Multiple OR were assessed as follows: All OR (AOR): response in all organs, mixed OR (MOR): response in some organs, no OR (NOR)]. AOR rates at 6 months improved with deepening HR; complete response , very good partial response , and partial response , respectively. A composite HR/OR (CHOR) model was developed using incremental scoring based on hazard ratios with scores of 0–3 for HR  and 0–2 for OR . Patients could be divided into two distinct CHOR groups (scores 0–3 and 4–5), with median OS in group 1 and group 2: Not reached vs. 34 months, p < 0.001 [Mayo] and 87 vs. 23 months, p < 0.001 [Pavia]. In conclusion, we developed a model that can assess multiple organs concurrently, and integrate both HR and OR assessments to determine early clinical benefit with treatment, which may be used as a surrogate end-point in trials and to compare outcomes with different therapies.Newly diagnosed AL amyloidosis patients were evaluated to develop a model for early assessment of treatment benefit at 6 months, integrating both hematologic (HR) and organ response (OR) assessment (testing cohort, Mayo:  The most commonly affected organs include the heart, kidney, and liver; and many patients have more than one organ involvement5. Prognosis depends both on the severity of organ involvement, especially the heart, and the underlying plasma cell burden8. Treatment is targeted toward the plasma cell clone13. In most patients, the organ dysfunction is the main driver of morbidity and mortality and the plasma cell burden is usually low15. Given this, it would be ideal to assess treatment efficacy by its impact on organ improvement. However, time to organ response (OR) can be varied and is usually delayed5. Therefore, treatment efficacy, especially early-on is typically determined by hematologic response (HR)16.Deposition of misfolded light chains secreted by the plasma cell clone leads to organ dysfunction in patients with light chain (AL) amyloidosis17. No model currently exists to integrate the two assessments for clinical use. This makes early assessment of treatment benefit difficult in this disease, preventing relatively rapid evaluation of clinical trial results and precludes design of clinical trials for timely intervention in patients with likely poor outcome with ongoing therapies. Early identification of patients who are not likely to benefit from a given therapy is increasingly important as these patients have an inferior survival and more treatment options are becoming available for these patients3. A composite model that takes into account both HR and OR at a given time point may allow for early assessment and thus become a useful surrogate endpoint for clinical trials. It may also identify patients who have not achieved a deep HR, but can safely continue first line therapy if they have achieved an OR. Such a surrogate model may also be helpful in light of new, emerging therapies that target the amyloid fibril and can potentially lead to earlier OR19.Deep HR increases the likelihood of OR and long-term survival, but this is not always the case and there is inter-patient variability in the relationship between depths of HR and ORIn this study, we have developed and validated a composite model to integrate OR and HR in AL amyloidosis to define a surrogate end point for use in treatment trials in AL amyloidosis.20 or mass spectrometry21. We identified 875 patients diagnosed from 1/1/2006 to 12/31/2015 from the Mayo Clinic dataset, of which 473 patients had HR and OR data available at 6 months  <5 mg/dL were assessed for response by criteria of complete response (CR) only, based on recent data24. Organ involvement and response were assessed by using existing criteria as described in supplementary data27. OR was classified as all organ response (AOR): response in all of the involved and evaluable organs ; mixed organ response (MOR): response in at least one of the organs and no organ response (NOR). Patients were assessed for response at the 6 months (±2 months) and 12 months (±2 months) time-point in the Mayo cohort and at 6 months time-point (±2 months) for the Pavia group.In evaluable patients, HR was assessed using validated criteriaA model for CHOR was developed using the Mayo Clinic test cohort Fig. . Patienthttp://www.medcalc.org) for the Pavia cohort. Chi-Square and Fischer Exact tests were used to carry out univariate analysis for categorical variables and Wilcoxon Rank Sum/Kruskal–Wallis for continuous variables. Survival analysis was carried out using the Kaplan–Meier method and the log-rank test was used to compare survival curves. Cox proportional hazards model was used to evaluate hazard ratios for survival. 95% confidence intervals (CI) are reported. OS was defined as the time from start of treatment to death. Cox regression was used to compare the predictive power of HR, OR, and the composite CHOR model28. Goodness of fit of nested models was evaluated using likelihood ratio tests. Predictive power of the individual and composite models were compared using Harrell’s C29. All hypothesis tests were two-sided, p-values below 0.05 were considered statistically significant.Statistical analysis was carried out using the JMP  and Stata  software for the Mayo Clinic cohort and using MedCalc Statistical Software version 18.1 ; bortezomib-based chemotherapy, 21% (n = 100); alkylator-based therapy, 35% (n = 165), immunomodulatory drugs 3% (n = 13), and steroids/other in one patient. HR rates at 6 months in the 410 evaluable patients were as follows: CR was seen in 25% (101/410), VGPR in 35% (144/410), PR in 24% (99/410), while 16% (66/410) of patients had NR/disease progression. There were 63 patients who were not evaluable given baseline dFLC <5 mg/dL. Of these, 29% (18/63) achieved a CR. In the Pavia cohort, treatment received was as follows: bortezomib-based chemotherapy, 44% (n = 255), melphalan and dexamethasone, 40% (n = 234), immunomodulatory drugs 9% (n = 52), ASCT, 1% (n = 9), and steroids/other 4% (n = 25). HR rates at 6 months were: CR: 14% (74/536), VGPR: 33% (176/536), PR: 19% (105/536), while 34% of patients had NR/disease progression (181/536). Remaining 39 patients had dFLC< 5 mg/dL and all achieved a CR.First-line treatment received by the 473 patients in the Mayo