Calcineurin inhibitors (CNIs) remain the cornerstone of maintenance immunosuppression (IS) after lung transplantation (LTx), although CNI-related life-threatening toxic effects may occur. Belatacept, a novel immunosuppressant that blocks a T-cell co-stimulation pathway, is a non-nephrotoxic drug indicated as an alternative to CNIs in kidney Tx. In LTx, there are only a few reports of belatacept conversion as a CNI-free or CNI-sparing IS treatment.We reviewed a series of 10 LTx recipients with conversion to a CNI-free belatacept IS regimen within the first year post-LTx (n = 7) or a belatacept/low-dose CNI combination after the first year (n = 3).Use of belatacept was triggered by severe renal failure in 9 patients and under-IS with previous other IS-related toxicities in 1 patient. Mean estimated glomerular filtration rate after starting belatacept significantly improved at 6 months after initiation and at the last-follow-up . The incidence of recurrent and/or severe acute cellular rejection (ACR) episodes was high in patients with CNI-free belatacept-based IS (n = 4/7). Chronic graft allograft dysfunction developed in 2 of 9 recipients under belatacept IS. Belatacept was stopped in 6 patients because of recurrent/severe ACR (n = 3), recurrent opportunistic infections (n = 1), center modified policy (n = 1), or other cause (n = 1).Early conversion to CNI-free belatacept-based IS improved renal function in this series but was counterbalanced by a high incidence of recurrent ACR, including life-threatening episodes. Other studies are needed to better determine the indications for its use after LTx, possibly with lower immunological risk IS regimens, such as CNI-sparing belatacept. Currently, calcineurin-inhibitors (CNIs) remain the main immunosuppressive (IS) therapy for long-term maintenance IS after lung transplantation (LTx). Nevertheless, severe CNI-related toxic effects may occur after LTx, mainly manifested by renal insufficiency –3, or neBelatacept, a new non-nephrotoxic immunosuppressive agent, is a selective costimulation blocker that binds CD80 and CD86, thereby blocking CD28-mediated costimulation in the T-cell activation cascade . In kidnOnly a few experiences with belatacept have been reported in lung Tx –14, and Data for 10 LTx recipients from Bichat and Foch Tx centers (France) for whom belatacept was used were analyzed within the 2016–2021 period. Maintenance IS therapy was similar in both centers, including tacrolimus (C0 trough level = 8–12 ng/L), mycophenolate mofetil (2 g/day) and prednisone (5 mg/day). An induction therapy with thymoglobulins or basiliximab was used only at Foch. The protocols of IS regimen administered in the 2 centers have been previously reported , and areThe first 7 patients (Bichat center) were switched to a CNI-free belatacept-based IS regimen during the first year post-LTx, associated with mycofenolate mofetil [MMF] continuation. Within the first 3 months after belatacept initiation, systematic transbronchial biopsies (TBBx) were planned at 1, 2, and 3 months post-conversion at the Bichat center.Three other patients followed at the Foch center were switched to a CNI-sparing belatacept IS regimen . The surveillance protocol for AR episodes and CLAD diagnosis in both centers was previously reported –18 see .All LTx recipients were considered for a switch from CNI to belatacept in case of severe renal insufficiency, with a high probability of permanent dialysis in case of continuation of CNI treatment. Belatacept was initiated after verifying positive antibody serostatus for Epstein–Barr virus (EBV), with a “less-intensive” regimen reported in kidney-Tx , 19 see. CNI wasThis study was approved by an institution ethics committee (IRB00012437), and conducted in accordance with good clinical practices and recommendations concerning human research contained in the Declaration of Helsinki. All patients gave their informed consent to be included in the study.t test or Mann–Whitney U-test. Statistical significance was set at p<0.05.Continuous variables are described with mean (SD) or median (range) and were compared by Student Characteristics of patients are detailed in In the 3 other patients who received belatacept associated with CNI reduction doses , causes for starting belatacept were severe renal insufficiency due to TMA (n = 2) or kidney insufficiency associated with under-IS in a patient with CLAD  values at 1 month, 3 months, and 6 months. Mean eGFR under belatacept increased from 21 ±9 mL/min (n = 10) to 43 ± 24 mL/min (n = 10) at 1 month (p = 0.01), to 50 ± 25 mL/min (n = 9) at 3 months (p = 0.005), to 55 ± 30 mL/min (n = 8) at 6 months (p = 0.006), and to 54 ±28 mL/min (n = 9) at the last-follow-up under belatacept (p = 0.002). One patient on dialysis was successfully weaned off renal replacement therapy (patient 1). The 3 last patients with CNI-sparing belatacept IS with very-low dose CNI continuation also showed a significant increase in eGFR at the last follow-up under belatacept versus at belatacept initiation . Outcome of individual creatinine clearance values for all patients under belatacept is shown in During follow-up, belatacept was discontinuated in 6 patients who were re-switched to a standard CNI-based IS. Causes of discontinuation were as follows: recurrent/severe ACR [n = 3], viral infection [n = 1], change in center policy [n = 1], and other [n = 1]). In the 5 patients with available follow-up after belatacept discontinuation, creatinine value was 154 ± 69 μmol /L at the date of re-conversion, and increased to 226 ± 145 μmol /L at 12 months post-reconversion (p = 0.2). Two of them showed a significant increase in mean creatinine level at 12 months post-reconversion , and one of these 2 patients required hemodialysis at month 9 post-reconversion.ACR episodes occurred in 4/10 patients after belatacept initiation, exclusively in patients with CNI-free belatacept-based IS . The TBB2 at exertion already present at belatacept initiation (patient 1) or with a bronchiolitis obliterans syndrome (BOS)1 pattern (patient 4). Another patient with BOS1 at belatacept initiation remained with stable lung function (patient 9). The outcome of individual forced expiratory volume in 1 sec values for patients after starting belatacept is shown in CLAD onset during belatacept exposure  and at lViral infection occurred in 3/10 patients under belatacept treatment , includiIn this series of LTx recipients with CNI-related severe renal insufficiency, the use of belatacept was associated with significant improvement in kidney function but also with a high incidence of recurrent/severe ACR, only observed in patients with CNI-free belatacept regimen (4/7 patients). Two of 10 patients also experienced severe viral disease, as reported in other solid-organ Tx cases under belatacept , 19.The significant improvement in kidney function with belatacept is in accordance with the potential partial reversibility of severe renal insufficiency due to CNI toxic effects observed in kidney Tx , 23, in The 2 cases of severe viral infections with disseminated VZV disease each occurred in one of the 2 centers, with or without previous induction IS therapy administered at the date of LTx. The severity of VZV infection led to discontinuing belatacept in 1 patient. Because VZV infection may present in a threatening form after solid-organ transplantation , this suThe occurrence of ACR episodes and/or CLAD development remain the main potential risk after belatacept conversion. The frequency of high grade ACR we observed in this series seems of particular concern. Of note, these ACR episodes occurred exclusively among the 7 patients with CNI-free belatacept IS (n = 4/7), with an unusual clinical presentation: indolent progressive ACR episodes or, in contrast, a fulminant ARDS-associated ACR episode . The fewCLAD occurred in 2/9 patients under belatacept with available follow-up after starting the conversion from CNI to belatecept, only in those under CNI-free belatacept IS . Hence, Belatacept was discontinued in 6 patients who were re-switched to a standard CNI-based IS. The causes of discontinuation were mainly recurrent/severe ACR in 3 patients with CNI-free IS but also severe viral infection in 1 patient, an indication for CNI treatment in 1 patient ,or change in center policy in 1 patient. Among 5 patients reconverted to CNI and with available follow-up, two experienced again a significant decline in renal function, which suggests also the potential benefit of belatacept for kidney function in case of CNI toxic effects after LTx.de novo belatacept-based IS versus tacrolimus-based IS. The belatacept group exhibited a significant increase in early deaths, with premature termination of the trial; however, both arms exhibited the same incidence of ACR, de novo DSAs, or CLAD occurrence Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: N/A**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The present study is a retrospective case series of 10 lung transplant recipients treated with belatacept almost exclusively for renal failure related to calcineurin inhibitor toxicity. This report is important to refine the possibility of using belatacept in these patients, perhaps with a CNI-sparing rather than a CNI free strategy, and after the first post-transplantation year. Indeed, it clearly confirms a sharp improvement in renal function, but also suggests a high risk of opportunistic infections and probable rejections.Major commentsThe improvement in renal function is impressive and well described. However, we lack information following conversion from belatacept to CNI (n=6). Did renal function remain stable or decline again?The proportion of rejections is high following first-year conversion from CNI to belatacept. We need to know:- Dose of MMF and AUC if available after conversion- Is tacrolimus C0 target 2-3 ng/ml or 1-3 ng/ml? (two different information in the text)- Induction  should be added in Table 1- History sensitization (DSA?) before conversion could be inserted in Table 2Several opportunistic infection are reported. This is a crucial and worrying point already described in other types of solid organ transplantations. Did they use post-conversion prophylactic treatment (for CMV and pneumocystosis). In addition, can authors provide information on D/R CMV serology as well as VZV serology? I find that discussion is essentially centered on rejection