, 1(1): vev016) by comparing the rate estimate with the expectation in the absence of temporal structure. Below the tweaks that need to be specified in the XML:1) make sure that each taxon has an associated leafHeight parameter [this goes in the treeModel block]:<leafHeight taxon="taxon1"><parameter id="age(taxon1)"/></leafHeight> …<leafHeight taxon="taxonN"><parameter id="age(taxonN)"/></leafHeight>2) add an operator that permutes the tip dates during the run, giving the evol rate expectation in the absence of temporal signal<swapParameterOperator weight="10" forceGibbs="true"><parameter idref="age(taxon1)"/> …<parameter idref="age(taxonN)"/></swapParameterOperator>3) to check whether all went well, log the tip-ages [and perhaps inlcude a tip age in the screen-logger element for a quick visual check]:in the fileLog-element:<parameter idref="age(taxon1)"/> …<parameter idref="age(taxonN)"/>and for the screenLog: <column label="age(taxon1)" sf="6" width="12"><parameter idref="age(taxon1)"/></column>The usual criterium is non-overlapping 95% credible intervals. The test for temporal signal should be reported, perhaps using a supplementary figure.https://pubmed.ncbi.nlm.nih.gov/20807414/). Both can be set up using BEAUTi.- The authors should justify their choice for the relaxed clock model: I believe that the HTLV evolutionary rate is linked to its transmission rate . If what the authors refer to as 'potential transmission clusters' indeed represent human-to-human transmission, I would think that one can expect 2 rate-distributions with different means operating on the phylogeny: a slower rate on the internal branches that represent evolution in the reservoir (on which I expect less frequent transmission), and a higher rate in clades that represent human-to-human transmission. In such situations the relaxed clock model will perform poorly , and a local clock model will be better suited. While the latter require prior knowledge on which branches to expect a rate difference, a random local clock model may offer a valuable 'naive' alternative (- the substitution model should be specified- line 209: "significant ESSs" => reword to "high ESSs" (there is no criterium to bin ESS as significant or not to my knowledge)- fig2a looks like an unrooted phylogeny, but it is not mentioned with which models/software this was inferred? - fig2b: this is not a nice presentation of the phylogeny. Better in this case is to use a rectangular layout. Also, the long tip labels hinder interpreting the tree - if these can be shortened to only contain the necessary information. The authors should use a time axis; why else estimate a time-calibrated phylogeny? The 2 major clades that are discussed should be clearly indicated. The purple for highlighting the clades with good support should be more bright to more clearly distinguis these clades from the rest. I miss a look at how long these cluster's MRCA goes back in time? Are they recent, old, a mixture of both? To when does the the 89-HTLV-clade MRCA date?- about the potential transmission clusters: there clearly is intermixing of STLV with HTLV, suggesting rather frequent cross-species jumps. But is it no also true that the extant diversity of the STLV reservoir is severely underrepresented (undersampling of the STLV reservoir)? If so, what look like transmission clusters can very well be separate cross-species transmission events for which the source is simply not sampled. What arguments do the authors have against this scenario? They mention that 3 ang cluster as a sister clade to a 89-HTLV clade, but I don't see how this backs the idea that this 89-HTLV clade represents human-to-human transmission. A random local clock model can add evidence for either scenario: if there is a rate shift on the branch leading to the MRCA of the 89-HTLV clade such that this evolves more at a higher rate then the branches pertaining to reservoir evolution (of which I suppose less frequent transmission), this would be evidence against multiple cross-species transmissions.- the NHPs in the questionnaire: many have not been shown to possibly harbour STLV-1 (supp table). I think that this substantially weakens the argument in favor of community spread based on "the lack of an association of NHP exposures and HTLV WB positivity" as NHP exposure via this measure does not very directly suggests exposure to STLV-1. Also, Can the lack of an association between NHP exposure and HTLV WB positivity be explained by higher sensitivty of the serological tests for HTLV-1 compared to HTLV2/3 (which are found in 4 of the NHP species)? This should be better discussed.- what is the cluster definition? Only based on support? Why not also take some measure of time/genetic distance into account?##########Results##########- line 236: which levels? Pleeas specify in the Methods section, or detail here. - line 243: "Only a marginally significant association244 between SFV and HTLV co-infection was observed .".=> Is a Fisher exact test not better suited here? > m <- matrix, nrow = 2, ncol = 2, byrow = T)> m   3 14 162 2872> fisher.test(x = m) Fisher's Exact Test for Count Datadata: mp-value = 0.06041alternative hypothesis: true odds ratio is not equal to 195 percent confidence interval: 0.6925923 13.7944348sample estimates:odds ratio  3.795586 ###########discussion:###########- "The overall HTLV seroprevalence in our population was 5.4% .... In accordance with our study’s 4.2% HTLV-1 prevalence"=> contradiction?- " Nonetheless, we also did not find any evidence of recent STLV-1 infection in our population despite high NHP exposure and previous identification of SFV infection in this same group."=> I perhaps overlook it, but how was the recency of HTLV infection established?#############supplementary info:#############what does '0/1' under STLV's reported refer to? Please add this information.Reviewer #2: In this manuscript, the authors studied the presence of HTLV infection in villages from a specific region of the Democratic Republic of the Congo (DRC).It is important to perform such studies in DRC, a very highly populated country of central Africa where relatively few studies have been performed on such viruses in humans. This is an interesting paper that combines serological, viro-molecular and phylogenetic findings and provides some data on the transmission dynamics of HTLV-1 in remote villages in DRC with populations exposed to frequent contact with non-human primates.This study was carried out by teams from the United States, one being headed by William M. Switzer, one of the leading scientists working on zoonoses in particular retroviruses such as HTLV and Simian Foamy Viruses.We were therefore quite surprised by a certain lack of precision, particularly in the Methods and Results sections presented in this study. In this context, the quality of the manuscript should be greatly improved especially by answering the following remarks and questions.Major comments: 1) Population description. Page 7 lines 126 and following. -The authors should clearly specify what was the original purpose of this population-based study and why they chose this particular region of the DRC and specifically these two villages? Are there any previously published results (with the exception of those concerning SFVs) from this population-based study?- In addition, they used only 3,051 of the 4,573 eligible individuals. Can the authors specify whether they think this fact may have introduced a bias in the study results? - What were the types of populations living in these areas ?- Page 7, Line 136: Could the authors describe in more detail the anonymization procedure used for the samples they collected?- How much blood did they sample from children, especially the younger ones, and what method did they use (EDTA tubes or DBS)?2) Results. - It is important to describe the study population in more detail . - It is not clear for the reviewer why the authors grouped the results after the WBs, considering the general HTLV item for sero-epidemiological analysis. Indeed, such HTLV includes, according to Table 1; HTLV-1, HTLV-2 and non-typeable HTLV. First, the authors should eliminate for their analysis those individuals diagnosed with HTLV-2 WB serology. Second, the results for non-typeable WB should be detailed: how many cases were HTLV-1/2 with reactivities to both MTA-1 and K55? It is very likely that in such a large series, some WB will exhibit such a pattern. Third, what does “like” mean after HTLV-1 and HTLV-2? According to the manufacturer of the WB kit, this is not mentioned. Finally, how many HGIP were observed among the 85 indeterminate pattern?.