cohort was as follows: autologous stem cell transplant (ASCT) with or without induction therapy, 41% . In the Mayo cohort, cardiac response was seen in 25% (73/293), renal response in 34% (103/300), and liver response in 25% (16/64) of patients evaluated at the 6 months time-point, respectively. Rates of OR at 6 months in the Pavia cohort were: cardiac: 27% (116/424), renal: 36% (135/375), and liver: 17% (9/52), respectively.For individual OR assessment, patients on dialysis at diagnosis were excluded from renal response assessment , NOR: 60% (n = 282/473). OR rates improved at 12 months; AOR: 45% (n = 194/435), MOR: 12% (n = 54/435) and NOR: 43% (n = 187/435). Combined OR rates at 6 months in the Pavia cohort were as follows: AOR 21% (n = 120/575), MOR: 18% (n = 105/575), NOR: 61% (n = 350/575). Combined OR rates increased with deeper HR. In the Mayo cohort, OR rates at 6 months for patients achieving hematologic CR (including CR in dFLC < 5 mg/dL) were: AOR 38% (45/119), MOR: 13% (15/119), and NOR 50% (59/119). OR in the hematologic VGPR group (including dFLC < 5 mg/dL without CR) were AOR: 30% (57/189), MOR: 16% (31/189), and NOR: 53% (101/189) and in the hematologic PR group were AOR: 16% (16/99), MOR: 18% (18/99), and NOR 66% (65/99). In the Pavia cohort, OR rates for patients achieving hematologic CR were: AOR 35% (40/113), MOR: 23% (26/113), and NOR 41% (47/113). OR in the hematologic VGPR group were AOR: 26% (46/176), MOR: 22% (39/176), and NOR: 51% (91/176) and in the hematologic PR group were AOR: 21% (22/105), MOR: 20% (21/105), and NOR 59% (62/105).Combined OR in the Mayo Clinic cohort at 6 months was as follows: AOR 26% (125/473), MOR: 14%  was not reached. vs. 81 vs. 52 months. These parameters were evaluated in the Pavia cohort based on 6-month OR and were predictive of OS In patients with involvement of both heart and kidney and who achieved cardiac response by 6 months, status of renal response did not impact survival further as shown in N = 349), group 2: scores of 4–5 (N = 124). As illustrated in Fig. p < 0.001) with HR of 3.4 (2.5–4.6), p < 0.001. This model was then validated in the Pavia cohort and median OS for patients in CHOR group 1 vs. 2 was 87 vs. 23 months, p < 0.001 with HR of 2.8 (2.2–3.5), p < 0.001. This model was also tested in sub-groups of patients with Mayo 2012 stages I–IV and at 12 months in the Mayo cohort with similar results  was developed based on HR and OR as described in the “Methods” section Fig. . PatientC = 0.59) compared to the HR model  as well as when compared to the OR model . In the subset of patients with cardiac involvement, the CHOR model (C = 0.62) had significantly higher predictive value compared with the HR model ; but not when compared to achieving a cardiac response . In patients with renal involvement, the CHOR model (C = 0.58) had significantly higher predictive value when compared with achieving a renal response ; however this difference was not significant when compared to the HR model We compared the CHOR model (group 1 vs. 2) at the 6-month time-point to (1) the HR criteria (achieving CR vs. not) and (2) achieving AOR vs. not using Cox regression, with Mayo cohort as the training cohort and Pavia cohort as the validation cohort. The CHOR model had significantly higher predictive power  in a group of patients. Our retrospective study assessed response with treatment in two independent cohorts of patients with AL amyloidosis to develop and validate a model integrating simultaneous assessment of both HR and OR. Importantly, the model was able to predict OS in both cohorts, with greater predictive power compared with HR or OR assessed in isolation. This model can be used as a surrogate end-point for rapid assessment of clinical trials. This would allow for shorter duration of follow-up and enable faster completion of these studies. This model can be incorporated in studies designed to make early treatment changes based on response. It can also be easily integrated into clinical practice for prognostication and integrating data across different therapeutics for clinical decision making.30. Rates of transplant were strikingly different in the two cohorts , which suggests that the results and the CHOR model are generalizable to patients managed with different treatment approaches.The testing and validation cohorts were large independent cohorts from amyloidosis referral centers with long-term follow-up data. The majority of patients in both cohorts had cardiac involvement and about one-half had involvement of more than one major organ. Overall treatment patterns observed in our cohorts are similar to other cohorts reported over this time periodAs there is no current method to evaluate multiple organ responses simultaneously, we first developed a combined parameter to assess OR. In both cohorts, patients who achieved response in all organs (AOR) had significantly better OS than those achieving response in some (MOR) or none (NOR) of the involved organs. When comparing MOR and NOR subgroups, there was no difference in OS in the overall Mayo Clinic cohort. However, there was clearly a significant difference MOR vs. NOR groups when evaluating patients with more than one organ involvement, which is the group where mixed or discordant organ responses are possible. This OR end-point was then combined with HR in a simple, easy to use CHOR model which scored patients from low to high if they achieved response vs. not. This scoring was derived from HR for survival from Cox proportional hazards analysis. Patients could be categorized into two distinct groups with different survival outcomes based on OR and HR assessment at the 6-month landmark in both cohorts. This model was able to distinguish between patients at the 12-month landmark as well. Moreover, this composite model had better predictive power for OS than either HR or OR in isolation in both the test and validation cohorts. The absolute survival outcomes in various groups were different in the Mayo Clinic and Pavia cohort. These differences are likely attributable to several factors including the differences in treatment, specifically the rates of stem cell transplant, which were strikingly different (41% vs. 1%) and possibly the responses in individual