while infectious risk is just as important.Minor comments- I alert authors that some surnames and family names are inverted- The meaning of abbreviation ACR should be provided at the first appearance in the text (present only in the abstract)Reviewer #2: The authors present a unique case series in lung transplant recipients with CNI associated renal toxicity converted to Belatacept immunosuppression. Conversion had the intended effect on renal function but also introduced higher than desired rates of acute rejection. Overall, the manuscript mostly meets PLOS ONE criteria and the following should enhance the manuscript further.1. The diction and grammar, use of language is not as clear as it should be for final publication. understandably English is presumably not the first language of the French authors, but improvements in this will enhance the readability and communication of data.2. Better disclosure of baseline renal function of subjects reported on would be helpful. It was stated they had normal eGFR (except 1) pre-transplant, but outlining this at least in the table and maybe even in graphical form would be very helpful. Not only pre-tx but also over the course post-transplant before conversion. I would also suggest the eGFR graph be primary figure and not supplementary, as it is most of the story here.3. Improvements in eGFR were quite dramatic. Given that they were all relatively early post-tx conversions, it is possible other transplant or or early post-tx factors were contributing to the low eGFRs in addition to CNI. this is why demonstrating the eGFR course during the post-tx, pre-conversion period might be informative. As well as addressing this possibility in the data presentation or discussion since controls are not part of the nature of case series such as this one.4. What happened to the eGFRs once patients were put back on CNI due to ACR?5. Disclosing the induction IS may also be informative re: rejection risk for each patient. Were the non-rejectors thymoglobulin recipients and rejectors Belatacept? were all recipients on triple IS (? prednisone)6. Emphasis on the low dose CNI approach with Belatacept as possible future approach should be stronger. Early post-tx risk of rejection with Belatacept generally understood across organs, and best highlighted by the Emory experience and BEST study in post trial experiences. mitigating with low dose cni/bela approach very rationale and slowly achieves goal of longer term preservation of renal function.7. Reference and discussion of experience with Bela in the other cardiothoracic organ (heart) may also complete discussion on the subject for this case series. several publications exist on conversion in this setting as well, but with less rejection as I recall, but this should be verified.8. the authors should very clearly state that this is a small case series composed of heterogeneous patient population that suggests certain points, but conclusions cannot be made.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  21 Dec 2022RESPONSES REVIEWERS COMMENTS PONE-D-22-26417Conversion to belatacept after lung transplantation: report of 10 casesPLOS ONEReviewer #1: The present study is a retrospective case series of 10 lung transplant recipients treated with belatacept almost exclusively for renal failure related to calcineurin inhibitor toxicity. This report is important to refine the possibility of using belatacept in these patients, perhaps with a CNI-sparing rather than a CNI free strategy, and after the first post-transplantation year. Indeed, it clearly confirms a sharp improvement in renal function, but also suggests a high risk of opportunistic infections and probable rejections.Major commentsThe improvement in renal function is impressive and well described. However, we lack information following conversion from belatacept to CNI (n=6). Did renal function remain stable or decline again?We agree this is interesting to know if what was the renal function outcome after re-conversion from belatacept to CNI. Among these 6 patients reconverted from belalacept to CNI, outcome of renal function was available at 12 months post-re-conversion in 5 of them, because one of them died rapidly thereafter . In the 5 patients with available follow-up, creatinine value was 154 (SD 69) micromole/L at the date of re-conversion, and increased to 226 (145) micromole/L at 12 months post-reconversion (p=0.2). Two of these 5 patients had significant increase of mean creatinine value from the date of re-conversion belatacept-tacrolimus to 12 months post-reconversion (from 179 (61) micromole/L to 377 micromol/L, p=0.04), and one of these 2 patients required hemodialysis at month 9 post-reconversion. Blood C0 target of tacrolimus after reconversion was 4-7 ng/ml (patient 4 and 6), 6-9 ng/ml (patient 5 and 10), and blood C0 target of ciclosporine was 80-150 ng/ml (patient 3). Hence, although the very few number does not allow for firm conclusion, the renal outcome the first year after re-conversion to CNI showed a severe decline of kidney function in 2 patients, and hemodialysis was required in 1 of them, suggesting again the transient benefit of belatacept exposure on kidney function in case of CNI toxicity after LTx.As suggested, we added these data in the manuscript, section renal outcome after belatacept page 7, line 12: “Belatacept was discontinuated in 6 patients who were re-switched to a standard CNI-based IS. Causes of discontinuation were: recurrent/severe ACR [n=3], viral infection [n=1], change in center policy [n=1], other [n=1]). In the 5 patients with available follow-up after belatacept discontinuation, creatinine value was 154 (SD 69) micromole/L at the date of re-conversion, and increased to 226 (145) micromole/L at 12 months post-reconversion (p=0.2). Two of them had significant increase of mean creatinine value at 12 months post-reconversion , and one of these 2 patients required hemodialysis at month 9 post-reconversion. “In Table 3, IS regimen of patients who were re-converted from belatacept to CNI are now included, to detailed the dosage of CNI during this period (See Table 3).In discussion page 12 line 16:“A significant decline of renal function was observed in 2 among 6 patients who were reconverted from belatacept to CNI, which again suggests the potential benefit of belatacept on kidney function in case of CNI toxicity following LTx.”The proportion of rejections is high following first-year conversion from CNI to belatacept. We need to know:- Dose of MMF and AUC if available after conversionDosage of MMF after conversion from CNI to belatacept was as follows: 1000 mgx2/day  or 500mgx2/day . No AUC was performed for these patients./ As suggested, these data have been inserted in the last column of Table 1 (see Table 1).- Is tacrolimus C0 target 2-3 ng/ml or 1-3 ng/ml? (two different information in the text)Tacrolimus C0 target was 2-3 ng/ml. This has been corrected.- Induction  should be added in Table 1.Induction therapy was not used for patients from Bichat (n=7). Nevertheless, an early use of basiliximab was used in the patients 1, 3, and 5 in this center after the immediate postoperative period, with an indication “CNI holiday”. This CNI holiday consisted in transient CNI reduction dosage associated with the use of basilixilab, at M3 for patient 1, M1 for patient 3, and M3 for patient 5. In case of CNI holiday, basiliximab 20 mg/d at day 0 and day 4 was administered, associated with reduction of CNI dosage (Tacrolimus C0: 2-3 ng/ml during 10 days). Because basiliximab was used in these patients, not as induction but as CNI holiday, it has been mentioned and inserted in Table 1.An induction therapy was used for patients from Foch hospital in 2 patients (Thymoglobulins), and these data have been inserted in Table 1. - History sensitization (DSA?) before conversion could be inserted in Table 2As suggested, history of sensitization before the date of starting belatacept in now shown in Table 2, associated to desensitization treatment administered to these sensitized patients. For example, Patient 1 had progressive increase of de novo DSA (anti-DQ6 with MFI of 4501 at M3), treated with PLEX and monthly IVIg (x3) before the date belatacept was started (See Table 2). This has been added in Table 2. Several opportunistic infection are reported. This is a crucial and worrying point already described in other types of solid organ transplantations. Did they use post-conversion prophylactic treatment (for CMV and pneumocystosis). In addition, can authors provide information on D/R CMV serology as well as VZV serology? I find that discussion is essentially centered on rejection while infectious risk is just as important.-Prophylactic treatment against pneumocystosis was systematically used in all patients (in both centers) from day 0, with Trimetoprim/Sulfamethoxasone (800mg): 800 mg 3 times a week, or atovaquone 750 mg x 2/day in case of side effects of Trimetoprim/Sulfamethoxasone, lifelong from day 0 of LTx. One patient had pneumocystosis infection at M4 after starting belatacept treatment. The patient was under atovaquone at this date, and it was suspected that the adherence to atovaquone was not optimal at this post-LTx date in this patient. This has been specified in the manuscript.Manuscript, page 11 line 5: “Pneumocystosis occurred in one patient (patient 10), who was suspected of non-adherence to atovaquone treatment.”.And in Supplemental data, page 2 line 5: “Prophylactic treatment against pneumocystosis was systematically used in all patients (in both centers) from day 0, with Trimetoprim/Sulfamethoxasone (800mg): 800 mg 3 times a week, or atovaquone 750 mg x 2/day in case of side effects of Trimetoprim, lifelong from day 0 of LTx”.