- The authors should first focus their work on the HTLV-1 virus, which is by far the most prevalent and important virus in their population. - Another question is therefore to know what are the criteria to consider that a person HTLV-1 infected with HTLV-1 in their study: either the authors use a serological criterion only with HTLV-1 in WB and HTLV-1/2 including those for which the MTA- band 1 is stronger than the K55 band, or they also add a PCR criterion by including non-typeable profiles for which the HTLV-1 PCR (s) are positive. Authors should select and clearly define their positivity criteria.Thus, the abstract, methods, results and Table 1 should be greatly modified based solely on analyses of HTLV-1 results.This would provide more robust and comparable data with other studies carried out elsewhere in the world and particularly in Africa.Subsequently, the authors may also describe data from the few individuals with HTLV-2 profile. It would be ideal to have specific HTLV-2 PCR as welle as others that can detect HTLV-3 and 4, as the authors know that people infected with the latter two retroviruses may have WB serologies, sometimes resembling HTLV-2. Can the authors comment on this, provide new data and modify their results?Page 31, Table 1: In addition of the number of HTLV-1 positive samples, the authors should add the resulting prevalence of HTLV-1 in each category.Page 32, Table 2: This table presents the odds ratio of being HTLV-1 in a household with at least one HTLV-1 infected member. Given that the prevalence of HTLV-1 increases with age and is higher among women, the results showed in this table are of limited interest and can lead to biased interpretation. Indeed, caution should be taken as the reference group here is the biological child. The odds ratio of being infected for the primary and secondary wife will obviously depend on which one is the mother of the child.Page 32, Table 3: As demonstrated by the authors, the exposure to nonhuman primates and the type of exposure are significantly associated with gender. Additionally, there is a significant difference of HTLV-1 seroprevalence between sex. The analysis of NHP exposure as potential risk factors of HTLV-1 transmission should therefore be at least adjusted on sex, but can also be adjusted on both age and sex. Table 3 should therefore give sex-adjusted OR and the authors should add the number of HTLV-1 positive samples in each category.Minor comments:Introduction Page 4, Line 67: The authors can also quote here a recent paper published on the molecular epidemiology of HTLV-1 especially in Africa .Methods sectionStudy population sub-sectionQuestionnaire administration sub-sectionPage 7, line 141: Regarding the data collected on exposure to non-human primates, how did the authors assess the bias related to unauthorized hunting activities that could result in the minimization of these activities?Serology, PCR, and phylogenetic analysis sub-sectionPage 8, Line 168: Considering the age of the samples collected in 2007, did the authors verify the quality of the DNAs by amplifying a housekeeping gene before carrying out targeted amplifications of the different HTLV genes/fragments?Results NHP Exposer sub-section Page 12: To our knowledge, it is mainly the bite of an PNH that has proven to be the main ”zoonotic” risk factor for being infected with HTLV-1 in these village populations. Authors should isolate this factor in their analysis and not include it with "scratching" and "playing" activities.(see Table 3).HTLV PCR and Phylogenetic Analyses sub-section Page 13: Concerning the PCR results, the authors must, for each serological profile in the WB , give the results of the different PCRs . This would make it possible to know the % of positive PCRs in each category. A table of this kind is available in the manuscript by Djuicy et al. PloS.TND, 2018, Table 1.Page 14: To my knowledge, the first demonstration that Allenopithecus nigroviridis are infected by HTLV-1 genotype b, has been published by Meertens et al. in Virology in 2001. This paper describing the 3 strains included in the phylogenetic analysis  should be quoted and discussed in this article.The different main clades of the phylogenetic tree (2B) should be presented more clearly with separations highlighting the different clades.Pages 14-17: The section between lines 314 and 367 is long and often difficult to read. The authors might consider simplifying and shortening it.DiscussionPage 18: In the introduction of the discussion, the authors could write a few lines on what is currently known about HTLV-1 related diseases in DRC  and the situation of donors blood or pregnant women.In general, authors should be cautious when referring to review articles especially for Africa and HTLV-1. Indeed, very often the methodologies used, such as the tests employed and the populations studied  are very different and not comparable. This applies in particular to the prevalence of HTLV on the African continent (see below).Page 18, line 396: To our knowledge, it is not reasonable to write that the prevalence rate of HTLV on the African continent averages 10%. As the authors are well aware, it is almost impossible for HTLV-1 to give an average for a large region or even a country because of very different prevalence in different geographies. Regional microepidemiology distribution is a main feature of HTLV-1 distribution; this has been well demonstrated in parts of African and southern Japan. Moreover, in the general adult population, even in remote villages, the highest prevalence very rarely reaches 5 to 15%. As far as we know, this prevalence has only been observed in part of the rural population of Gabon. The data obtained in this study are quite comparable and constitute one of the highest prevalence of HTLV-1 observed in Africa. This finding could be clarified by the authors, at least in the abstract, results and discussion.Please comment on this topic and modify the sentence on line 396 accordingly.--------------------Figure Files:https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. 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For instructions see  22 Oct 2020AttachmentHTLV DRC_Rev01_2020_Response.docxSubmitted filename: Click here for additional data file. 26 Oct 2020Dear Dr. Switzer,We are pleased to inform you that your manuscript 'Human T-cell Lymphotropic Virus Type 1 Transmission Dynamics in Rural Villages in the Democratic Republic of the Congo with High Nonhuman Primate Exposure' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. 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All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.Best regards,Johan Van WeyenberghAssociate EditorPLOS Neglected Tropical DiseasesEdgar CarvalhoDeputy EditorPLOS Neglected Tropical Diseases*********************************************************** 8 Jan 2021Dear Mr. Switzer,We are delighted to inform you that your manuscript, "Human T-cell Lymphotropic Virus Type 1 Transmission Dynamics in Rural Villages in the Democratic Republic of the Congo with High Nonhuman Primate Exposure," has been formally accepted for publication in PLOS Neglected Tropical Diseases.We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. 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The final article will be published to the same URL, and all versions of the paper will be accessible to readers.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases. Best regards,Shaden Kamhawico-Editor-in-ChiefPLOS Neglected Tropical DiseasesPaul Brindleyco-Editor-in-ChiefPLOS Neglected Tropical Diseases


Downstream of kinase (Dok) 3 is a member of the Dok family of adaptor proteins known to regulate signaling pathways downstream of various immunoreceptors. As Dok-3 lacks intrinsic catalytic activity, it functions primarily as a molecular scaffold to facilitate the nucleation of protein complexes in a regulated manner and hence, achieve specificity in directing signaling cascades. Since its discovery, considerable progress has been made toward defining the role of Dok-3 in limiting B cell-receptor signaling. Nonetheless, Dok-3 has since been implicated in the signaling of Toll-like and C-type lectin receptors. Emerging data further demonstrate that Dok-3 can act both as an activator and inhibitor, in lymphoid and non-lymphoid cell types, suggesting Dok-3 involvement in a plethora of signal transduction pathways. In this review, we will focus on the structure and expression profile of Dok-3 and highlight its role during signal transduction in B cells, innate cells as well as in bone and lung tissues. The specific response of cells to environmental stimuli requires crosstalk between multiple signaling pathways, and such specificity in signaling is achieved through a class of proteins known as adaptors, which link specific protein partners together, in a reversible manner, via their protein binding modules to elicit the appropriate cellular response . DownstrOf the seven members in the Dok adaptor family, Dok-1, -2 and -3 are found mainly in hematopoietic cells  while thDownstream of kinase 3 is a relatively small protein comprising of approximately 496 amino acids. At the amino terminus, Dok-3 contains a pleckstrin homology (PH) domain involved in plasma membrane localization via its interaction with membrane phospholipids. Most of the PTKs which phosphorylate Dok-3, as well as its downstream binding partners, are localized to cellular membranes; hence the PH domain is crucial to the membrane localization of Dok-3 for the induction of its function. The PH domain is followed by a central phosphotyrosine binding (PTB) domain which facilitates the binding of Dok-3 to phosphotyrosine-containing consensus motif NPXpY found in partnering proteins. This enables Dok-3 interaction with its upstream PTKs, and the initiation of its function as a scaffold protein to nucleate the formation of multimeric protein complexes. At the carboxyl terminus, there is a proline-rich region with multiple tyrosine residues which can serve as docking sites for SH2-containing proteins upon phosphorylation , 8 Figu.Even though the seven Dok family members share structural similarities characterized by an amino-terminal PH domain followed by a central PTB domain, phylogenetic analysis revealed that they cluster into three different subgroups consisting of Dok-1/2/3, Dok-4/5/6, and Dok-7 respectively . Within Unlike Dok-4, -5, and -6 which are preferentially expressed in neural cells and Dok-7 in skeletal muscles , 4. DownDownstream of kinase 3 has been demonstrated to participate in the signaling of B cell-receptor (BCR) in B cells , 11–16, Downstream of kinase 3 is itself activated by phosphorylation. A number of PTKs have been shown to phosphorylate Dok-3 and these include Abl , Src famThe multiple kinases that can phosphorylate Dok-3 and the diverse classes of signaling molecules that can interact with Dok-3 reveal the complexity of Dok-3 signaling and highlight its flexibility in orchestrating distinct signaling complexes to achieve specific cellular outcomes .Over the past decades, Dok-3 has generally been implicated in the inhibition of immunoreceptor