organs, particularly cardiac response, which is the major driver of survival in AL amyloidosis. Further, while the absolute survival outcomes differed in the two cohorts, the general magnitude of difference was similar.27. However, renal response remains important as it is a strong predictor for renal failure requiring dialysis as shown in our study and prior reports27. As over two-thirds of patients with AL amyloidosis can have involvement of more than one organ4, the combined CHOR model, with superior predictive value would be applicable to all patients with AL amyloidosis.In subset analysis of patients with cardiac involvement, the composite model had better predictive value compared with HR, but not cardiac response. This may be due to lack of adequate power or alternatively, cardiac response may be the main driving factor impacting survival. On the other hand, in the subset of patients with renal involvement, the CHOR model performed better compared to achieving renal response in predicting patient survival, but not when compared to HR. This may again be due to lack of adequate power or the fact that achieving renal response does not impact OS27.Overall, patients who achieve both OR and HR early in disease course have superior outcomes. This finding is reassuring and the development of a model which can be used systematically to assess both responses is a novel contribution of our study. The current model is able to integrate the relative improvements in hematological and organ parameters to provide a unified readout that can be used in clinical trials, as well as in clinical practice for potentially altering treatment approaches. Our study has limitations given its retrospective design, and heterogeneous nature of treatment received by patients. Moreover, the survival outcomes of patients in the two cohorts are different, likely related to baseline risk and differences in therapies used. However, development of a model in a real world scenario with an independent validation cohort results in wider applicability of the model. Previous cohorts of patients that have been used for development of HR and OR criteria for amyloidosis have also been treated in a heterogeneous mannerIn conclusion, we have developed a model in AL amyloidosis to assess multiple organs concurrently, as well as integrate both HR and OR assessments to determine early clinical benefit with treatment, supporting its use as a surrogate end-point in clinical trials and compare outcomes with different therapeutic approaches. Future studies incorporating this endpoint should be designed to evaluate the utility of changing treatment in patients not achieving this endpoint.Supplementary Data


We aimed to evaluate the effect of vitamin D levels on the functional outcome of elderly women who underwent total knee arthroplasty (TKA).Seven hundred and four patients (1013 knees) who underwent primary TKA were included in our retrospective study. Preoperative vitamin D levels were measured and the relationship analyzed between these and age, weight, height, body mass index, and bone mineral density. Two hundred and twenty patients (220 knees) who received unilateral TKA and were followed up for more than 1 year after operation were divided into two groups: Group 1, serum 25-hydroxyvitamin D3 (25(OH)D3) level < 20 ng/ml; and Group 2, 25(OH)D3 level ≥ 20 ng/ml. Both groups were evaluated for the relationship between vitamin D levels and postoperative Visual Analogue Scale (VAS) score, Knee Society Knee Score (KSKS), Knee Society Function Score (KSFS), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) score.p = 0.033). No significant differences were observed between the groups in terms of postoperative VAS score, KSKS, KSFS, and WOMAC score.The number of vitamin D-deficient patients (< 20 ng/ml of serum 25(OH)D3 level) was 556 (79.0%). In the correlation analysis, the vitamin D level was negatively correlated with weight only (Vitamin D deficiency was highly prevalent in patients who underwent TKA. Vitamin D levels negatively correlated with weight. Low vitamin D level was not a risk factor for unsatisfactory TKA outcome in elderly women. Total knee arthroplasty (TKA) is one of the most successful interventional procedures to improve the health of patients . Based oVitamin D is well known to play an important role in the bone and mineral metabolism of the body, and a deficiency of Vitamin D can cause rickets in children, osteopenia in adults, osteoporosis, and fractures. Vitamin D deficiency has been noted to be relatively common worldwide; according to the various criteria of vitamin D 25-hydroxyvitamin D3 (25(OH)D3) deficiency, the prevalence rate is approximately 47–93% in Korea .Osteoarthritis is a disease that causes the inflammation and deformation of joint cartilage and the synovial and peripheral synovial membrane . VitaminLow vitamin D levels have been reported to adversely affect functional outcomes after hip arthroplasty, and it has been reported that vitamin D deficiency in patients with knee arthroplasty is associated with a high complication rate, poor functional state before and after surgery, and long hospital stay –22. HoweHowever, the Korean female population has the lowest absolute concentration of serum 25(OH)D3 compared with other countries, and the prevalence of vitamin D deficiency is higher in elderly women. The criterion for vitamin D deficiency is also different (25(OH)D3 level < 20 ng/ml) –25. For We hypothesized that the vitamin D level would affect the short-term functional outcome after total knee arthroplasty in Korean elderly women.This study was approved by the institutional review board of our hospital.A total of 704 patients (1013 knees) who underwent primary total knee arthroplasty from February 2012 to December 2015 by one surgeon were included in our study. Preoperative vitamin D levels were measured the day before operation, regardless of whether or not the patient had vitamin D intake. The mean age of the 704 patients (1013 knees) was 69.4 ± 7.0 years (range 41–87 years). Seventy-six patients (10.8%) were male and 628 patients (89.2%) were female. The number of vitamin D-deficient patients (< 20 ng/ml of serum 25(OH)D3) was 556 (79.0%). The prevalence of vitamin D deficiency