-Prophylactic treatment of CMV infection was not altered by the conversion CNI-Belatacept, whatever the date of starting belatacept. At Bichat hospital, it included Rovalcyte: 900 mg/day from day 0 post LTx to 6 months in absence of CMV mismatch or to 12 months post-LTx in case of CMV mismatch. At Foch hospital, same prophylactic treatment was used, except in patients with CMV D+/R+ or D-/R+, for who Zelitrex 1000 mg x 4/day was used during the first 6 months. In both centers, in case of negative CMV status (D-/R-), a prophylactic treatment with Zelitrex 1000 mg x3/day was given within the first 3 months. As suggested, D/R CMV serology and VZV serology are now detailed in the manuscript (Table 1). In supplemental data, we have now specified prophylaxis for CMV and VZV infections, page 2 line 14: “Prophylactic treatment of CMV infection at Bichat hospital included Rovalcyte: 900 mg/day from day 0 post LTx to 6 months in absence of CMV mismatch, or to 12 months post-LTx in case of CMV mismatch. At Foch hospital, same prophylactic treatment was used, except in patients with CMV D+/R+ or D-/R+ status, for who Zelitrex 2000 mg x 4/day was used during the first 6 months. In both centers, in case of negative CMV status (D-/R-), a prophylactic treatment with Zelitrex 1000 mg x3/day was given within the first 3 months.”We agree with reviewer that occurrence of severe opportunistic infections under belatacept is a crucial point also, as showed in 2 patients with disseminated VZV infection . In these 2 patients, VZV serology of recipient was positive before Tx (inserted in Table 1). These 2 patients were without viral prolonged specific prophylactic treatment of VZV infection at the date of starting belatacept , but patient 1 had only the end of Rovalcyte prophylaxis during the first 20 days under belatacept. Hence, the severity of these disseminated VZV disease under belatacept, as previously described in SOT recipients, raises this issue of systematic prophylaxis against VZV infection in case of a positive serologic status in post-LTx. For CMV disease, systematic surveillance of CMV PCR may allow to early detect CMV replication without systematic prophylaxis and to avoid severe form of infection with adapted rovalcyte treatment.As suggested, we have now highlighted this issue in the manuscript:-Table 3: D/R CMV serology and VZV serology are now detailed in the manuscript (Table 1).-In the discussion, page 11 line 22: “Hence, because VZV infection may present as a life threatening disseminated infection after solid-organ transplantation (1), this suggests to use a systematic prophylaxis against VZV infection in case of a positive serologic status in post-LTx.”Minor comments- I alert authors that some surnames and family names are invertedWe thank reviewer 1 and this has been corrected.- The meaning of abbreviation ACR should be provided at the first appearance in the text (present only in the abstract).This has been corrected.Reviewer #2: The authors present a unique case series in lung transplant recipients with CNI associated renal toxicity converted to Belatacept immunosuppression. Conversion had the intended effect on renal function but also introduced higher than desired rates of acute rejection. Overall, the manuscript mostly meets PLOS ONE criteria and the following should enhance the manuscript further.1. The diction and grammar, use of language is not as clear as it should be for final publication. understandably English is presumably not the first language of the French authors, but improvements in this will enhance the readability and communication of data.As suggested, the whole manuscript has been reviewed by an English native.2. Better disclosure of baseline renal function of subjects reported on would be helpful. It was stated they had normal eGFR (except 1) pre-transplant, but outlining this at least in the table and maybe even in graphical form would be very helpful. Not only pre-tx but also over the course post-transplant before conversion. I would also suggest the eGFR graph be primary figure and not supplementary, as it is most of the story here.As suggested, outcome of renal function starting from day 0 of LTx to the day starting belatacept for all patients has now been added with a new Figure 1, as a primary figure .In the manuscript, page 7 line 2: “Post-LTx outcome of renal function before conversion to belatacept for all patients is shown in Figure 1, as individual creatinine values of the 10 patients from day 0 of LTx to the day of conversion to belatacept. Mean creatinine value increased from 66 (27) micromole/L at day 0 of LTx to 275 (116) micromole/L at the date of conversion to belatcept (p=0.0002).”  mol at the date of conversion to belatcept to 126 (65) �  mol at the last day of follow-up under belatacept (p=0.0009).”In the same way, and as suggested, the previous supplemental figure S1, showing the outcome of individual creatinine clearance values for patients after starting belatacept from day 0 of belatacept to last follow-up, is now shown as a primary figure , with an addition of the following sentence page 7, line 17 in the manuscript (section results): “Outcome of individual creatinine clearance values for all patients under belatacept is shown in Figure 2A. Among all patients, mean creatinine value decreased from 274 (116) �3. Improvements in eGFR were quite dramatic. Given that they were all relatively early post-tx conversions, it is possible other transplant or or early post-tx factors were contributing to the low eGFRs in addition to CNI. this is why demonstrating the eGFR course during the post-tx, pre-conversion period might be informative. As well as addressing this possibility in the data presentation or discussion since controls are not part of the nature of case series such as this one.As suggested in response to point n°2, the pre-conversion period of renal function outcome is now shown in data, and included in a new Figure 1 as a primary Figure (see response to point 2).4. What happened to the eGFRs once patients were put back on CNI due to ACR?We agree with reviewer that this is an important point. Among these 6 patients reconverted from belalacept to CNI, outcome of renal function was available at 12 months post-re-conversion in 5 of them, because one of them died rapidly thereafter . In the 5 patients with available follow-up, creatinine value was 154 (SD 69) micromole/L at the date of re-conversion, and increased to 226 (145) micromole/L at 12 months post-reconversion (p=0.2). Two of these 5 patients had significant increase of mean creatinine value from the date of re-conversion belatacept-tacrolimus to 12 months post-reconversion (from 179 (61) micromole/L to 377 micromol/L, p=0.04), and one of these 2 patients required hemodialysis at month 9 post-reconversion. Blood C0 target of tacrolimus after reconversion was 4-7 ng/ml (patient 4 and 6), 6-9 ng/ml (patient 5 and 10), and blood C0 target of ciclosporine was 80-150 ng/ml (patient 3). Hence, although the very few number does not allow for firm conclusion, the renal outcome the first year after re-conversion to CNI showed a severe decline of kidney function in 2 patients, and hemodialysis was required in 1 of them, suggesting again the transient benefit of belatacept exposure on kidney function in case of CNI toxicity after LTx.As suggested, we added these data in the manuscript, section “renal outcome” page 7, line 12: “Belatacept was discontinuated in 6 patients who were re-switched to a standard CNI-based IS . In the 5 patients with available follow-up after belatacept discontinuation, creatinine value was 154 (69) micromole/L at the date of re-conversion, and increased to 226 (145) micromole/L at 12 months post-reconversion (p=0.2). Two of them had significant increase of mean creatinine value at 12 months post-reconversion , and one of these 2 patients required hemodialysis at month 9 post-reconversion. In Table 3, IS regimen of patients who were re-converted from belatacept to CNI are now included, to detail the dosage of CNI during this period (See Table 3).In discussion page 12 line 16. :“A significant decline of renal function was observed in 2 among 5 patients who were reconverted from belatacept to CNI, which again suggests the potential benefit of belatacept on kidney function in case of CNI toxicity following LTx.”5. Disclosing the induction IS may also be informative re: rejection risk for each patient. Were the non-rejectors thymoglobulin recipients and rejectors Belatacept? were all recipients on triple IS (? prednisone).ACR episodes occurred in 4/10 patients after belatacept initiation, exclusively in patients with CNI-free belatacept-based IS (Table 2). These 4 patients did not receive induction therapy by thymoglobulin, and this has been added, in Table 1 and in the manuscript. Nevertheless, although induction therapy was not used for patients from Bichat (n=7), an early use of basiliximab was used during the first year in patients 1, 3, and 5 of this center. This indication of “CNI holiday” consisted in transient CNI reduction dosage associated with the use of basilixilab, at M3 for patient 1, M1 for patient 3, and M3 for patient 5. In case of CNI calcineurin holiday, basiliximab 20 mg/d at day 0 and day 4 was administered, associated with reduction of CNI dosage (Tacrolimus C0: 2-3 ng/ml during 10 days). As suggested, all these data have been inserted in Table 1.An induction therapy was used for patients from Foch hospital . All patients were under triple IS, including belatacept and prednisone, and at least one other IS therapy (Table 1). To be clearer, prednisone and other associated IS therapy have been specified in Table 1.6. Emphasis on the low dose CNI approach with Belatacept as possible future approach should be stronger. Early post-tx risk of rejection with Belatacept generally understood across organs, and best highlighted by the Emory experience and BEST study in post trial experiences. mitigating with low dose cni/bela approach very rationale and slowly achieves goal of longer term preservation of renal function.We agree that it is a crucial point. Interestingly, an experience in kidney Tx was reported with the adoption of belatacept-based IS in association of same low dose Tacrolimus (2-3 ng/mL) that we used, showing a satisfactory outcome under this IS regimen in a specific population of KTs with a high immunological risk . This reference has been added and discussed, page 15, line 4: “Interestingly, a small series in kidney Tx with a high immunological risk showed satisfactory outcome with a very similar protocol that we used (CNI-sparing belatacept IS in association of same low dose Tacrolimus [2-3 ng/mL])(2).”Hence, we believe from our observations that there is a rational for a future approach with very low CNI/belatacept association, and this has been further discussed in the manuscript, , keeping in mind the point 8 (see below) that this is a small series with heterogeneous patient population which included only 3 patients with CNI-reduction associated with belatacept.In the discussion, page 14 line 21: “In the absence of clinical validation of such predictors, our results suggest to further explore CNI-sparing belatacept IS rather than CNI-free IS, after a case-by-case careful evaluation of the risk/benefit ratio.”In the discussion, page 15 line 5, the following sentence: “studies are needed to better determine the potential indications of its use following LTx, possibly with lower immunological risk IS regimens, such as CNI-sparing belatacept IS after the first year post-LTx.”7. Reference and discussion of experience with Bela in the other cardiothoracic organ (heart) may also complete discussion on the subject for this case series. several publications exist on conversion in this setting as well, but with less rejection as I recall, but this should be verified.We have now added experience of belatacept IS regiment reported in heart Tx in a series of 40 patients . This study showed rather similar findings as compared to our series in LTx: 1/ The main reason for switching to belatacept was to preserve renal function, resulting in discontinuation of CNI and changes in IS therapy in 76% of cases. Following administration of belatacept, a very significant improvement of renal function was observed  in a similar range with our study. 