signaling pathways. However, we now know that this is not always the case. As the roles of Dok-3 in different cell types are starting to be unraveled, accumulating evidence suggests that Dok-3 can function, in a context-dependent manner, as both a positive and negative regulator of signaling pathways, in both immune as well as non-immune cells .Downstream of kinase 3 was discovered and cloned as a tyrosine-phosphorylated protein which interacts with two inhibitory molecules, SHIP and Csk. SHIP, a 5′ inositol phosphatase implicated in the negative regulation of immunoreceptor signaling, is observed to bind to the PTB domain of Dok-3 via their tyrosine-phosphorylated residues, and this interaction is further facilitated by the association between their SH2 domain and the tyrosine-phosphorylated residues on Dok-3. On the other hand, Csk, a PTK involved in the negative regulation of Src family kinases, is able to contact the tyrosine-phosphorylated residues in the carboxy-terminal region of Dok-3 with their SH2 domain to mediate binding. Since Dok-3 has the tendency to interact with these two inhibitory molecules upon engagement of BCR, and overexpression of Dok-3 inhibited BCR-mediated nuclear factor of activated T-cells (NFAT) activation and Interleukin (IL)-2 secretion in stimulated B cells, this study provided the first line of evidence that Dok-3 functions as a negative regulator of immunoreceptor signaling in B cells  Figure .in vivo -331/SH2 interaction between Dok-3 and Grb2, and PTB/pTyr-1020 within NPXY motif between Dok-3 and SHIP-1. As such, Dok-3 is able to regulate the subcellular localization of SHIP-1 and position it in the vicinity of plasma membrane for dephosphorylation of the lipid phosphoinositol 3,4,5-trisphosphate (PIP3). In this way, it is able to limit plasma membrane recruitment of BCR effector proteins and thus the efficiency of BCR signal transduction including intracellular calcium fluxes  cells, the generation of antigen-specific plasma cells and their antibody responses were severely compromised. These Dok-3-deficient B cells were unable to sustain and upregulate the expression of programmed cell death 1 (PD-1) ligand 1 (PDL1) and PD-1 ligand 2 (PDL2) respectively, owing to enhanced calcium signaling due to the lack of negative regulation by Dok-3 as described above. Hence, Dok-3 plays a role in the antigen-driven phase of B-cell differentiation  B and T follicular-helper . The PTB domain of Dok-3 binds to the immunoreceptor tyrosine-based activation motif (ITAM) of DAP12, an adaptor protein which couples to various cell surface immunoreceptors such as TLRs. Upon challenge with low-dose LPS, Dok-3 is tyrosine phosphorylated by Src family kinases in a DAP12-dependent manner, resulting in their translocation to the plasma membrane. Subsequently, tyrosine-phosphorylated Dok-3 associates with Grb2 and Son of sevenless homology 1 (SOS1) and sequesters Grb2 from SOS1 to inhibit downstream activation of the Ras-Erk pathway and the production of pro-inflammatory cytokines such as tumor necrosis factor (TNF) α . In HEK2Contrary to its well-established role as a negative regulator downstream of immunoreceptors, Dok-3 has also been demonstrated to play a positive regulatory role in macrophages during interferon (IFN)-β production in response to influenza virus infection or polyinosinic-polyribocytidylic acid [poly(I:C)] stimulation. Upon stimulation of TLR3, Dok-3 is tyrosine phosphorylated by BTK, and its carboxyl SH2-targeting domain forms a scaffold for the nucleation of TRAF3 and TBK1. As a result, TBK1 is activated by phosphorylation on Ser 172, possibly through autophosphorylation or by an unknown effector kinase, for the downstream induction of IFN regulatory factor (IRF) 3 and hence IFN-β production  Figure .The compensatory effect of Dok-3 for other closely related Dok family members, Dok-1 and Dok-2, has also been highlighted in the context of macrophages. Studies in mice showed that combined ablation of Dok-1, Dok-2, and Dok-3 promoted the development of histiocytic sarcoma, a malignant proliferation of tissue-resident macrophages. While macrophages lacking Dok-3 alone or Dok-1 and Dok-2 in combination did not show abnormalities, cells with combined loss of Dok-1, Dok-2, and Dok-3 demonstrated enhanced proliferation, suggesting that the Dok proteins mutually compensate for each other’s function and cooperatively limit the proliferation of macrophages in response to macrophage colony-stimulating factor (M-CSF) and granulocyte-macrophage colony-stimulating factor (GM-CSF). However, how ablation of Dok proteins can contribute to the malignant transformation of macrophages, leading to histiocytic sarcoma remains unknown . SpecifiCandia albicans  and their differentiation into alveolar type II (AT2) cells, leading to neoplastic transformation of the lung. Indeed, these n humans .In addition, Dok-1, -2, and -3 function cooperatively to prevent inflammation and maintain homeostasis in the lungs. While Dok-3 single knockout and Dok-1/2 double knockout mice were clinically well, combined ablation of these Dok proteins resulted in pulmonary inflammation, with increased infiltration of macrophages, eosinophils, neutrophils and lymphocytes in the lungs, resulting in an asthma-like airway disorder. An increased production of Th2-type cytokines, including IL-4, -5, and -13, was observed in the bronchoalveolar lavage fluid (BALF) of the triple knockout mice, contributing to inflammation of the airway. Hence, Dok-1, -2, and -3 act cooperatively to negatively regulate pulmonary inflammation .Adaptor proteins confer specificity in signaling events through their protein binding modules which dictate their binding partners, activity and subcellular localization, thereby regulating protein-protein interactions in a spatially and temporally regulated manner. Downstream of kinase 3 belongs to the Dok family of adaptor proteins, and it has some degree of homology, and as such overlapping functions, with other Dok members, in particular Dok-1 and -2. However, over the past years, emerging evidence suggests that Dok-3 plays a unique role in regulating cell signaling pathways via interaction with a distinct set of signal transducing molecules. Even though it was initially recognized as a negative regulator of immunoreceptor signaling, Dok-3 was later shown to possess both activating and inhibitory functions, in both lymphoid as well as non-lymphoid tissue. Hence, it is becoming clear that Dok-3 is a complex adaptor associated with a plethora of diverse cellular processes implicated in health and diseases such as cancer, bone homeostasis, immune suppression, as well as anti-viral and -fungal immunity. Since Dok-3 was also found to be expressed in organs like colon, reproductive tract and urinary bladder , it is iGiven the importance of Dok-3 in the mechanistic regulation of cell signaling and its implication in human health and diseases, it will be exciting to see in near future the discovery of drugs which could block the specific interaction of Dok-3 with its various binding partners for prophylaxis or treatment of diseases. For instance, association between Dok-3 and Card9 is pivotal for suppressing Card9 signaling and hence restraining the fungicidal properties of neutrophils such as phagocytosis, pro-inflammatory cytokines release and NETs formation during fungal infection . By desiEven though adaptor proteins like Dok-3 are essentially viewed as “undruggable,” mainly due to the lack of well-defined binding pockets which modulate protein-protein interactions, recent progress in drug discovery has proven that they are amenable to drug inhibition. High affinity regions termed “hot spots” which drive protein binding have been mapped onto protein-protein interfaces, and these hot spots are particularly adept at binding small-molecule or peptide inhibitors , 33. SucTaken together, Dok-3 is a complex adaptor which forms an integral part of a highly diverse functional network within cells. Although we have gained rich knowledge of Dok-3 signaling and their interacting partners in recent years, its underlying expression and functions in other cell types, tissues and organs remain largely unexplored. With a clear understanding of its signaling mechanisms and physiological roles, there exists an opportunity for the development of Dok-3 inhibitors for clinical intervention of various diseases. Thus, we anticipate in the near future further progress in our understanding of Dok-3 in the field of signal transduction, and the emergence of small-molecule inhibitors of adaptor-binder interactions as a potential therapy to modulate human diseases.JTL and JKHT wrote the manuscript. K-PL conceived the idea. H-HL and K-PL edited and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The present study aimed to prepare usnic acid (UA)-loaded chitosan (CS) nanoparticles (UA-CS NPs) and evaluate its antibacterial activity against biofilm-forming pathogenic bacteria. UA-CS NPs were prepared through simple ionic gelification of UA with CS, and further characterized using Fourier transform infrared spectroscopy, X-ray diffraction, and field-emission transmission electron microscopy. The UA-CS NPs presented a loading capacity (LC) of 5.2%, encapsulation efficiency (EE) of 24%, and a spherical shape and rough surface. The maximum release of UA was higher in pH 1.2 buffer solution as compared to that in pH 6.8 and 7.4 buffer solution. The average size and zeta potential of the UA-CS NPs was 311.5 ± 49.9 nm in diameter and +27.3 ± 0.8 mV, respectively. The newly prepared UA-CS NPs exhibited antibacterial activity against persister cells obtained from the stationary phase in batch culture, mature biofilms, and antibiotic-induced gram-positive and gram-negative pathogenic bacteria. Exposure of sub-inhibitory concentrations of UA-CS NPs to the bacterial cells resulted in a change in