and the correlation between vitamin D and baseline characteristics were investigated.3 level < 20 ng/ml; and group 2, ≥ 20 ng/ml) [Then, to further investigate the relationship between functional scores and vitamin D levels, we excluded patients who could affect the functional score. First, 76 male patients (94 knees) were excluded. Afterward, another 291 patients (582 knees) who underwent bilateral surgery during the study period were excluded because one side of the operation could influence the pain and rehabilitation of the other side. Also, patients with postoperative complications that might affect the rehabilitation process (four patients and four knees) and patients that underwent reoperation (one patient and one knee) were excluded. Another 112 patients (112 knees) who were not followed up for 1 year after the surgery were excluded. Finally, 220 female patients (220 knees) who received unilateral TKA and were followed up for more than 1 year after the operation were included in the analysis of the relationship between vitamin D levels and functional score. Vitamin D deficiency was defined as a serum 25(OH)D3 level of less than 20 ng/ml and participants were divided into two groups: group 1, 25(OH)D0 ng/ml) . Among tT-score of the proximal femur and the average of the two lowest scores of the lumbar spine. The VAS score, KSKS, KSFS, and WOMAC score were measured again 1 year after surgery.The preoperative serum 25(OH)D3 level was measured, and their medical records were reviewed for age, sex, height, weight, body mass index (BMI), bone mineral density (BMD), Visual Analogue Scale (VAS) score, Knee Society Knee Score (KSKS), Knee Society Functional Score (KSFS), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) score. The BMD was assessed using dual-energy X-ray absorptiometry , which included the first to fourth lumbar spine segments and the proximal femur. The representative value of BMD was taken from the lowest A single senior surgeon performed all surgeries. Skin incisions were at the midline, and midvastus arthrotomy was done for all patients. The posterior cruciate was sacrificed, and Advance Medial Pivot Total Knee Arthroplasty  and NexGen Legacy posterior-stabilized (LPS)-flex fixed TKR system  implants were used. Cement was used for implantation in all cases. Immobilization was performed using an extension brace on the first postoperative day and patients were allowed to undertake quadriceps strengthening exercises and active straight leg-raising as instructed prior to surgery. On the second postoperative day, continuous passive motion exercises were initiated. At 3–4 days after surgery, when straight leg-raising could be performed without difficulty, progressive weight bearing was started with the assistance of a walker or a cane.t test and chi-square test were performed for the comparison between the groups. In addition, an analysis of covariance (ANCOVA) was performed to control the variables with a significant correlation with the vitamin D level. p < 0.05 was regarded as statistically significant. Statistical analysis was performed using SPSS version 21 .Data were presented as means with ranges. All of the continuous variables (preoperative serum 25(OH)D3, age, height, weight, BMI, BMD, VAS score, KSKS, KSFS, and WOMAC score) are accepted to be normality assumptions based on the Kolmogorov–Smirnov test and parametric tests were performed. Pearson correlation was used to analyze the correlation between vitamin D level and various variables. The two-sample Before comparing the postoperative results of the two groups, correlation analysis was performed to identify the variables that could disturb the level of vitamin D. There was no correlation between preoperative vitamin D levels and age, height, BMI, and BMD. However, preoperative vitamin D levels showed a negative correlation with weight Table .Table 2The preoperative VAS score, KSKS, KSFS, and WOMAC score showed no significant differences between the two groups and significantly improved after operation in both groups. Regarding the correlation between weight and vitamin D levels, the postoperative VAS score, KSKS, KSFS, and WOMAC score indicated no significant variations between the two groups Table .Table 3The most important finding of the present study is that there is no relationship between preoperative vitamin D levels and short-term functional score after TKA.Until now, several functions of vitamin D have been identified; therefore, much research has been conducted to check whether vitamin D affects arthroplasty , 26, 27.Vitamin D deficiency affecting the functional score after TKA has been reported in previous studies , 18, 20.There is still some debate as to what level of vitamin D is regarded as sufficient. There are various reports on the criteria for dividing vitamin D insufficiency and deficiency. Hwang et al.  have sugIt has been reported that vitamin D-deficient patients showed similar functional score to the vitamin D-sufficient patient group after a total joint replacement with vitamin D supplementation , 22. KelThere are several limitations of this study. First, there is a large difference in the sample size between the two groups because the prevalence of vitamin D deficiency is higher in the Korean population than in other populations. Further prospective studies in age-matched populations are necessary. Second, vitamin D is also highly prevalent related to parathyroid hormone, binding protein level, and seasonal effect, and is also related to the timing and amount of blood sampling before surgery, so studies to control these variables are needed.Vitamin D deficiency was highly prevalent in patients who underwent TKA. Vitamin D levels negatively correlated with weight. Low vitamin D level was not a risk factor for unsatisfactory TKA outcome in elderly women.