2/More frequent and more severe rejection episodes were observed after conversion to belatacept. One patient died of severe organ rejection while on IS regimen free of CNI, and including belatacept, everolimus, MMF, and steroids. 3/ A rather high number of discontinuation of belatacept was observed, including treatment failure (16/40 during the period of the study).Hence, we have added these points and this reference in the manuscript:Page 12 line 1: “The significant improvement in kidney function with belatacept is in accordance with the potential partial reversibility of severe renal insufficiency due to CNI toxic effects observed …, in heart Tx (3) and in some LTx reports .Page 14, line 11: “Experience with CNI-free belatacept IS regimen has also been reported after heart Tx, showing, as in our series, more frequent and more severe rejection episodes after conversion to belatacept (3).”8. the authors should very clearly state that this is a small case series composed of heterogeneous patient population that suggests certain points, but conclusions cannot be made.This has been stated in the manuscript page 15, line 10: “It should be emphasized that our small series of heterogeneous LTx recipients does not allow firm conclusion, and only suggests a possible benefit of CNI-sparing belatacept in low-immunological risk patients after 1 year post-Tx.”1. Helou E, Grant M, Landry M, Wu X, Morrow JS, Malinis MF. Fatal case of cutaneous-sparing orolaryngeal zoster in a renal transplant recipient. Transpl Infect Dis 2017; 19.2. Gallo E, Abbasciano I, Mingozzi S, Lavacca A, Presta R, Bruno S, Deambrosis I, Barreca A, Romagnoli R, Mella A, Fop F, Biancone L. Prevention of acute rejection after rescue with Belatacept by association of low-dose Tacrolimus maintenance in medically complex kidney transplant recipients with early or late graft dysfunction. PLoS One 2020; 15: e0240335.3. Launay M, Guitard J, Dorent R, Prevot Y, Prion F, Beaumont L, Kably B, Lecuyer L, Billaud EM, Guillemain R. Belatacept-based immunosuppression: A calcineurin inhibitor-sparing regimen in heart transplant recipients. Am J Transplant 2020; 20: 553-563.4. Timofte I, Terrin M, Barr E, Sanchez P, Kim J, Reed R, Britt E, Ravichandran B, Rajagopal K, Griffith B, Pham S, Pierson RN, 3rd, Iacono A. Belatacept for renal rescue in lung transplant patients. Transpl Int 2016; 29: 453-463.5. Iasella CJ, Winstead RJ, Moore CA, Johnson BA, Feinberg AT, Morrell MR, Hayanga JWA, Lendermon EA, Zeevi A, McDyer JF, Ensor CR. Maintenance Belatacept-Based Immunosuppression in Lung Transplantation Recipients Who Failed Calcineurin Inhibitors. Transplantation 2018; 102: 171-177.AttachmentResponses to reviewers comments .docxSubmitted filename: Click here for additional data file. 24 Jan 2023Conversion to belatacept after lung transplantation: report of 10 casesPONE-D-22-26417R1Dear Dr. Brugière,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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I think your manuscript will be helpful for transplant community.Reviewer #2: authors have responded to critiques appropriately. the manuscript is improved and clearer. the data more interpretable and the conclusions supported by the data.**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No********** 6 Mar 2023PONE-D-22-26417R1 report of 10 cases.Conversion to belatacept after lung transplantation: Dear Dr. Brugière:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! 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More research is needed to elucidate the neurobiological mechanisms underlying ADHD. We leveraged functional annotation information into an ADHD PGS to (1) improve the prediction performance over a non-annotated ADHD PGS and (2) test whether volumetric variation in brain regions putatively associated with ADHD mediate the association between PGSs and ADHD outcomes.ADHD polygenic scores (PGSs) have been previously shown to predict ADHD outcomes in several studies. However, ADHD PGSs are typically N = 555). Multiple mediation models were tested to examine the indirect effects of two ADHD PGSs—one using a traditional computation involving clumping and thresholding and another using a functionally annotated approach —on ADHD inattention (IA) and hyperactivity-impulsivity (HI) symptoms, via gray matter volumes in the cingulate gyrus, angular gyrus, caudate, dorsolateral prefrontal cortex (DLPFC), and inferior temporal lobe.Data were from the Philadelphia Neurodevelopmental Cohort (AnnoPred ADHD PGS and IA symptoms in adolescents. No indirect effects via brain volumes were detected for either IA or HI symptoms. However, both ADHD PGSs were negatively associated with the DLPFC.A direct effect was detected between the AnnoPred ADHD PGS was a more developmentally specific predictor of adolescent IA symptoms compared to the traditional ADHD PGS. However, brain volumes did not mediate the effects of either a traditional or AnnoPred ADHD PGS on ADHD symptoms, suggesting that we may still be underpowered in clarifying brain-based biomarkers for ADHD using genetic measures.The  Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects 3–6% of youths and adults worldwide , 2. GeneSORCS3, DUSP6, SEMA6D) are involved in neurotransmission, neuronal development, and neuronal plasticity. Despite the wealth of neurobiological information gained from powerful ADHD GWA studies, these data have not been incorporated in ADHD PGS applications.ADHD PGSs correlate with ADHD outcomes across many studies because the genetic effect sizes that are used in their computations are based entirely on the statistical associations  between individual level genotypes and a trait of interest conducted in a large GWA study. However, PGS associations with trait outcomes may not necessarily reflect casual  signals . This knAnnoPred, a statistical method that upweights genetic variants located in annotation categories that are overrepresented in a GWA study of interest [AnnoPred PGS method demonstrated superior accuracy in complex trait predictions compared to other PGS methods [AnnoPred should not only improve ADHD PGS prediction performance over a non-annotated ADHD PGS, but it should also provide more signal for elucidating potential neurobiological pathways of risk for ADHD given that AnnoPred PGS specifically upweights functionally relevant signals.One powerful approach to do so is called interest . Annotat methods . Thus, mIn addition to gene enrichment findings from ADHD GWA studies , other pmediate genetic associations (via PGSs) of several psychiatric traits and disorders, including schizophrenia, depression, bipolar disorder, ADHD, and autism. This study examined the association between PGSs for five psychiatric disorders and volume of several brain regions of interest  in a large, population-based sample of 9–11-year-old children (n = 1139). ADHD PGSs were associated with smaller caudate volume but not the other regions, and the association between the ADHD PGS and male-specific attention problems was mediated by caudate volume, in line with the well-replicated association between caudate and ADHD. Their findings provide compelling  evidence that ADHD PGSs may more proximally tap into biological signals in the development of ADHD.Whereas prior studies investigated genetic and brain volume associations with ADHD as separate mechanisms, Alemany and colleagues  examinedn = 3200), researchers found that volumetric differences between groups were larger among children (aged between 4 and 14) than among adolescents (aged between 15 and 21) or among adults (aged 22–63) [n = 672). They reported a reversal in the direction of difference between cases and controls among young adolescents (aged 8–15), older adolescents (aged 16–22), and young adults (aged 22–30), such that caudate and putamen volume were smaller in adolescents with ADHD than controls, but larger in young adults with ADHD than controls [However, one critical limitation of the prior study  is that d 22–63) . Specificontrols . The morcontrols  likely c aged 22–, such thAnnoPred [AnnoPred PGS will associate with ADHD outcomes  across age groups. Second, and based on previously reported age-related brain volume differences between ADHD cases and controls across age groups, we hypothesized that the volumes of all five brain regions will mediate the associations between the AnnoPred ADHD PGS  and ADHD outcomes in children and adolescents, but not in adults. We made no hypothesis regarding potential genetic differences for ADHD IA and HI symptoms given that there have been no strong lines of evidence suggesting differential ADHD PGS associations by ADHD presentation type.This current study focused on children aged 8–11), adolescents (aged 12–17), and young adults (aged 18–21) from the Philadelphia Neurodevelopmental Cohort (PNC). Although ADHD PGSs are frequently used in clinical samples, they are also predictive of psychiatric traits in population samples as well, as demonstrated in the meta-analysis of ADHD PGSs across several studies . Examini, adolescn = 137), to participants and collaterals for participants aged 11 to 17 , and to