morphology. The present study suggests an alternative method for the application of UA into nanoparticles. Furthermore, the anti-persister activity of UA-CS NPs may be another possible strategy for the treatment of infections caused by biofilm-forming pathogenic bacteria. Studies have shown that the formation of persister cells by most pathogenic bacteria is a challenging task when treating infections using antimicrobial agents ,2,3. PerThe naturally isolated antimicrobial agents are considered eco-friendly, cost-effective in isolation, biocompatible, and with the least cytotoxic effect ,11,12. RListeria monocytogenes and Staphylococcus aureus) and gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa), which are produced in stationary phase in a batch culture, in the biofilm state, or antibiotic-induced. Chitosan is a natural linear polycationic amino polysaccharide derived from chitin by enzymatic and chemical methods ,25. Due Biocompatibility, easy availability, cost-effectiveness, and biodegradability of chitosan make it widely accepted as a carrier of several hydrophobic, hydrophilic, and unstable drugs ,26. In tThe loading of UA into CS NPs was confirmed by release assay (using UV-Vis spectrophotometry) and FTIR analysis. Reports have shown that UA presents various absorption spectra when dissolved in different solvents ,32,33. IHence, to check the encapsulation of UA in the CS NPs, we determined the release of UA in the buffered solution by measuring the absorption at 337 nm . The relControlled release of UA was observed at pH 1.2, 6.8, and 7.4 B. The inThe controlled release of several drugs from CS NPs has also been reported for other drugs, such as chlorogenic acid and carvacrol ,39. The −1 was observed, which was previously identified as the –C=O stretching of conjugated cyclic ketones. Similarly, the peak at 1632 cm−1 corresponds to the methyl ketone. The bands at 1283 and 1072 cm−1 correspond to the antisymmetric and symmetric m(COC) aryl alkyl ether modes, as identified by Pagano et al. [2 stretching), 2875 (-CH stretching), 1647 (C=O stretching of amide I), 1153 (asymmetric stretching of C-O-C bridge), and 1025 cm−1 (C-O stretching)  A. These Along with the appearance of UA peaks, some characteristic peaks of chitosan have also been observed in the FTIR spectra of UA-CS NPs, which indicates that there might be some physical interaction between UA and CS. Thus, based on the release assay, and FTIR results, we concluded that the UA was encapsulated in the chitosan polymeric nanoparticles. Furthermore, the crystallographic structure of the UA-CS NPs was determined using XRD. The XRD spectra showed several sharp characteristics as well as diffuse peaks in the diffraction pattern of the UA-CS NPs B. The chThe diameter (hydrodynamic) of the UA-CS NPs was determined using dynamic light scattering (DLS) A. The paE. coli was 1024 µg/mL  [S. aureus) [Mycobacterium aureum and M. tuberculosis) [S. epidermidis was 5–10 µg/mL [S. aureus and 256 µg/mL against P. aeruginosa strain pMF230. The MIC value of UA against E. coli is not available; however, a MIC value of 0.39 µg/mL against L. monocytogenes was reported using a disc diffusion method [P. aeruginosa and S. aureus compared to the previously reported MIC values for free UA. The higher MIC value of UA-CS-NPs against these bacterial strains might be due to the encapsulation of UA inside the CS NPs, which results in the lowest availability of free UA, since approximately 76% release of UA at pH 7.4 was determined in the release assay  , 64 µg/m aureus) , and 32 culosis) . Similar10 µg/mL . Francol10 µg/mL  reportedse assay B. The maE. coli, P. aeruginosa, and S. aureus present high numbers of persister cells [E. coli and P. aeruginosa occurred at 1024 µg/mL of UA-CS NPs  was observed , above MIC (P. aeruginosa), and both MIC and sub-MIC (S. aureus and L. monocytogenes) of UA-CS NPs. However, variable killing occurred in persister cells obtained from biofilms or induced by the antibiotic. In addition, the colony morphology of E. coli observed on the tryptic soy agar (TSA) plate differed, with larger cells in the stationary phase as compared to the size of biofilm- and antibiotic-induced cells. Similarly, the colonies of P. aeruginosa, L. monocytogenes, and S. aureus were found to be slightly smaller in the case of biofilm- and antibiotic-induced cells. A possible explanation for the smaller colonies and tolerant properties of the cells towards the drug is the exposure to stressful environmental conditions such as biofilms and antibiotics [It was concluded that UA-CS NPs exhibited potential killing activity against different types of persister cells, either produced in naturally growing cultures or induced upon exposure to the antimicrobial agents. It was concluded that the killing of persister cells by UA-CS NPs occurred differently in the case of cells from stationary phase from batch cultures compared to the persister cells isolated from biofilms or induced by the antibiotic. The cells from stationary phase cultures of all bacteria were completely killed by the MIC , irregular, swollen, thickening, and oval cells (in the case of S. aureus), and shrinkage of the cells (in the case of E. coli and P. aeruginosa).The effect of UA-CS NPs on the morphology of gram-positive and gram-negative bacteria was examined using SEM. The sub-inhibitory concentration of UA-CS NPs for each bacterium was used to evaluate the changes in cell morphology . In the he cells A. Furthe control B. The shA-CS NPs B. In theol cells C. Similand ¼-MIC D. From tS. aureus upon exposure to UA [L. monocytogenes is a unique finding in the present study. Numerous studies have reported the elongation and filamentation of L. monocytogenes cells in the presence of several environmental stress conditions such as temperature, salt, pH, and antimicrobials [L. monocytogenes cells by UA-CS NPs in the present study might be due to the inhibition of cell division. The genome of L. monocytogenes contains the lmo1071 gene that encodes the FtSW protein, which is responsible for cell division; when the gene is mutated cells become elongated [L. monocytogenes is an adaptive mechanism to combat and survive under stressful environmental conditions. Similarly, the thickening of the cell wall is another defensive mechanism against environmental stresses and antimicrobial agents, as it has been found in staphylococci in the presence of UA and some of the conventional antibiotics [Furthermore, unique morphological changes such as a shrunken surface with irregular folds, swollen, cell wall thickening, oval, and irregular shape have been reported for methicillin-resistant re to UA . The elocrobials ,77,78. Tlongated . Hence, longated . This moibiotics ,81.E. coli, P. aeruginosa, S. aureus, and L. monocytogenes cells were killed by the MIC and sub-MIC of UA-CS NPs. The bacterial morphology was changed upon treatment with sub-MIC of UA-CS NPs, resulting in elongation (L. monocytogenes), irregular, swollen, thickening, and oval (S. aureus), or shrunken cells (E. coli and P. aeruginosa). Future studies are required to investigate the killing mechanism of UA-CS NPs towards different types of persister cells. Although, chitosan is a good biocompatible material, however, further studies are required to assess the cytocompatibility of UA-CS NPs in vivo and also its efficacy using some eukaryotic model organism. Based on the present findings, the encapsulated UA in CS NPs may be applied to treat the infection caused by biofilm-forming pathogenic bacteria. This study also provides a new direction for the loading of the least stable and hydrophobic drug into biocompatible chitosan for antimicrobial therapy.With the aim of improving the bactericidal activity of UA, its encapsulation into CS NPs was successfully conducted by a simple ionotropic gelification method. The encapsulation of UA into the CS NPs was confirmed by release assay, FTIR, XRD, and FE-TEM. The percentages of EE and LC were 24% and 5.2%, respectively. The shape of the UA-CS NPs was spherical and rough surface with an average diameter of 311.5 ± 49.9 nm. The UA-loaded CS NPs exhibited bactericidal activity towards gram-positive and gram-negative bacteria. Different types of persisters such as stationary phase cells, persisters obtained from mature biofilms, and antibiotic-induced Escherichia coli (KCTC1682), Listeria monocytogenes (KCTC3569), Pseudomonas aeruginosa PAO1 (KCTC1637), and Staphylococcus aureus (KCTC 1916) were obtained from the Korean Collection for Type Cultures . The growth media for the cultivation of L. monocytogenes, S. aureus and P. aeruginosa was tryptic soy broth , whereas, for E. coli, Luria Bertani (LB) broth was used. Usnic acid, Tween 60, low molecular weight chitosan with 75–85% degree of deacetylation (CAS # 9012-76-4), and tripolyphosphate (TPP) were purchased from Sigma-Aldrich Co. . The methodology used for the preparation of UA-CS NPs was a single-step ionotropic gelification method as described earlier with slight modifications ,39. Brie−1 using 16 scans at a resolution of 4 cm−1. The X-ray diffraction patterns of the prepared CS NPs (loaded or unloaded with UA) were carried out using an X’Pert-MPD PW 3050 diffractometer .The detailed characterization of the newly prepared UA-CS NPs was carried out using different instrumentations and the procedures as described earlier ,83. The The amount of UA encapsulated in the CS NPs was determined by measuring the absorption spectrum using UV-vis absorption spectroscopy. The percentage of encapsulation efficiency (EE) and loading capacity (LC) were determined according to the previously explained procedure ,48. In tThe experiment was conducted in triplicates and repeated three timesThe release kinetic of UA from the CS NPs was also determined by measuring the absorption at 337 nm as described earlier ,22,48. FThe bactericidal properties of UA-CS