Management of severe malaria remains a critical global challenge. In this study, using a multiplexed quantitative proteomics pipeline we systematically investigated the plasma proteome alterations in non-severe and severe malaria patients. We identified a few parasite proteins in severe malaria patients, which could be promising from a diagnostic perspective. Further, from host proteome analysis we observed substantial modulations in many crucial physiological pathways, including lipid metabolism, cytokine signaling, complement, and coagulation cascades in severe malaria. We propose that severe manifestations of malaria are possibly underpinned by modulations of the host physiology and defense machinery, which is evidently reflected in the plasma proteome alterations. Importantly, we identified multiple blood markers that can effectively define different complications of severe falciparum malaria, including cerebral syndromes and severe anemia. The ability of our identified blood markers to distinguish different severe complications of malaria may aid in developing new clinical tests for monitoring malaria severity. Kumar et al. use proteomes of patients with falciparum and vivax malaria and dengue fever, and identify differentially abundant proteins and their associated functions in the different severities and diseases. This study offers mechanistic pathways to malaria severity, and adopts a machine learning approach for predictive proteins for the malarial types and severities. Plasmodium genus1, and the vector involved is female Anopheles mosquito. It is the most widespread tropical parasitic disease with a worldwide occurrence of 228 million clinical cases and 0.4 million deaths in 2018 (ref. 2). India majorly contributes to the global malaria burden and has the largest population in the world at risk of malaria, with 85% of the total Indian population living in malarious zones3. Worrisomely, the subsidiary burdens of malaria, such as malnutrition and anemia, increase the risk of complications and severity of the disease5. Among the five parasites causing malaria in humans, Plasmodium falciparum and Plasmodium vivax have the most extensive global distributions and are capable of leading severe fatal clinical manifestations6. Particularly, P. falciparum infections often turn severe and life-threatening, specifically when managed inappropriately8. One of the prime causes behind the progression of this parasitic infection from mild through complicated to severe disease is missed or delayed diagnosis10. Of note, neither parasite density nor parasitemia can consistently define malaria severity11. Hyper-parasitemia does not necessarily provide primary prognostic significance in semi-immune individuals, as they often tolerate high parasitemia burden without any physiological signs of disease or severe effects12.Malaria is a vector-borne infectious disease caused by the protozoan parasites of the 16. Additionally, the RDT-based detection approaches target PfHRPII for P. falciparum only, pLDH for all Plasmodium species, and aldolase for P. falciparum and P. vivax. Importantly, many population-based studies have shown partial or complete deletion of pfhrpII and pfhrpIII genes, which may lead to false-negative results in RDTs19. Overall, these issues amply highlight the need for identification of additional parasite proteins and host factors for better diagnosis of malaria. Moreover, the establishment of predictive blood biomarkers for malaria severity and complications would be highly promising for prognosis, monitoring disease progression and responses to therapy, and predicting outcomes.There are several limitations for the existing diagnostic methods for malaria, which include microscopic examination of thick and thin blood smears, polymerase chain reaction (PCR)-based molecular diagnostics, and rapid diagnostic tests (RDTs)18. Intriguingly, several studies from our and other research groups have defined promising panels of plasma and serum markers in falciparum24 and vivax26 malaria. A considerable amount of further research is required to understand the complex pathophysiology of severe malaria29. In particular, the blood markers that can effectively define different complications of severe malaria are not clearly demarcated hitherto. Indeed, the precise mechanisms that cause a transition from non-severe to severe fatal clinical manifestations in malaria, which often happens very rapidly, remains largely obscure. These are the critical questions that remained to be addressed to understand the molecular basis of severe malaria.In recent years, plasma/serum proteomics studies have contributed substantially to elucidate the complex pathogenesis of malaria and other infectious diseasesIn this study, we performed comprehensive proteomics analysis of plasma samples from falciparum malaria patients with different severity levels and clinical manifestations to understand the mechanisms of malaria severity and its complications. We further carried out a comparative analysis with differentially abundant plasma proteins identified in vivax malaria (VM) and dengue patients to specify the alterations observed in falciparum malaria. Importantly, we identified several blood-based host proteins marker for malaria severity and complexity, which can aid in monitoring disease progression. Further, we have also identified parasite proteins such as Serine Repeat Antigen 4 and Fructose-Bisphosphate Aldolase in severe malaria patients’ plasma samples, which can help in the diagnosis of severe malaria. To the best of our knowledge, this is one of the most comprehensive blood-based proteomic studies on falciparum and VM. Our findings provided insights regarding malaria pathogenesis and molecular cues of severity and complicated disease manifestations associated with this parasitic infection.p < 0.05) (in both NSFM and SFM) as compared to HC. The magnitude of alterations (decrease) in platelet counts and hemoglobin (Hb) levels were found to be more prominent in severe malaria. Liver function parameters including total bilirubin, serum glutamic-oxaloacetic transaminase (SGOT), serum glutamic-pyruvic transaminase (SGPT), and alkaline phosphatase (ALP) were found to be higher in SFM patients as compared to NSFM and HC  in SA as compared to CB and other different types of complications of SFM, while the other parameters were almost comparable. These liver function parameters were slightly higher in non-severe malaria as compared to HC, but the level of alteration was minimal , Non-severe falciparum malaria (NSFM), Severe falciparum malaria (SFM), Cerebral malaria (CB), Severe anemia (SA), Control for severe anemia (CSA), Control for cerebral malaria (CCB), Non-severe dengue fever (NSD), Severe dengue (SD), Non-severe vivax malaria (NSVM), and Severe vivax malaria (SVM). The subjects were recruited from different malaria epidemic regions in India. The number of samples analyzed for each group is provided in Supplementary Data 32. TMT-based multiplexed quantitative proteomics was used to map the plasma proteome (host) alterations, while we used a label-free quantitation (LFQ) approach for detection and quantification of the parasite  proteins in host plasma. Quality control check (QC) of the datasets was performed by plotting the density plots for FM, VM, and DF using raw and normalized abundances at a proteome scale  altered proteins were considered for machine learning, and the elastic net regularized logistic regression method was applied to predict the best panel of proteins. Some selected targets were validated using mass spectrometry (MS)-based multiple reaction monitoring (MRM) assays. Eventually, we investigated the physiological pathways over-expressed in falciparum and VM  altered in SFM as compared to NSFM, and their abundance profiles enabled us to differentiate SFM and NSFM , and it distinguished between the three study populations: HC, NSFM, and SFM  and 11,530 peptides were identified corresponding to 1495 proteins identified combining all the samples, among which 296 proteins were present in more than 60% of the clinical samples used for the label-based FM study Fig. . The proSFM Fig. . SignifiThe platelet degranulation process was found to be highly active in FM. Plasminogen (PLG), Platelet factor 4 (PF4), Profilin-1 (PFN1), Kininogen-1 (KNG1), and Pro-platelet basic protein (PPBP) were found to be down-regulated in NSFM as compared to SFM, while Alpha1-antitrypsin (SERPINA1) and Alpha2-antiplasmin (SERPINF2) were dysregulated in SFM as compared to NSFM Fig. . Proteinn = 6) and cerebral malaria  exhibited 28 and 35 differentially abundant proteins (p < 0.05), respectively, in comparison to HC and 17 proteins in SA as compared to CB  can effectively distinguish CB or SA as compared to HC  as compared to NSFM. Most of these proteins were found to be down-regulated in CB. Out of these 12 altered proteins, 3 candidates—HGFA33, SERPINA3, and ALX1 protein—were found to be up-regulated, while the other 9 proteins were down-regulated as compared to NSFM. Five proteins were significantly altered, specifically in CB as compared to NSFM. Of note, these proteins were unaltered in meningitis patients   in NSVM and SVM as comparison to HC and 45 differentially abundant proteins (p < 0.05) in SVM as compared to NSVM, respectively  in NSVM, SVM, and DF also showed three distinct clusters  using an LFQ approach.  for sporozoites gliding and invasion36. SERA4, along with the other SERA member proteins, helps in maintaining the blood stage of the pathogen’s life cycle. However, their clear physiological functions still remain unknown37, and need to be investigated further.HSP 70 and HSP 90 were reported as up-regulated at temperature 38 °C and above, which helps the survival of the parasite in the erythrocytic stage of its life cycle in the hyperthermic condition of the hostp < 0.05) plasma proteins identified in malaria and dengue. Of note, we are able to separate VM, FM, dengue, and HC samples based on the abundance profiles of the altered plasma proteins  for the three models were as follows: (i) dengue vs. malaria: 0.1, 0.26, 0.975, 0.967, and 0.96; (ii) FM vs. VM: 0, 80.2, 1, 1, and 1; and (iii) CB vs. SA: 0.04, 41.73, 1, 1, and 1, respectively. It is evident from the model performance metrics that the resulting elastic net regularized logistic regression model can predict and classify dengue vs. malaria, FM vs. VM, and cerebral vs. severe malaria anemia cases almost perfectly. These results are quite fairly stable across all (outer) k-fold iterations  and performance metrics  across malaria and Dengue effectively differentiated between these two infections  from dengue patients. The three-dimensional PLS-DA plot for all altered proteins and heat map profiles of the top 20 significantly altered proteins , FM (n = 30 × 3), VM (n = 30 × 3), and DF (n = 27 × 3) along with 15 synthetic heavy peptides. The measurements obtained in MRM correlated well with the TMT-based discovery-phase proteomics  proteins and parasite proteins in plasma samples using MRM-based mass spectrometric assays. We have considered 7048 transitions corresponding to 86 human plasma proteins (403 unique peptides), which were identified as the best classifier in our machine learning analysis for the optimization of MRM assays. Initially, we started with 40 different method files having a maximum of ~200 transitions, then we refined our methods and finalized 847 transition corresponding to 128 peptides and 46 proteins Fig. . MRM assSimilarly, we validated the detection and quantification of the parasite proteins in human plasma samples. Ninety-five transitions corresponding to eight parasite proteins were optimized, and we were able to quantify those in the plasma samples using MRM assays . The variants of PfEMP1 are involved in cytoadherence and mediate binding of the infected erythrocytes to endothelial vasculature6. Some of the parasite proteins that were identified in the plasma samples of SFM patients, such as FBA, HSP 70, HSP 90, enolase, and SRA4 can generate antibody responses, as we have shown previously in VM38. Similarly, Moussa et al.24 reported the presence of FBA, SRA protein, and histone H3 in plasma samples of children with CB. Most of these proteins play a very important role in catalytic activity and protein binding  possibly due to alterations in the levels of proteins such as PF4, PPBP, PFN1, KNG1, CLU, and PLG in NSFM , a platelet-secreted chemokine, and platelet activation marker that takes part in the process of clearing the parasites by inducing macrophage chemotaxis and mediating neutrophil accumulation44 . This leads to the activation of endothelium, disruption of blood flow, and ultimately causes tissue hypoxia. These events lead to an increase of endothelial receptors, and therefore shedding of soluble endothelial receptors, eventually leading to endothelial damage57. We anticipate that