participants themselves if they were between the ages of 18 and 21 . To ensure that there was some degree of consistency with respect to raters, we used data from collaterals for participants between 8 and 17 years of age and from self-report for those older than 18. For additional details on phenotyping and coding, please refer to He and Li [N of 555.The Philadelphia Neurodevelopmental Cohort (PNC) is a population-based cohort of children, adolescents, and young adults with data collected on psychiatric disorders, medical history, neuroimaging, genetics, and neurocognition. Participants were recruited from the greater Philadelphia, Pennsylvania, area between November 2009 and December 2011 . Psychiae and Li . Due to e and Li , 35, we Neuroimaging details for the PNC sample are provided in Sattherwaite et al.  and are recon_all., with all default parameters taken, was used with processing steps briefly summarized here; the processing stream performs the following sequential steps: skull stripping, volumetric labeling, intensity normalization, white matter segmentation, surface atlas registration, surface extraction, and gyral labeling. The Destrieux atlas [Freesurfer (v. 7.1.1)  was usedux atlas  was usedux atlas . To accoux atlas .IA and HI ADHD symptoms were assessed from six and three dichotomous items (yes/no responses) administered on the adapted K-SADS, respectively. Example items included: “Did you often have trouble paying attention or keeping your mind on your school, work, chores, or other activities that you were doing?”, “Did you often have trouble making plans, doing things that had to be done in a certain kind of order, or that had a lot of different steps?”, and “Did you often blurt out answers to other people’s questions before they finished speaking or interrupt people abruptly?”. Symptom counts of the two ADHD dimensions were computed by summing the number of endorsed items for each dimension.p values < 1.0e − 4, and call rates < 95%. We phased and imputed the genotype data using the Haplotype Reference Consortium reference panel version r1.1 2016 available on the Michigan Imputation server [All participants were genotyped upon consent using common single nucleotide polymorphism (SNP) arrays including Affymetrix Affy60 and Axiom, and Illumina HumanHap550 , Human610-Quad (v1), and HumanOmniExpress. We applied pre-imputation quality control measures to our genotype data, including removing SNPs with minor allele frequencies (MAF) < 5%, Hardy–Weinberg equilibrium n server . After iThe ADHD GWA study was a case–control meta-analysis that consisted of 55,374 children and adults  from 12 studies of mixed (but predominantly European) ancestries . The larr2 threshold of 0.1 for clumping. Finally, we generated an ADHD PGS in the PNC target sample using PRSice-2 software [p value threshold of 1.0, which allowed us to include all available SNP information while also facilitating the generalizability of our findings to other samples.To construct the traditional PGS, we used PLINK  to clumpsoftware  and specAnnoPred [AnnoPred score. We used 1000 Genomes Project European samples [AnnoPred to produce SNP posterior mean effects.We also constructed a PGS for ADHD using AnnoPred , a BayesAnnoPred  and ADHDAnnoPred . We incoAnnoPred , GenoCanAnnoPred , and 66 AnnoPred  to impro samples  as the lAnnoPred) as predictors, and ADHD IA and HI symptoms as outcomes. In statistical mediation, the bivariate association between the predictor and the outcome is termed a total effect. The association between the predictor and the outcome via mediators is termed the indirect effect. Finally, the association between the predictor and the outcome after accounting for the mediators is termed the direct effect [p values were false discover rate (FDR) adjusted using the Benjamin-Hochberg method [4.1.2 using the following packages: semTools, lavaan, psych, and stats. Age, biological sex, standardized total intracranial volume, Euler Numbers, and the first 10 genetic principal components to account for population stratification were included as covariates in all models. It should be noted that covarying out genetic principal components does not correct for all population stratification effects, however [We fit parallel multiple mediation models that included brain volumes of five ROIs  as mediators, ADHD PGSs , with 272 (49.10%) female participants. When stratified by age groups , the average age was 9.672 (sd = 1.058), 14.790 (sd = 1.710), and 18.64 (sd = 0.837), respectively. Furthermore, 46.715% (n = 64), 46.464% (n = 138), and 57.851% (n = 70) were females across these age groups, respectively. Participants had an average of 2.489 (sd = 3.080), 2.037 (sd = 2.883), and 2.380 (sd = 2.675) ADHD symptoms, respectively. More details of the sample are shown in Table The average age of the PNC sample was 14.36 (FDR-corrected p > .05). Furthermore, no indirect effects emerged with the traditional ADHD PGS on HI or IA symptoms via the volumes of angular gyrus, caudate, cingulate gyrus, DLPFC, and the inferior temporal lobe. However, the traditional ADHD PGS was negatively associated with DLPFC volume . In addition, cingulate gyrus volume was positively associated with HI symptoms .Multiple mediation models for IA and HI symptoms fit the data well . We found no significant total and direct effects of the traditional ADHD PGS on either HI or IA symptoms , the models fit well for IA and HI symptoms . No total, direct, or indirect effects via the volumes of the five brain regions were detected .The same models were tested separately in each of the three age groups to investigate the possibility of developmental differences of the traditional ADHD PGS on IA and HI symptoms, and the possible mediational effects via the volumes of angular gyrus, caudate, cingulate gyrus, DLPFC, and the inferior temporal. For children ((n = 297) . Additionally, cingulate gyrus volume was positively associated with HI symptoms , and caudate volume was negatively associated with IA symptoms .Similarly, no total or direct effects of the traditional ADHD PGS nor indirect effects via the volumes of the five brain regions were detected for adolescents n = 97 . We observed no total or direct effects of the traditional PGS nor indirect effects of the traditional PGS via the volumes of the five brain regions in young adults . Additionally, cingulate gyrus volume was positively associated with HI symptoms .The models for IA and HI symptoms fit well . There was neither a total nor direct effect of the oms Fig. . There wAnnoPred ADHD PGS on HI and IA symptoms were not significant, nor were the indirect effects via the volumes of five brain regions .For children, the models fit well for IA and HI symptoms . Total and direct effects of the ons Fig. . The AnnAnnoPred ADHD PGS on IA symptoms , but not on HI symptoms. No total or indirect effects of the AnnoPred ADHD PGS emerged for IA or HI symptoms via the five brain volumes .For adolescents, the models fit acceptably for IA and HI symptoms . There was a direct effect of the AnnoPred ADHD PGS, nor were there significant indirect effects of the AnnoPred ADHD PGS via the volumes of five brain regions . We detected no significant total or direct effect of the ons Fig. . No assooms Fig. .AnnoPred method, would be mediated by volumes of five brain regions—the caudate, cingulate gyrus, angular gyrus, DLPFC, and inferior temporal lobe—in a population-based dataset of children, adolescents, and young adults. We also examined age-stratified effects of the potential mediation pathways between ADHD PGSs and ADHD symptoms. With respect to our first hypothesis, we found that the traditional ADHD PGS was not associated with either IA or HI symptoms across all developmental age groups. However, the AnnoPred ADHD PGS was associated with IA symptoms, but only in adolescents. With respect to our second hypothesis, brain volumes of the caudate, cingulate gyrus, angular gyrus, DLPFC, and the inferior temporal lobe did not statistically mediate the association between the AnnoPred ADHD PGS on IA and HI ADHD symptoms. This was consistent in our mediation models in the full sample and in the age-stratified models.The primary objective of this study was to investigate whether associations between ADHD outcomes and two types of ADHD PGSs, one computed using a traditional method and another that was biologically informed using the AnnoPred ADHD PGS was associated with IA (but not HI) symptoms for adolescents specifically. In contrast, the traditional ADHD PGS was not associated with ADHD outcomes in any of our models. Incorporating biological information into PGS computations may yield more developmentally sensitive predictive signals for ADHD symptoms than traditional ADHD PGSs, which have previously been shown to be less discriminative between various age groups [AnnoPred ADHD PGS was only associated with IA but not HI symptoms in adolescence, which is consistent with the relative stability of IA symptomology over time in individuals [First, we found that the e groups , 9. One e groups , 51–54. e groups . Our finividuals .AnnoPred ADHD PGS and ADHD symptoms would be mediated by brain volumes. In fact, the lack of strong associations between the AnnoPred ADHD PGS and the volumes of caudate, cingulate gyrus, angular gyrus, DLPFC, and the inferior temporal lobe, and between the volumes of these five brain regions and ADHD symptoms may be due to the non-clinical population of the PNC dataset. The relative lack of variability in ADHD symptomology in the PNC dataset may have limited our power to detect strong effect sizes in our study. In addition, most structural MRI studies of ADHD and ADHD GWA were conducted with case–control samples of youths and/or adults [Our findings did not support our second hypothesis that the association between r adults . The brar adults . Future r adults .AnnoPred ADHD PGSs and DLPFC volumes. Although DLPFC volume was not associated with ADHD symptoms perhaps due to the limited variability in ADHD symptoms in the PNC sample, prior studies have found that the DLPFC was the most enriched region with respect to ADHD-associated genetic variants relative to the other brain regions we tested [However, we did detect a negative association between both the traditional and e tested , 19. In e tested , 60. GivAdditionally, we found a positive association between cingulate gyrus volume and HI symptoms in the full sample and in adolescents specifically. This finding was inconsistent from prior literature which found reduced volume in the cingulate cortex in youths and adults with ADHD relative to controls , 56, 61.AnnoPred PGS and IA symptoms in adolescents, but not for HI symptoms. Fourth, we examined cortical and subcortical volumes in our study, in line with empirical precedent [Our study was limited by several noteworthy issues. First, our measure of ADHD in young adults was based on retrospective self-reports, whereas ADHD was measured in children and adolescents using collateral  reports. Reporter heterogeneity  may haverecedent . Howeverrecedent , 57, 64.recedent , 65, recrecedent . The smarecedent  given threcedent ). Additirecedent , 68, 69.AnnoPred ADHD PGS on IA symptoms, suggesting the utility of leveraging functional annotation information in computations of ADHD PGSs. Still, the “average” ADHD PGS explains only about 4% of the variance in ADHD outcomes across studies, and even less variance in population-based cohorts [To conclude, we found a developmentally specific direct effect of the  cohorts . As GWA