NPs were determined by microbroth dilution method as described earlier . BrieflyE. coli, tetracycline for P. aeruginosa and S. aureus, and ciprofloxacin for L. monocytogenes were carried out according to the previously described procedure [600 ~1.0. These cells were treated with the MIC value of UA-CS NPs and different antibiotics at the concentration of 100 µg/mL and incubated at 37 ℃ for 3 h under shaking (250 rpm). The viability of each bacterial cells was enumerated by serial dilution (10−6 dilution) and spread plating (100 µL) on TSA plate. Similarly, the formation of persister cells from mature biofilm (grown for 5 days) was also measured as described below. The free-floating planktonic cells (obtained from the 5-day mature biofilm) from the 96-well microtiter plate were removed and the adhered cells in the wells were washed twice with fresh culture media and scraped off. These cell suspensions were serially diluted (10−6 dilution) in fresh culture media and spread plated (100 µL) on TSA plate. These TSA plates were incubated at 37 ℃ and the CFU of bacterial colonies that appeared on the TSA plate was determined. The experiment was conducted in triplicates and repeated three times.The effect of UA-CS NPs on the formation of persister cells from selected bacteria, which were induced by antibiotics such as ampicillin for rocedure ,70. Brie600 = 3.0). After incubation, the full-grown cells were harvested by centrifugation (13000 rpm for 10 min). The obtained cell pellets were washed three times with PBS (pH 7.4). Finally, the cell pellets were re-suspended in PBS and adjusted the OD to 0.2. These cells (240 µL) were placed in a 96-well microtiter plate along with different concentrations of UA-CS NPs (ranging from 256 and 1024 µg/mL) and incubated under shaking (567 cpm) at 37 ℃ for 6 h in a microplate reader. After incubation, the cells were serially diluted (up to 10−6 dilution) in fresh TSB and spread plated (100 µL) on the TSA plate. These plates were incubated overnight and appeared colonies were counted. The experiment was conducted in triplicates and repeated three times.The isolation of stationary phase culture was carried out as described earlier with slight modification . A singlP. aeruginosa, S. aureus, L. monocytogenes, and E. coli was allowed to establish in their respective growth media at 37 ℃ for 5 days as described earlier [−6) in fresh TSB and spread plated (100 µL) on the TSA plate. These plates were incubated overnight and counted the appeared colonies. The experiment was conducted in triplicates and repeated three times.Firstly, the mature biofilm of  earlier . The fre600 = 3.0). Previous reports showed that when stationary phase cell culture was treated with antibiotics, additional persister cell formation was formed as a result [P. aeruginosa and S. aureus at the working concentrations of 100 µg/mL, whereas the cell culture of L. monocytogenes and E. coli were treated with ciprofloxacin and ampicillin at 100 µg/mL. The antibiotic-treated cell cultures were grown under shaking conditions (250 rpm) at 37 ℃ for 3 h. The antibiotic-induced persister cells were harvested by centrifugation (13000 rpm for 10 min) followed by washing with PBS three times. Obtained pellets were re-suspended in PBS and adjusted the final OD with a value of 0.2. These antibiotic-induced persister cells were treated with different concentrations (ranging from 256 and 1024 µg/mL) of UA-CS NPs and allowed to grow at 37 ℃ for 6 h. After incubation, the cell culture was serially diluted up to 10-6 dilution and spread plated (100 µL) on a TSA plate. The TSA was plate incubated overnight at 37 ℃ and their CFU were determined. The experiment was conducted in triplicates and repeated three times.The antibiotic-induced persister cells were treated with UA-CS NPs and the effect on its number was enumerated by counting the cells on the TSA plate. The procedure was executed as described earlier with slight modifications . The seea result ,70. ThesThe impacts of UA-CS NPs on the cell morphology of bacteria was checked under scanning electron microscopy (SEM) as described earlier with slight modification . Brieflyp < 0.01 and * p < 0.05 were considered as significant. The GraphPad Prism 7.0  was used to plot all graphs. Furthermore, the statistical analysis of each experimental data was also executed using one-way ANOVA, and results were presented as means ± SD. ** UA, usnic acid; CS, chitosan, NPs, nanoparticles; CS NPs, chitosan nanoparticles; UA-CS NPs, usnic acid-chitosan nanoparticles; SEM, scanning electron microscopy; EE, encapsulation efficiency; LC, loading capacity; FETEM, field emission transmission electron microscopy; FTIR, Fourier transform infrared spectrometer; DLS, dynamic light scattering; MIC, minimum inhibitory concentrations.


When presented with a set of possible reach targets, the movement trajectory can reveal aspects of the underlying competition for action selection. Current goals and physical salience can affect the trajectory of reaching movements to be attracted towards a distractor. Some studies demonstrated that stimuli associated with reward can also cause an attraction when reaching towards the reward stimulus was previously rewarded and the reward stimulus was physically salient. Here we demonstrate that a non-salient stimulus that signals the availability of reward attracts reaching movements even when moving towards it was never necessary nor rewarded. Moreover, the attraction by reward is particularly evident with short-latency movements. We conclude that neither physical salience nor reinforcing the movement towards a stimulus is necessary for reward to gain priority in the selection for action. Movements towards a target can be influenced by the current goals of the observer , two participants due to data corruption and one participant for misunderstanding the instructions. This resulted in a final sample of 20 participants . The sample size was estimated using GPower ) on a gray background. An eye-fixation circle presented 2.5 vd below screen center and a finger-start circle presented 1.5 vd below the eye fixation circle  consisting of three coordinates: Lightness (≈luminance), A (green-red axis), and B (blue-yellow axis). The background  and the shapes were isoluminant. The six shape colors had equal chroma, and were equally spaced around the LAB color space . For each participant, one of these colors was randomly chosen as the high reward-signaling color and another one as the low reward-signaling color. On high- and low-reward trials one of the distractors was in the high- or low-reward-signaling color, respectively. The colors of the other shapes were randomly sampled from the remaining set of colors without replacement. On baseline trials the colors of all shapes were sampled from the remaining set of colors.The fixation/start screen was presented for a randomized minimum time of 500 ms or 750 ms, or until gaze and finger position were evaluated positively . Next, the search display was presented for 1,000 ms or when a reach end was detected (finger velocity dropped below 20 mm/s after moving more than 40 mm from the start position). Only when the reach ended before a variable deadline and within 28 mm from the center of the target shape, was the trial evaluated as correct. The variable deadline was based on the 80th percentile of the response times (reach latency + movement time) of the last 100 trials in which participants reached the target before the offset of the search display, i.e. within 1,000 ms. Before 100 trials were gathered, the variable deadline was set to 700 ms based on another study from our group with a similar design  of target position (4), distractor position relative to the target (3) and distractor condition (3) were counterbalanced; resulting in 22 repetitions of each combination.2 across the four points. The endpoint of a reach was defined as the point in time when the velocity dropped below 20 mm/s  of missing reach data due to the temporarily blocked view of the marker were interpolated for each dimension separately. In the offline analysis, the starting point of a reach was defined as the first sample of four consecutive vector velocity readings greater than 25 mm/s where there was a total acceleration of 20 mm/sTrials were excluded when at least one of the following criteria was reached: a saccade of >2.5 vd was detected; the reach end was more than 28 mm away from the target center; the reach start was more than 10 mm from the finger start circle; the maximum reach velocity was >5,000 mm/s; the reach latency was <200 ms or >600 ms. Over all criteria and all participants 9.72% of trials were excluded.To determine whether reaching movements deviated towards or away from the reward-signaling distractor, we calculated the attraction score  for short and 366 ms (SD = 43 ms) for long latency bins.F = 8.461, p = 0.009, ηp2 = 0.308, which showed that reaching movements curved more towards the high- compared to the low-reward-signaling distractor. There was no main effect of reach latency, F = 2.252, p = 0.15, ηp2 = 0.106. However, the interaction effect between reward and reach latency was significant, F = 10.455, p = 0.004, ηp2 = 0.355, which showed that the effect of reward on reach curvature was more pronounced for short  = 2.698, p = 0.014, d = 0.603; whereas curvature on low-reward trials did not differ from baseline, t(19) = 0.128, p = 0.899, d = 0.029. Hence, reaches curved towards the high-, but not low-, reward-signaling distractor compared to baseline. We further tested whether reach curvature differed between short latency high-reward trials and long latency high-reward trials. We found that reach curvature on high-reward trials was more pronounced for short compared to long reach latencies, t(19) = 2.575, p = 0.019, d = 0.576. Furthermore, for long reach latencies, curvature was not different between