this could be one of the possible mechanisms associated with the progression of NSFM to CB .We observed up-regulation of the proteins related to intrinsic pathways such as vWF, F10, and F2 in SFM. Infected RBCs induce the expression of tissue factors on endothelial cells and monocytes, which results in the expression of cytokines via signaling pathways, ultimately leading to endothelial cell activation. vWF is a secondary marker for endothelial cell activationity Fig. 47. Impor CB Fig. . Some of60. We observed increased levels of C3 which is an active component of the complement system. Complement C5, C6, and C7 form a complex c5c6c7 (ref. 61), and bind to the cell membrane and membrane attack complex with the help of C8 and C9 (refs. 63). In our study, we observed all of these three proteins forming the complex  were down-regulated while the free monomers (C8 and C9) were up-regulated in NSFM  in circulation65. We observed a significant elevation in blood levels of Hb subunit alpha and beta in FM patients, as also described previously by Kassa et al.23. The free Hb is oxidized by free radicals, thereby releasing free heme. Of note, free heme is very toxic to the cells and induces inflammation, macrophage activation, and oxidative stress. However, the down-regulation of these proteins  indicate their involvement in scavenging of free heme and ROS in FM. RBC lysis and highly activated complement system may have direct effects on the destruction of erythrocytes and cytokines indirectly affect the inhibition of erythropoiesis. This clearly indicates an increased removal of the infected and uninfected erythrocytes that plays an essential role in severe anemia during malaria  and reduce the process of erythropoiesisParasite proteins that were consistently detected in almost all the severe malaria patients could be promising for developing different diagnostic approaches. Of note, those could be an indicator of severe infection as they are specifically detected in severe malaria patients. However, it remains to be seen whether these blood-based protein markers for malaria severity and complexity identified in our study are validated in bigger heterogeneous clinical cohorts. One potential caveat to this study is the possibility for asymptomatic infection in the control cohorts, although these were entirely devoid of any clinical symptoms of malaria. Moreover, genetic analysis of human plasma samples for glucose 6 phosphate dehydrogenase (G6PD) deficiency and hemoglobinopathies was outside the scope of this study. This leaves a lack of information on certain key host responses during the regulation of host proteins in the response of malaria. These could be an effective future continuation of the present investigation, especially as the findings continue to move towards translational research. Collectively, our findings provided some novel mechanistic insights into malaria severity, and we anticipate that this will accelerate the opportunities for developing clinical tests integrating these host and parasite proteins for monitoring malaria severity and complexity.This study was conducted involving malaria and DF patients, and control subjects from two different malaria-endemic regions of India. Subjects were enrolled in Calcutta medical college (CMC), Kolkata, India, and Sardar Patel medical college (SPMC), Bikaner, India. More precisely, this comprehensive proteomics study was accomplished involving HC subjects, and patients suffering from NSFM, SFM, and different complications  and CB and their corresponding control subjects, anemic [CSA], and meningitis patients [CCB], NSD; SD, NSVM, and SVM. This study was approved by the institutional review boards and ethics committee of the Indian Institute of Technology Bombay (IITB-IEC/2016/026). Prior to the sample collection process, written informed consent was received from each participant after giving detailed explanations about the experimental procedure in the language best understood by the potential participants.66. The brief workflow is depicted in Supplementary Materials and Methods, and schematic representation is provided in Supplementary Fig. P. falciparum and P. vivax) were excluded from this study. The case definitions of severe malaria67 were adopted from standard WHO guidelines  NSD and (2) SD based on clinical features  and hematological parameters . In our study, IgM was used as the confirmatory marker for dengue infection. If NS1 was found to be positive, we retested for IgM during the 5–7 days of fever to confirm the diagnosis.According to the recent WHO guidelineg for 10 min, and plasma samples were stored at −80 °C until further processing69.Blood samples were collected into ethylenediaminetetraacetic acid (EDTA), BD vacutainer, tube, and gently mixed by inverting 8–10 times. EDTA-anticoagulated blood (3 ml) was centrifuged at 1000Blood samples of malaria-infected patients were collected from the different endemic regions of India. The collected samples were microscopy and RDT-positive samples. Further confirmation of the malaria parasite, nested PCR was performed using dried blood spots of the obtained samples. Only PCR-positive singly infected patient samples were taken forward for multi-omic study. For nested PCR from dried blood spots, Whatman filter paper 1 was taken, and 20 µl of RBC pellet was spotted on the Whatman paper. For each sample, 3–5 spots were made as per the availability of the RBC pellet. Two dried spots were punched in a 1.5 ml eppendorf tube. Added 200 µl of 0.5% freshly prepared saponin in PBS (or up to the volume required to submerge the punched spots). A short spin was given to settle down the droplets on the brim. Samples were incubated for 10 min at RT. Six hundred microliters of PBS was added to the blood spots for wash and supernatant was discarded. Washing was done for 2–3 times, and then the PCR reaction was set.TM Top 12 abundant depletion column following the manufacturer’s instructions . Briefly, 15 µl of crude plasma was directly added to the resin slurry in the columns, and the mixture was incubated for 60 min at room temperature with gentle vortexing in between. The filtrate was collected in 2 ml Eppendorf tubes by centrifuging at 1000g for 2 min. The depleted plasma samples