However, much less is known about the interplay between environmental and genetic risks relevant to ADHD—a better understanding of which could inform both mechanistic models and clinical prediction algorithms.N = 11,876) and the case‐control Oregon‐ADHD‐1000 (N = 1449), were used to examine additive (G + E) and interactive (GxE) effects of selected polygenic risk scores (PRS) and environmental factors in a cross‐sectional design. Genetic risk was measured using PRS for nine mental health disorders/traits. Exposures included family income, family conflict/negative sentiment, and geocoded measures of area deprivation, lead exposure risk, and air pollution exposure (nitrogen dioxide and fine particulate matter).Two independent data sets, the population‐based Adolescent Brain Cognitive Development Study (ABCD) . Significant effect size heterogeneity across ancestry groups was observed for genetic and environmental factors . GxE interactions observed in the full ABCD cohort suggested stronger environmental effects when genetic risk is low, though they did not replicate.ADHD PRS and family conflict jointly predicted concurrent ADHD symptoms in both cohorts. Additive‐effects models, including both genetic and environmental factors, explained significantly more variation in symptoms than any individual factor alone (joint Reproducible additive effects of PRS and family environment on ADHD symptoms were found, but GxE interaction effects were not replicated and appeared confounded by ancestry. Results highlight the potential value of combining exposures and PRS in clinical prediction algorithms. The observed differences in risks across ancestry groups warrant further study to avoid health care disparities. Both attention‐deficit/hyperactivity disorder (ADHD) polygenic risk score (PRS) and family environment were robustly associated with ADHD symptoms in two independent cohorts.Additive effects of PRS and environment explained significantly more variation in symptoms than either domain alone, indicating the potential utility of combining risk factors in clinical prediction algorithms.Genotype‐by‐environment interaction interactions observed were confounded by ancestry and did not replicate, suggesting these effects are likely small for the PRS and exposures examined here.Differences in effects of PRS and exposures across ancestry groups warrants further research to avoid health care disparities.2) and fine particulate matter (PM2.5)  is associated with social disadvantage , such as the DRD4 and MAOA variable tandem repeats  of over 11,000 children selected from around the nation, along with the unique Oregon‐ADHD‐1000 case‐control sample  that multiple genetic (PRS) and environmental risk factors are additively associated with both dimensional measures and ADHD/non‐ADHD status, and (2) that genetic effects are dependent upon environmental context (or vice versa). Novel features of this report include the use of multiple PRS, geocoded exposure variables, and the inclusion of risk factors both intrinsic and extrinsic to the family in a GxE study of ADHD. Moreover, risk‐inducing environmental exposures are more heavily concentrated among minoritized racial and ethnic groups in the U.S.  and the Oregon‐ADHD‐1000 (N = 1449). The ABCD cohort is a large, 21‐site, diverse sample of children aged 9–10 years at baseline   ADHD DSM‐oriented scale . Geospatial location data were linked to external reference databases using methods described previously  were estimated using hybrid spatiotemporal models, which utilize satellite‐based aerosol optical depth models, land‐use regression, and chemical transport models , lead exposure risk, and levels of NOFamily income and family sentiment/conflict were evaluated to index the proximal family environment. Family income scales for both cohorts are described in Supporting Information In ABCD, family conflict was measured with the family conflict subscale of the widely‐used Family Environment Scale  and predictors of interest were standardized  and the standardized regression coefficients reported. Sensitivity analyses were performed to determine if variable transformation affected the observed associations, with no appreciable effect on results  genomic and/or exposure variables. GxE effects were examined with models that included interaction terms for each pair of genomic and exposure variables—all pairs tested individually in separate models. For each model, a pooled R‐squared value was calculated as the mean of the R2 values across all 50 imputations , while effects on the categorical outcome (ADHD/non‐ADHD status) was examined only in the two case‐control cohorts. Models were fit using the meta R package  within each cohort. In ABCD, results from each of the three ancestry subgroups were compared and meta‐analyzed, using a random‐effects meta‐analysis implemented with the The categorical outcome (ADHD/non‐ADHD status) was analyzed only within the European‐ancestry subgroups, given the small number of cases in the other ancestry groups. Additionally, analyses for all outcomes were also conducted in each full cohort .N = 15 predictors tested) and the set of models examining interaction effects (N = 54 G‐E pairs tested). Individual main effects with p < .00333 and interactions with p < 9.26e‐4 were considered statistically significant in discovery analyses in ABCD. To assess replication in the Oregon‐ADHD‐1000, a significance threshold of p < .05 was used. The Supporting Information p < .10.Due to the large number of PRS and exposures examined, a Bonferroni multiple testing correction was applied separately to the set of models examining individual main effects . In terms of family environment and exposures, the Oregon‐ADHD‐1000 had significantly lower median family income, ADI, lead risk, and NO2 exposure, but significantly higher PM2.5 exposure . The same patterns hold when comparing the Oregon‐ADHD‐1000 to the ABCD matched case‐control sub‐sample . Likewise, results were the same when comparing the European‐ancestry subgroups of the two cohorts, except there was no significant difference in lead exposure risk.The distribution of ADHD symptoms, ADHD diagnoses, environmental exposures, and demographics for all three cohorts are reported in Table r = .29 and .28 for ABCD and Oregon‐ADHD‐1000, respectively), MDD (r = .23 and .17) and CUD (r = .17 and .21) .Small but statistically reliable correlations were observed among the various PRS, as expected given the known genetic correlation among psychiatric disorders. In the European‐ancestry subgroups of both cohorts, the ADHD PRS was most strongly correlated with PRS for ASD , family income , family conflict , and NO2 levels . In the Oregon‐ADHD‐1000 cohort, ADHD PRS was correlated with negative sentiment  and lead risk .Significant correlations between PRS and environmental measures were also seen in both cohorts. In ABCD the ADHD PRS was weakly, but significantly, correlated with ADI , indicating the measures represent distinct risks. Correlations among all PRS and environmental measures in the European‐ancestry subgroups are reported in Figure Correlations among environmental exposures were small to moderate , family income , and family conflict  were all significantly associated with total ADHD symptoms in the expected direction. PRS for ADHD , MDD , ASD , and CUD  were also significantly associated with total symptoms.For dimensional analysis of total ADHD symptoms, in the ABCD European‐ancestry subgroup, ADI , family income , ADHD PRS , MDD PRS , and ASD PRS  on total ADHD symptoms.Replication in the Oregon‐ADHD‐1000 European‐ancestry subgroup was observed for effects of familial negative sentiment , family conflict , and ADHD‐PRS . However, tests of heterogeneity suggest real differences in the effects of risk factors across the three ancestry groups in ABCD for ADI  and income , as well as PRS for ADHD , MDD , and ASD .Results for all ancestry groups in ABCD are shown in Figure p = 7.93e‐5), family conflict (log(OR) = .517, p = 2.02e‐9), and ADHD‐PRS (log(OR) = .479, p = 8.51e‐8) were significantly associated with ADHD status in the European‐ancestry matched case‐control subsample of ABCD. In the European‐ancestry subgroup of the Oregon‐ADHD‐1000 cohort, effects of negative sentiment (log(OR) = .863, p = 6.57e‐14) and ADHD‐PRS (log(OR) = .533, p = 3.56e‐8) replicated, but family income was not statistically significant (log(OR) = −.117, p = .186)  = −.322, R2 values, are reported in Table The additive effects of all PRS and environmental exposures were examined by including all risk factors in the same regression model and applying backwards selection using a stringent cutoff (see Methods) until only those risk factors significantly and independently associated with the outcome after multiple testing remained. The effect estimates from these reduced additive‐effects models, along with R2 = .091) than any single genetic or environmental factor . In the Oregon‐ADHD‐1000 European‐ancestry subgroup, only the effects of ADHD‐PRS and negative sentiment replicated (p < .05) in the additive‐effects model . Again, the additive model explained significantly more symptom variance (R2 = .173) than either factor alone .In the ABCD European‐ancestry subgroup, the