high reward and baseline trials, t(19) = 1.739, p = 0.098, d = 0.389.To investigate whether a high-reward-signaling stimulus, that is physically non-salient and never a target, influences the curvature of reaching movements to a searched target Fig. , we perfort Fig.  comparedort Fig.  reach lar(18) = -0.573, p = 0.008.Next, we investigated whether participants whose latency was shorter showed a larger influence of reward on curvature. We ran a between-subjects Pearson correlation between reach latency and the effect of reward on curvature (high reward – low reward). We found that the effect of reward was larger for participants with shorter reach latencies; Analyses). The results show that the high-reward-signaling distractor attracted the reaching movement more from movement onset until 71% along the trajectory than the low-reward-signaling distractor. For short-latency movements  = 0.206, p = 0.814, ηp2 = 0.011; movement time, F = 3.115, p = 0.056, ηp2 = 0.141; nor reaching endpoint accuracy, F = 1.873, p = 0.168, ηp2 = 0.090. For completeness, we ran paired-samples t-tests to investigate the trending effect of reward on movement time. Movement time was significantly different between low- (305 ms) and high- (309 ms) reward trials, t(19) = 2.415, p = 0.026, d = 0.540; but not between baseline (305ms) and low-reward trials, t(19) = 0.304, p = 0.764, d = 0.068; or baseline and high-reward trials, t(19) = 1.740, p = 0.098, d = 0.389.To investigate whether movement latency, movement time, and reaching endpoint accuracy were affected by reward, we ran one-way ANOVAs with the factor reward . The effect of reward was not significant for movement latency, The current study shows that a physically non-salient stimulus signaling high in contrast to low reward attracts reaching movements. Reaching movements clearly deviated towards the high-reward-signaling distractor even though it was never the reach target and reaching towards it was never rewarded. Conversely, reaching towards the distractor would have led to the omission of reward. The attraction by the high-reward-signaling distractor was present in short- but not in long-latency movements. The attraction of the reaching movement can be attributed to the reward-signaling stimuli obtaining an increased selection priority for action independently of physical salience. The priority for selection increases the likelihood of the activation of a movement plan towards the high-reward-signaling distractor. The activation of the movement plan causes the subsequent reaching movement to be deviated towards the high-reward-signaling distractor if the competition with the movement plan is not resolved. The competition is then resolved by suppressing the movement plan to the distractor. As this suppression increases gradually, attraction by the reward-signaling distractor is more pronounced for short than long-latency movements.The current findings advance our understanding of how reward association can shape the selection for action. In previous studies (Chapman et al., Moher et al.  observedThe current findings show similarities with studies on the influence of reward-associated stimuli on visual selection. Failing et al.  found thThe prioritization of reward-signaling stimuli during visual selection has been argued to be strategic, to gain information and reduce uncertainty about the possible outcome, rather than an involuntary attraction driven by reward value (Le Pelley, Mittchel, Beesley, George, & Wills, Together, our findings show that a reward-associated stimulus gains priority in the action selection process, even when it is not physically salient, is never a movement target, and reaching towards it is never rewarded.


Uric acid (UA) level is of the valuable signs of inflammation. However, the role of UA in the outcomes of hematopoietic stem cell transplantation (HSCT) such as GVHD and patients’ overall survival is still a matter of debate. In this study, we aimed to evaluate the relationship between UA levels and GVHD incidence and overall survival in allogeneic HSCT patients.A total of 201 patients who were admitted for allogeneic transplantation at Taleghani hospital, Tehran, Iran, were considered for retrospective analysis. Serum UA levels from 1 week before transplantation until 2 weeks after transplantation were used to determine thresholds and find out the association of serum UA levels with GVHD and overall survival.We showed that the determined thresholds using receiver operating characteristic curves have poor predictive value for GVHD and overall survival. The patients with serum UA higher than 3.4 mg/dL had 37% lower odds of GVHD incidence and 35% lower hazard of death than patients with UA lower than 3.4 mg/dL.Our results indicated that serum UA levels lower than 3.4 mg/dL could significantly increase the incidence of GVHD and hazard of death. The antioxidant functions of UA could explain the lower incidence of GVHD in hyperuricemic patients. However, the inconsistencies of the previous studies require further investigation to elucidate the role of UA in the prediction of GVHD. About 50 000‐60 000 transplantations are performed annually, worldwide.22.12 of body area/d from day −6 to −3) or MAC‐3 which is MAC‐2 plus antithymocyte globulin  . Aplastic and Fanconi anemia patients received CY 60 mg/kg/d on day −3 and −2 with ATG . Patients with HD and NHL received Reduced Intensity Conditioning regimen (RIC) consists of Flu 30 mg/m2 of body area for 5 days, CCNU  100 mg/m2 for 2 days and Melphalan  40 mg/m2 for 1 day. GVHD prophylaxis was methotrexate  plus cyclosporine A  with or without ATG. The course of methotrexate therapy was 10 mg/m2 of body surface on days +1, +3, +6, and +11 and for cyclosporine was 5 mg/kg/d in two divided doses on day −5. The level of cyclosporine was maintained between 200 and 300 ng/mL. Also, the ATG was given as 1.5 mg/kg/d on days −3, −2, and −1.A total of 201 patients with hematological disorders who were admitted for allogeneic transplantation at Taleghani hospital, Tehran, Iran, between 2008 and 2018 were considered for retrospective data analysis. All patients were categorized based on their diagnosed disease, including acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), aplastic and Fanconi anemia , Hodgkin disease (HD), non‐Hodgkin lymphoma (NHL), and others. These patients gave the informed consent that their data were used for scientific analysis, and the study was approved by the Ethics Committee of the Shahid Beheshti University of Medical Sciences . The samples of bone marrow were used for disease diagnosis by standard techniques such as karyotyping, hemograms, and cell surface marker detection. High‐dose chemotherapy‐based myeloablative conditioning (MAC) regimens were administered for allo‐HSCT including ALL, AML, and CML received MAC‐1 regimen  or MAC‐2  were mobilized after 4 days of 10 mg/kg G‐CSF  administered in HSCT donor. Apheresis time was 250 minutes depending on the volume and speed of blood flow of PBSC donor. Cell viability in apheresis product was assessed using the trypan blue staining  and enumeration on a Neubauer chamber with light microscopy. Also, the counts of CD34+  cells and CD3+  cells were evaluated by flow cytometry .The standard clinical signs such as rash, diarrhea, and liver function abnormalities associated with biopsy and histopathological criteria in involved organ were main manifestations for diagnosis of GVHD according to the GVHD criteria provided by National Institute of Health.2.3Serum UA levels were measured from day −7 until day +14. Peripheral blood samples were collected, and UA levels in serum samples were evaluated using an ADVIA 1800 clinical chemistry analyzer (Toshiba).2.4Data were expressed as means ± standard deviation (SD) or frequency (%). The main purpose of this study was to identify the effects of serum UA on the incidence of GVHD and the overall survival with the adjusted effect of other risk factors. The logistic regression was employed when the outcome variable was GVHD. The odds ratios, 75 and 90% confidence interval (CI), were calculated accordingly. When the outcome was the overall survival, we conducted a Cox regression analysis. The hazard ratios, 75 and the 90% CI were evaluated as well. The median of serum UA in each disease category as well as the correlation of UA level with the overall survival was determined.P‐value <.25 in univariable analysis and P‐value <.1 in multivariable was considered as significant. All analyses were conducted through the SPSS version 19.0 (SPSS Inc).The threshold for serum UA level in three different periods of time including 1 week before transplant, 1 and 2 weeks after transplant was determined using both the receiver operating characteristic (ROC) curves and median. The sensitivity, specificity, and the area under curve (AUCs) were also assessed in ROC curves analysis. Finally, due to the importance of the week before transplantation, the median of UA level in the time period between day −7 to the transplantation day was selected as the cutoff and utilized in the univariable and multivariable analysis. The survival curve was drawn based on the median of serum UA. The 33.1The descriptive statistics is shown in Table 3.2Beside the median of serum UA levels during the week before transplantation as the main cutoff point for UA in this study, the ROC curve was also drawn to determine a cutoff and evaluate the predictive values of serum UA level before and after transplant for GVHD. As shown in Table 3.3In order to determine the predictive values of serum UA level before and after transplant for overall survival, we performed ROC analyses. The results are indicated in Table 3.4P‐value = .15). The blood group also significantly affected the GVHD outcome. The patients with blood group A and B had 96% and 64% higher odds of incidence, consecutively compared to blood group