ADHD‐PRS, ADI, family income, and family conflict were independently and additively associated with total ADHD symptoms. The additive effects of these three risk factors explained significantly more symptom variation  and ADHD‐PRS , but effects of income (p = .005) and ADI (p = .523) were not significant (after multiple‐testing correction) due to non‐significant effects in the Hispanic/Latino and Black/African‐American groups.Comparisons of additive effects on total ADHD symptoms across the three ancestry groups in ABCD are shown in Figure Additive polygenic and environmental effects are consistent across a variety of dimensional measures of ADHD symptoms , the additive‐effects models in the ABCD and Oregon‐ADHD‐1000 cohorts had mean AUC‐ROCs of 0.699 and 0.764, respectively  interactions between family income and PRS for SCZ, BP, CUD, and ALCDEP were significantly associated with both total ADHD and inattention symptoms. For total ADHD symptoms, the strongest interaction effect, between income and SCZ‐PRS Figure , indicatp‐values >.05).However, it should be noted that interactions observed in the full ABCD cohort should be interpreted with caution, given that PRS and income/ADI are confounded with ancestry  <.1) and there was little attenuation of effects in additive models, indicating the PRS and environmental effects on ADHD symptoms were largely independent. However, because the ADHD‐PRS explains only a fraction of the total genetic risk for ADHD, it is likely that our findings are an underestimate of true G‐E correlation , these correlations were small . New here is more evidence that additive effects of PRS and exposure measures explained markedly and significantly more symptom variance than either domain alone. This is promising and important for future efforts to develop clinical prediction algorithms. Yet, these models explained only a small fraction of the variance, confirming that additional measures will be needed beyond PRS and global exposure indices to develop such prediction algorithms for research or clinical use. Our finding that the fraction of variance explained by PRS can be increased suggests that incorporating additional measures  may yield further increases in the accuracy of predictions.As expected, polygenic risk for ADHD was significantly associated with ADHD symptoms in the European‐ancestry subgroups of both cohorts  failure to replicate in the Oregon‐ADHD‐1000 cohort and (b) apparent confounding due to ancestry effects. It should be noted however that failure to see interaction effects of pollution measures with PRS scores hardly rules out the likelihood that GxE effects occur at a more granular level. For example, the effect of even low‐level lead exposure on ADHD varies depending upon genotypes in genes regulating lead metabolism . It is likely that toxicants play a role much earlier in development , and association between family conflict/negative sentiment and ADHD independent of polygenic risk for psychopathology. Finally, it supports the utility of combining environmental exposure measures and PRS in future prediction algorithms.In conclusion, the present study adds to the literature finding that interactions between PRS and particular exposures are elusive and likely small for mental disorders in children. However, other types of interactions that are theoretically driven remain in need of examination. At the same time, results confirm association between ADHD‐PRS and ADHD (Megan M. Herting, Stephen V. Faraone, Michael A. Mooney, Joel T. Nigg: Conceptualization. Megan M. Herting, Michael A. Mooney, Joel T. Nigg, Peter Ryabinin, Joel Schwartz, Katharine Selah: Methodology. Dylan Antovich, Michael Kozlowski, Michael A. Mooney, Hannah Morton, Peter Ryabinin, Katharine Selah: Formal Analysis. Megan M. Herting, Joel T. Nigg, Joel Schwartz: Resources. Hannah Morton, Elizabeth Nousen, Jessica Tipsord: Data Curation. Michael A. Mooney, Peter Ryabinin: Visualization. Joel T. Nigg, Jessica Tipsord: Project Administration. Michael A. Mooney, Joel T. Nigg: Supervision. Joel T. Nigg: Funding Acquisition. Michael A. Mooney, Joel T. Nigg, Peter Ryabinin: Writing – Original Draft. All authors: Writing – Review & Editing.Stephen V. Faraone and Joel T. Nigg both serve on the JCPP Advances Editorial Advisory Board. The remaining authors have declared they have no competing or potential conflicts of interest.Human subjects and ethics approval was obtained from the local University Institutional Review Board. A parent/legal guardian provided written informed consent, and children provided informed written assent.Supporting Information S1Click here for additional data file.


Attention deficit and hyperactivity disorder (ADHD) and Anxiety disorder (AD) are psychiatric conditions that should be kept in mind in every child and adolescent presenting with irritability. It is known that circadian rhythm disorders and especially the evening type chronotype are associated with ADHD and AD symptoms.In this study, it was aimed to reveal the relationship between the chronotype and sleep habits and the level of irritability between the two groups in children and adolescents with AD and ADHD.In this cross-sectional study, 38 cases diagnosed with AD for the first time, 38 cases diagnosed with ADHD for the first time, and 76 healthy control groups without any psychiatric disorder or physical disease were included in this cross-sectional study. In the study, the sleep habits of the participants were evaluated with the Child Sleep Habits Questionnaire (CIAA); the chronotype preferences of the participants with the Child Chronotype Questionnaire (CCTQ) and the irritability levels of the children with the Affective Reactivity Index parent report form (ARI-P)It was observed that the AD and ADHD groups had significantly higher ARI-P, CCTQ and CIAA scores compared to the control group. In the correlation analysis, when ADHD and AD were evaluated alone, no significant difference was found between CIAA and ARI-P. The ADHD group had higher CCTQ and ARI-P scores, although not statistically significant, compared to the AD group. Although there was no significant relationship in the AD group, a weak relationship was found between CIAA and ARI-P in the ADHD group.In our study, it was observed that the evening type chronotype was more prevalent in children with ADHD and AD, and sleep disorders and irritability were more common than the control group. When ADHD and AD groups were compared, no statistically significant difference was found. In the literature, it has been stated that evening chronotype carries a higher risk in terms of psychopathology, irritability seen at a young age can predict anxiety and mood disorders in adulthood, and irritability seen in ADHD can predict mood disorders that occur during follow-up. In this context, investigating the relationship between irritability, sleep disorders and chronotype on the basis of psychopathologies can make important contributions to the literatüre.None Declared


Natural products are chemical compounds that form the basis of many therapeutics used in the pharmaceutical industry. In microbes, natural products are synthesized by groups of colocalized genes called biosynthetic gene clusters (BGCs). With advances in high-throughput sequencing, there has been an increase of complete microbial isolate genomes and metagenomes, from which a vast number of BGCs are undiscovered. Here, we introduce a self-supervised learning approach designed to identify and characterize BGCs from such data. To do this, we represent BGCs as chains of functional protein domains and train a masked language model on these domains. We assess the ability of our approach to detect BGCs and characterize BGC properties in bacterial genomes. We also demonstrate that our model can learn meaningful representations of BGCs and their constituent domains, detect BGCs in microbial genomes, and predict BGC product classes. These results highlight self-supervised neural networks as a promising framework for improving BGC prediction and classification. Biosynthetic gene clusters (BGCs) encode for natural products of diverse chemical structures and function, but they are often difficult to discover and characterize. Many bioinformatic and deep learning approaches have leveraged the abundance of genomic data to recognize BGCs in bacterial genomes. However, the characterization of BGC properties remains the main bottleneck in identifying novel BGCs and their natural products. In this paper, we present a self-supervised masked language model that learns meaningful representations of BGCs with improved downstream detection and classification. Natural products are chemical compounds that form the basis of many pharmaceuticals and clinical therapeutics . Their cDiverse natural products can be produced in living organisms via groups of genes called biosynthetic gene clusters (BGCs). Genome mining has become a powerful tool for exploring the complex and diverse chemical space of natural products . Fast, iIdentification of BGCs directly from genomic sequences is critical to navigating natural product space and nominating novel natural products. While complementary data modalities involving joint genome sequencing and mass-spectrometry data can be used to link products with gene clusters , the majantiSMASH  is an early tool for BGC discovery that uses a set of curated profile-Hidden Markov Models (pHMMs) to call biosynthetic gene families and a set of heuristics to tag a portion of a genome as a BGC , 9. antiA recent approach, DeepBGC, introduced a deep learning genome-mining strategy for biosynthetic gene cluster annotation that addresses these limitations . SimilarRather than relying on expert-curated annotations and negative examples, self-supervised masked language models promise the ability to learn biologically-relevant patterns directly from a large set of BGC examples. Recently, self-supervised masked language models of biological sequences have been used to study proteins –21, DNA Biosynthetic Gene CARP (BiGCARP). We leverage these representations to detect BGCs from microbial genomes and then classify them based on their natural product class. We further investigate the potential advantages of our model by comparing our approach with DeepBGC, and demonstrate that BiGCARP achieves improvements in BGC prediction and natural product classification. BiGCARP highlights self-supervised