O , . Other factors such as diagnosed disease, CMV, and conditioning regimen did not have any significance effect on the incidence of GVHD. The results of the multivariable analysis were significant for blood group A which revealed that the odds of incidence in patients with blood group A was half the patients with blood group O .We conducted a univariable and multivariable logistic regression to identify the pre‐transplant risk factors for GVHD. The results are illustrated in Table 3.5P‐value = .20). The diagnosed disease was a risk factor which suggested a significant effect on the hazard of death . The hazard of death in patients with AML was almost half of those classified in subgroup “HD” .We applied a Cox proportional hazards model in order to determine the significant risk factors on the survival of the patients. As it is illustrated in Table P‐value = .08). Prophylaxis regimen of CSA + MTX+ATG increased the death hazard up to 2.14 times greater than the regimen of CSA + MTX . The patients with serum UA higher than 3.4 mg/dL had the hazard of death 35% lower than patients with UA lower than 3.4 mg/dL . The survival curve illustrated that the overall survival of patients with the median serum UA levels higher than 3.4 mg/dL was better than the patients whose median UA levels were lower than 3.4 mg/dL .The conditioning regimen was among the influential factors illustrating that in MAC2 regimen, the hazard of death was 2.63 times greater than the RIC regimen (CI: 1.37‐5.02], .37‐5.02,4The radio/chemotherapy, along with the conditioning regimen before the HSCT, cause massive cell death in all organs of the patients. Dying cells release a variety of molecules which is called DAMPs or danger signals including extracellular adenosine triphosphate, UA, high mobility group box‐1 protein, etc.The accessibility and ease of measurement together with its role in the inflammatory conditions, such as HSCT, make the serum UA as an attractive biomarker in researches to predict the GVHD.The purpose of this study was to identify the effects of serum UA on the incidence of GVHD and the overall survival with the adjusted effect of other risk factors. To achieve this goal, serum UA levels of 201 patients with hematological disorders were measured from 1 week before the allo‐HSCT until 2 weeks after transplantation. The threshold for serum UA level in three different periods, including 1 week before transplant, 1 and 2 weeks after transplant was determined using ROC curves. As it is illustrated in the results, the AUC of the cutoff determined by ROC for prediction of both GVHD incidence and overall survival in all three periods was lower than 60% which means these cutoffs have low capacity to predict the outcomes. Moreover, the sensitivity and specificity of the cutoffs were low. These results show that the thresholds achieved by ROC curve are not a reliable cutoff to predict the HSCT outcome.The other method that is used to determine the cutoff for serum UA in our patients was median. The median of serum UA levels in the period of 1 week before the transplantation was 3.4 mg/dL, which was selected as a cutoff for the statistical analysis. Generally, the cutoff point for hyperuricemia is 6.7 mg/dL at which the crystallization of UA begins.Our univariable analysis showed that the cutoff value of serum UA level at 3.4 mg/dL has a significant effect on the occurrence of GVHD in a way that GVHD incidence in patients with UA higher than 3.4 mg/L was 37% lower than patients with the UA below the cutoff. However, the multivariable analysis showed no significant association between serum UA levels and GVHD. This finding contradicts with some previous reports which claimed that the higher serum UA is associated with GVHD incidence and its severity,The probable explanation provided by Ostendorf and his colleagues for the negative association between UA levels and GVHD was the reduced antioxidative capacity in case of hypouricemia.In accordance with the results of GVHD, it is indicated that the lower serum UA levels are associated with a higher risk of death and inferior overall survival. It is found that patients with serum UA higher than 3.4 mg/dL had 35% lower hazard of death compared to patients with UA lower than 3.4 mg/dL. Despite the significant association of low UA levels with the GVHD incidence, Ostendorf et alOf note, including the ATG to the prophylaxis regimen, increased the hazard of death up to 2.14 times greater, compared to the regimen without ATG. It should be noted that all significant results in overall survival came from univariable analysis and multivariable analysis showed no significant effect which indicates that each prognostic factor in the presence of other factors has no significant role in the prediction of OS.Moreover, in our univariable analysis, the patients diagnosed with AML showed better OS among other diseases. Intriguingly, hyperuricemia was reported in one‐third of hematological malignancies such as AML and demonstrated to be associated with poor prognosis and poor survival in AML.Conclusively, we demonstrated that the lower serum UA levels are associated with GVHD incidence and inferior overall survival. Our result is in line with some previous studies and contradicts some others which suggest that there might be an optimum range for UA levels which control the HSCT outcome and UA levels either lower or higher than this range could be detrimental for HSCT outcome. More comprehensive studies can evaluate the validity of this hypothesis.K.GH. SP, SS, and AH participated in designed experiments and critical revision of the manuscript; RN, MHK, and M.S performed data collection and analyzed the data. All authors revised the manuscript and approved the final paper.


Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk  and protective variables . We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking  and physical activity . Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.The online version contains supplementary material available at 10.1007/s00429-020-02184-6. Structural brain changes during normal aging comprise decreases in gray matter (GM) and white matter  while drinking a beer . Only few studies investigated the effects of lifestyle on brain structure and function or on cognition in a multidimensional way. For example, Floel et al.  found thAge Gap Estimation (BrainAGE) score. BrainAGE is positive if aging patterns observed via MRI appear older than expected based on chronological age (accelerated brain aging), and negative if they appear younger (decelerated brain aging). BrainAGE and several other MRI-based brain age prediction models have been established as a meaningful imaging biomarker to study the prediction of future brain aging patterns and their longitudinal trajectories  in the 1000BRAINS cohort of “normal” aging older adults  with an age range of 56–85 years . The study protocol of 1000BRAINS was approved by the Ethics Committee of the University of Essen (Germany). All participants gave written informed consent in agreement with the declaration of Helsinki.For the lifestyle analyses, we used data of those participants aged older than 55 years recruited from the Heinz Nixdorf Recall study  of the Heinz Nixdorf Recall study that commenced in June 2011  within the last 4 weeks was assessed via a self-report questionnaire . To obtain a risk score where a value of zero would indicate a mathematical balance of negative and protective behaviors, an additional linear transformation of the z-transformed lifestyle behaviors was applied: The protective variables were linearly transformed into negative scores by subtracting the maximum value from each individual measurement, whereas risk behaviors were analogously transformed into positive scores by adding the minimum value to each individual measurement. Hence, all values for risk behaviors were positive. Finally, the linearly transformed values of all individual lifestyle variables were summed up into one combined lifestyle risk score.The combined lifestyle risk score was constructed as used in a previous study  and the CAT12 package (http://dbm.neuo.uni-jena.de) running under Matlab . First, T1-weighted images were corrected for bias field inhomogenities and spatially normalized. Then, images were segmented . The following scan parameters were used: TR = 2.25 s, TE = 3.03 ms, TI = 900 ms, FoV = 256 × 256 mms et al. . MRI preM = 60.7 years, SD = 13.4 years; Mmale = 60.7 years, SDmale = 14.0 years; Mfemale = 60.9 years, SDfemale = 12.5 years). Even though our study focuses on the association between lifestyle behavior and brain aging in older adults, we chose to train the RVM on the full cohort of 1000BRAINS for two reasons: (i) from a neuropsychological perspective, brain aging is a highly complex phenomenon, which can - as a pattern - better be learned by the algorithm when having access to a larger age range, which in the full cohort spans the entire adult life span (18.5–87 years), i.e., the algorithm has access to phenotypically richer data. However, using the whole age range has methodological advantages as well (ii): having as many and as morphologically rich training data as possible within the learning phase results in higher  degrees of freedom, higher predictive power, and higher accuracy. This has been shown even though training and test samples differ demographically . The regression of estimated age on chronological age explained up to 52% of the variance  = 1.55, p = 0.213, η2 = 0.002, nor in BrainAGE, F = 1.35, p = 0.246, η2 = 0.002. Mean estimated age was 1.28 years lower for females than for males with F = 3.98, p = 0.046, η2 = 0.005  = 39.50, p < 0.0001, η2 = 0.06]. No correlation between BrainAGE and education was found (p = 0.937), even when stratifying the analyses for the two sexes , reflecting a rather protective behavior within the selected older subsample