neural networks as a promising framework for improving BGC characterization.To accelerate identification and classification of BGCs, we developed a self-supervised neural network masked language model of BGCs from bacterial genomes . Our modBiosynthetic Gene Convolutional Autoencoding Representations of Proteins (BiGCARP) to reconstruct the original class token and Pfam sequence. BiGCARP combines the ByteNet encoder dilated CNN architecture from [We first developed a self-supervised training scheme to train BiGCARP to learn representations of BGCs. As BGCs have a hierarchical structure, they can be represented at four main levels. From the least-to-most granular, these are: genes, Pfam domains (families of evolutionary-related proteins), amino acids, and nucleotides. We note that more granular units of representation lead to longer sequences. BGCs typically contain several dozen genes, each of which contains one or more Pfam domains. Each Pfam domain contains tens to hundreds of amino acids, and each amino acid is encoded by three nucleotides. This introduces a trade-off between modeling short sequences where each unit is complex or modeling long sequences where each unit is simple. In order to balance input sequence length and information content of individual units, we chose to represent BGCs as sequences of Pfams. This is the same level chosen by DeepBGC . As showure from  with linPfam embeddings map protein families from our vocabulary to vectors into a 1280-dimensional space, and thus serve as the inputs to BiGCARP. We train three versions of BiGCARP with different initial Pfam embeddings. The BiGCARP-ESM-1b-finetuned and BiGCARP-ESM-1b-frozen models are both initialized with Pfam embeddings obtained by averaging the per-residue output from ESM-1b for each domain. BiGCARP-ESM-1b-finetuned has its embeddings finetuned during self-supervised BGC training, while BiGCARP-ESM-1b-frozen has the initial embeddings frozen at the onset of self-supervised BGC training. Finally, BiGCARP-random is initialized with a random Pfam embedding, which is finetuned during self-supervised BGC training. All three versions of BiGCARP are trained on BGC sequences extracted from the antiSMASH dataset . We usedWe used uniform manifold approximation and projection (UMAP) to visualize the input Pfam embeddings after self-supervised training on the antiSMASH training set . Each prWe find that initializing Pfam domain embeddings using ESM-1b improves the quality of the learned representations, as these embeddings take into account protein family amino-acid sequence and protein structural information. We next evaluated BiGCARP’s pretraining performance after self-supervised training . We use In addition, using the 9-genomes validation set from DeepBGC , we evalWe then finetuned BiGCARP on the training dataset reported in DeepBGC v0.1.0 , which uIn addition to detecting BGCs in microbial genomes, predicting their product classes would provide further aid in discovering new natural products. BiGCARP learns to predict a BGC’s product class from its Pfam sequence by reconstructing masked class tokens during self-supervised training . During All three versions of BiGCARP out-perform DeepBGC on average AUROC across the product classes, and ensembling their predictions via their arithmetic mean further improves accuracy, as shown in datasets and annotations used can be found on Zenodo.We compared the ability of BiGCARP and antiSMASH 6.1.1  to identBiosynthetic Gene Convolutional Autoencoding Representations of Proteins (BiGCARP), a masked language model that learns representations of BGCs based on their Pfam domains, detects BGCs, and predicts their product classes. To our knowledge, this is the first work to use Pfam domains as tokens in a masked language model. We demonstrate that our model learns biologically-reasonable representations of Pfam domains. Representing BGCs as Pfam domains was a compromise between limiting the sequence length while having fine-grained sequence information. Models on the level of amino acid residues or the nucleotide sequence may be able to resolve more details at the cost of more computation. BiGCARP is a strong BGC detector even without seeing negative examples, and achieves state-of-the-art accuracy in product class prediction.Biosynthetic gene clusters (BGCs) are a promising source of natural products, but are difficult to discover, express, and characterize. Recent work in self-supervised deep learning has shown promise for modeling DNA, RNA, proteins, and glycans. We develop In presenting the first use of Pfam domains as tokens for a self-supervised language model, this work opens opportunities for future method development and model refinement. For example, our results indicated minimal benefit to using ESM-1b pre-trained Pfam embeddings. Future work could evaluate whether complementary methods for protein sequence pretraining, such as the convolutional-based CARP model , confer For BGCs, these downstream tasks could include predicting their expression conditions or the chemical structures of their products. Future work to assess the performance benefit of fine-tuning BiGCARP will help determine the potential utility of BiGCARP for a variety of downstream tasks. Without fine-tuning, our models are useful for detecting previously unknown BGCs in microbial genomes and predicting BGC product classes.In summary, we present BiGCARP, a self-supervised masked language model for the detection and characterization of biosynthetic gene clusters. The BiGCARP model described here could be deployed for downstream tasks of interest, including chemical product structure characterization and BGC mining. This study highlights the potential of self-supervised deep learning as a framework for BGC discovery and characterization.In this section, we elaborate on details of our self-supervised deep learning framework for detecting BGCs from bacterial genomes and classifying them into their natural product classes. The workflow is summarized in datasets used can be found on Zenodo. Future work may include using newer antiSMASH databases for pretraining for improvement of BiGCARP.To curate our pretraining dataset, we ran antiSMASH  2.0, a microbial genome mining tool for BGC identification and analysis , on a daOur training, validation, and test sets were produced from an 80/10/10 split of the total set. Note that random splitting of data is widely avoided in biological sequence modeling, since it leads to evaluation of overly simple generalization. For example, in protein modeling, one instead uses sequence-identity based splits as a proxy for evolutionary signal . Proper mapping from antiSMASH product types to common MIBiG compound classes can be found on Zenodo.We evaluated the performance of our models and compared it to the DeepBGC model by testing its ability to detect BGCs within bacterial genomes and to predict their corresponding product classes. To do this, we utilized DeepBGC’s training set with 617 positive and 10128 negative BGC samples to finetune our models [To demonstrate the applicability of BiGCARP in advancing natural product discovery, we curated 773 unannotated bacterial genomes for BGC identification. We obtained all bacterial genomes with an assembly level of ‘complete’, ‘chromosome’, or ‘scaffold’ in GenBank and FASTA format released after 4 September 2020 available to download from the NCBI Datasets Genome Data Package using the ncbi-genome-download tool, yielding 108,007 assemblies that are unannotated in antiSMASH database version 3.0. We randomly chose 773 to analyze.We then used Prodigal  version We represent each Pfam as a vector. To do this, we take the first sequence in the alignment for a Pfam, then use ESM-1b , a proteWe train BiGCARP using the masked language model objective described in . We prep−4, and mixed precision training using PyTorch [Model weights and datasets are available on Zenodo; training code is available on our BiGCARP repository and code to run pretrained BiGCARP models is available on our protein sequence models repository on Github, with a command line manual for how to use the models. We do not report replicates for results as that would require training each model from scratch multiple times.BiGCARP is a dilated 1D-convolutional neural network masked language model based on ByteNet  and CARP PyTorch  and NVIDWe use the intuition that the model should make more confident predictions when given BGC sequences than non-BGC sequences to predict BGC start locations and whether each domain is part of a BGC. For each bacterial genome, we prepend a mask token to each possible subsequence of 64 domains and pass the resulting sequences to BiGCARP. With the exception of domains at the beginning and end of the genome, each domain is thus scored 64 times. For each window, we calculate the entropy of the predictions for the prepended mask token (start entropy), the entropy for each of the 64 domains in the window (domain entropy), and the negative log-likelihood of each domain in the window (negative log-likelihood). We predict whether a domain is the start of a BGC using the start entropy of the window for which it is the first domain; positions with a lower start entropy are more likely to be BGC start locations. We predict whether each domain is part of a BGC using the average of the start entropies for every window in which it appears and its domain entropy and negative log-likelihood within each window in which it appears . Domains with lower scores are more likely to be within a BGC.−4 with early stopping using supervised results on 9-genomes.We follow the supervised training procedure described in DeepBGC. Using the positive BGC domain sequences from MiBIG (version 1.4) and 10128 negative BGC domain sequences from DeepBGC, at each epoch, we shuffle the sequences into a “genome” and then predict whether each domain is part of a BGC. We fine-tune the self-supervised versions of BiGCARP as well as a randomly-initialized version using the Adam optimizer and a learning rate of 10S1 Fig(a) Heatmap of Euclidean distances between domain embeddings in the following Pfam clans: NADP_Rossmann, P-loop NTPase, and MBB. (b) Average Euclidean distance between Pfam domain embeddings in the aforementioned Pfam clans (c) Pairwise sequence alignment percent identity matrix between Pfam clans.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.