of 1000BRAINS.In our first analysis of lifestyle, we investigated the association between combined lifestyle risk and BrainAGE. Table We now investigated how well combined lifestyle risk predicted BrainAGE in the multiple linear regressions implementing all three approaches to deal with chronological age . Please also see Table β = 0.182 to β = 0.183, all p < 0.001. These results remained stable, even after outlier exclusion , as well as cognitive status (DemTect) and BMI  as covariates. All of these associations were significant at padjusted ≈ 0.003. Within the fully adjusted model, the combined lifestyle risk score predicted 2.3–2.5% of the variance in BrainAGE, with a MAE of about 3.93 years in all approaches.Combined lifestyle risk significantly predicted BrainAGE in all three age approaches using sex as covariate: Higher combined lifestyle risk was predictive of higher BrainAGE, with a regression coefficient of F = 5.14–5.17, all p =  < 0.001, R2 = 0.040–0.048. Neither alcohol consumption , nor social integration  significantly predicted BrainAGE , and cognitive status  as covariates. In this fully adjusted model, pack-years of smoking predicted 2.9–3.2% of the variance in BrainAGE with a MAE of 3.88–3.90 years . Higher physical activity remained significantly associated with lower BrainAGE after correction for all three additional variables, i.e., BMI, education and cognitive status in all three approaches  above padjusted ≈ 0.003. It was predictive of 2.0–2.1% of variance in BrainAGE with a MAE of 3.90–3.93.In the next step, we examined which individual lifestyle variables were predictive of BrainAGE by including all four individual lifestyle variables into a multiple linear regression as explanatory variables, while correcting for sex. This was done for all three approaches. Overall, the model significantly predicted BrainAGE, F,615 = 5.F = 0.18, p = 0.672], even after outlier correction . Nonetheless, we were interested in investigating sex differences further in exploratory analyses. After stratifying the group by the two sexes to account for overall sex differences in BrainAGE, higher combined lifestyle risk was still associated with higher BrainAGE in all age approaches in both males  and females . Neither introducing education, nor BMI and cognitive status as additional covariates changed these associations (Table padjusted ≈ 0.003), though.The between-subjects ANCOVA did not show a significant interaction between sex and the combined lifestyle risk score on BrainAGE , as well as for female participants . After outlier correction , and for female participants , which was also true after outlier correction. All regression statistics can be found in Suppl. Table 6.Adding a quadratic term to the linear regression of BrainAGE on combined lifestyle risk did not result in a significantly better fit than the linear regressions, with both functions explaining 2.4–3.0% of the variance and the quadratic term itself not being significant, which was also true after outlier correction and for each approach. After splitting the sample by sex, there was no difference between the linear and quadratic fit for male , while the quadratic term itself was not significant . This was also true in male participants only , which was no longer significant after outlier correction . In male participants, including the quadratic term did also result in a slightly better model fit , but this effect as well as the better model fit disappeared after outlier correction . In female participants, neither the quadratic nor the linear model showed a significant association between physical activity and BrainAGE with even lower estimates of explained variance (R2 = 0.004–0.005) than in the linear regressions only.Regarding the association between physical activity and BrainAGE, the quadratic term showed an additional effect on BrainAGE  × age + (− 0.015) × education + 0.019 × DemTect + 0.46 × pack-years; BrainAGEfemale = − 0.326 + (− 0.07) × age + (− 0.052) × education + 0.361 × DemTect + 0.47 × pack-years). For males this estimation was the same for all three approaches. For females the increase in BrainAGE was estimated higher in approach 2 with 0.63 month, but the same for approach 3. Comparing never, moderate and severe smokers revealed, that the brains of severe smokers (BrainAGE = 1.61) appeared significantly older than those of never (BrainAGE = − 0.05) and moderate (BrainAGE = − 0.03) smokers, but that there was no significant difference between never-smokers and moderate smokers in average BrainAGE.For one increase in the combined risk score (risk score = 1), BrainAGE was estimated at 4.92 months older in addition to the effect of sex, chronological age, education and cognitive status (approach 1), which the beta values had already been corrected for (BrainAGE = 1.747 + (− 0.90) × sex + (− 0.76) × age + (− 0.029) × education + 0.077 × BMI + 0.146 × DemTect + 0.412 × riskscore). Without chronological age (approach 2), BrainAGE was estimated at 5.16 months older and using the age-corrected residuals BrainAGE was estimated 5.04 months older. For each pack-year of cigarettes, BrainAGE was estimated at 0.6 months older in approach 1 (BrainAGE = 2.91 + (− 0.229) × sex + (− 0.07) × age + (− 0.032) × education + 0.164 × DemTect + 0.5 × pack-years), which was slightly higher with 0.62 months in approach 2 and and the same for approach 3. Finally, we calculated an increase in BrainAGE of 0.55 months per pack-year in males and 0.56 months per pack-year in females  × age + (− 0.058) + (− 0.039) × education + 0.042 × BMI + 0.012 × DemTect + (− 0.058) × physical activity; BrainAGEfemale = − 0.115 + (− 0.105) × age + (− 0.058) × education + 0.078 × BMI + 0.383 × DemTect + (− 0.029) × physical activity). This was the same, when regressing chronological age out of the BrainAGE score before estimation (approach 3) in both sexes. When not including chronological age (approach 2), the estimation of BrainAGE increase was almost identical with 0.72 months in males, and 0.34 months in females.For each metabolic equivalent that was expended per week, BrainAGE was estimated 0.55 month younger when including chronological age in approach 1 (BrainAGE = 3.445 + (− 0.492 × sex + (− 0.088) × age + (− 0.056) × education + 0.056 × BMI + 0.176 × DemTect + (− 0.046) × physical activity). When not including chronological age (approach 2), the estimation was slightly higher with 0.56 months of BrainAGE, but this vanished when chronological age was regressed out before estimation (approach 3). Finally, we calculated a decrease in BrainAGE of 0.70 months per metabolic equivalent in males and 0.35 months per metabolic equivalent in females , but in female  participants, which was expectable since a small correlation between chronological age and BrainAGE is visible in females , but the residual variance of chronological age, which is statistically there, is regressed out, nevertheless. However, there is no conclusive solution to this issue in the present literature , but not in female participants (p = 0.169). This, however, may be due to the smaller proportion of females in the whole sample. Hence, it is possible, that the effect is present in females, but even larger sample sizes are needed to detect it in females as well . The statistical significance is non-conclusive, hence. The picture gets more unambiguous, when inspecting the amount of predicted variance in BrainAGE (R2). Here, physical activity was predictive of 3.4% of variance in BrainAGE in males, but of a strikingly different 0.8% of variance in females. This hints at physical activity contributing more to BrainAGE prediction in males.Most previous studies on lifestyle did not examine interaction effects of sex, as done in classic psychological research or specifically conducted sex-stratified analyses, as done in epidemiological research  prediction accuracy measured in MAE for any of the models examining lifestyle was highest in females. In females, the lowest MAE was 3.70 years in the fully adjusted model examining physical activity. For comparison, the lowest MAE in males was MAE = 3.99 years (fully adjusted model for the combined lifestyle risk score) and in the whole subsample MAE = 3.88 years (fully adjusted model examining pack-years) showing that factors contributing to prediction capacity may differ between males and females . Note that all models including cognitive status (measured using the DemTect) predicted a relatively high proportion of variance in BrainAGE in females (up to 7.4% in approach (i) in combination with the combined lifestyle risk score). This proportion was considerably higher in females than in males (3.9% explained variance within the same model). Hence, cognitive status may in females be a more important mediator when it comes to the prediction of structural brain aging from lifestyle behavior, whereas lifestyle behavior may contribute more to prediction in males.Thus, the current study found hints for sex differences within this association, but larger samples are needed. Taken together, higher physical activity seems to be one lifestyle behavior that may be predictive of decelerated brain aging, in line with previous studies. As Kramer and Colcombe  state inWe did not find an association between BrainAGE and social integration or alcohol consumption. Regional differences in brain structure associated to social integration, as well as alcohol consumption could be present, but might not have been identifiable with the specific approach of the current study. Several explanations might hold for these observations. To date, the number of studies investigating social integration in relation to structural brain decline in older adults is relatively small  than the average age-matched data of the sample and captures deviation from expected, i.e., typical development Below is the link to the electronic supplementary material.