were concentrated using a vacuum centrifuge.Removal of high abundant proteins is crucial for the detection of the low-abundant proteins, many of which frequently act as potential biomarkers. Therefore, before processing the plasma samples for proteomic analyses, the top 12 highly abundant proteins  were removed by using PierceProtein extracted from the plasma samples of HCs and infected patients were quantified using Bradford Assay kit (Bio-Rad) following the manufacturer’s instructions. The depleted plasma samples were loaded onto one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels (12.5% acrylamide-bisacrylamide) for the quality check control  following the manufacturer’s instructions. One hundred micrograms of depleted protein sample from each group  were denatured using 15 µl of 6 M urea. The denatured proteins were reduced by adding tris (2-carboxyethyl) phosphine (TCEP) (Sigma Aldrich) to a final concentration of 20 mM and were incubated at 37 °C for 60 min. The reduced samples were alkylated by adding iodoacetamide  to a final concentration of 40 mM and were incubated at room temperature in dark for 30 min. The samples were then diluted eight times with 50 mM ammonium bicarbonate buffer, and trypsin (Pierce Trypsin) was added to a 1:30 ratio (trypsin: protein) for performing in-solution digestion. Samples were incubated at 37 °C for 18 h for efficient digestion. The digestion was quenched by adding formic acid to a final concentration of 1%TMT six-plex isobaric tags  were used for FM and Dengue (FC) samples and TMT 10-plex isobaric tags  were used for VM samples for labeling of the digested peptides as per the manufacturer’s instructions. In brief, digested peptides were reconstituted in dissolution buffer and were vortexed to mix well. A pooled sample was prepared from each individual patient sample and was considered as a reference for performing normalization of the different mass spectrometric datasets. TMT reagents were reconstituted in 40 µl of anhydrous acetonitrile (ACN) and the reagents were added to the corresponding aliquot of digested plasma protein sample following the labeling strategy  set at 5e (ref. 4) was followed by up to 20 ms/ms scans at a resolution of 30,000 with AGC set at 4e (ref. 5). MS/MS spectra were collected with a normalized collision energy of 35% and an isolation width of 1.2m/z. Dynamic exclusion was set to 40 s, and the peptide match was set to on. Surveys scans were performed in the Orbitrap mass analyzer and data-dependent MS2 scans were performed in Orbitrap mass analyzer trap using higher-energy collisional dissociation (HCD) following isolation with the instrument’s quadrupole. The intensity threshold of the peptide was set 5e (ref. 3). Internal calibration was carried out using a lock mass option (m/z 445.1200025) from ambient air. The same parameters were used for LFQ (n = 21), except collision energy, which was set 30%.Multiplexed TMT-labeled samples (6/10-plex) were analyzed as biological replicates using an Orbitrap Fusion Tribrid mass spectrometer interfaced with an Easy-nLC 1200 system (Thermo Fisher Scientific). The mobile phase consisted of milli-Q water with 0.1% formic acid as solvent A and 0.1% formic acid/80% acetonitrile as solvent B. Each fraction was reconstituted in 15 µl of solvent A and 1 µg of digested peptides were loaded on to a pre-analytical column . Isocratic gradient of 10–35% B in 103 min, 35–95% B in 2 min and holds at 95% B for 15 min at 300 nl/min flow rate were used on an analytical column . A single Orbitrap MS scan from 375 to 1700Homo sapiens fasta  from Uniprot database . All searches were configured with dynamic modifications for the TMT reagents (+229.163 Da) on lysine and N-termini, oxidation of methionine residues (+15.9949 Da) and static modification as carbamidomethyl (+57.021 Da) on cysteines, monoisotopic masses, and trypsin cleavage (max 2 missed cleavages). The peptide precursor mass tolerance was 10 ppm, and MS/MS tolerance was 0.05 Da. The false discovery rate (FDR) for proteins and peptides was kept 1%71. TMT signals were corrected for isotope impurities based on the manufacturer’s instructions.TMT 6-plex- and TMT 10-plex-based quantitative proteomic analysis were carried out using individual samples of malaria and dengue patients. Raw instrument files were processed using Proteome Discoverer (PD) version 2.2 (Thermo Fisher Scientific). In each TMT experiment, .raw files for all fractions were merged and MS2 spectra were searched using the Sequest HT and Mascot (v2.6.0) search engine against Plasmodium falciparum 3D7 (Plasmo TaxID=36329_and_subtaxonomies) (v2017-08-25). The parasite data were downloaded from PlasmoDB (https://plasmodb.org/plasmo/) on 28/06/2018. The search parameters were kept the same as above mentioned for the TMT 6-plex method except for dynamic modifications for the TMT reagents (+229.163 Da) on lysine and N-termini of the peptide.In LFQ, the .raw files from the label-free method were searched against the database of 73, where run-to-run normalization using a reference pool and quantile normalization were performed. Sixty-six proteins were selected based on adjusted p value (p < 0.05) in malaria and p value (p < 0.05) in DF  by driving coefficients of unimportant covariates to zero and (ii) l2 norm helps address the issue of multicollinearity82. k-fold nested cross-validation  was used as it provides robust and almost unbiased parameter estimates and model performance evaluation even for small sample sizes84.The elastic net regularized logistic regression method was used to classify: (1) dengue vs. malaria: HC vs. Dengue, HC vs. malaria, dengue vs. malaria; (2) type of malaria: HC vs. FM, HC vs. VM, FM vs. VM; (3) severity of FM: HC vs. NSFM, HC vs. SFM, NSFM vs. SFM; (4) severity of VM: HC vs. NSVM, HC vs. SVM, NSVM vs. SVM; (5) severity of dengue: HC vs. NSD, HC vs. SD, NSD vs. SD. Of note, the elastic net regularized logistic regression model offers more flexibility in two ways—(i) the lk-fold inner cross-validation loop for hyperparameter tuning and model selection and (ii) k-fold outer cross-validation loop for evaluating the model selected by the inner cross-validation. The entire dataset was randomly split into k-folds, out of which the kth fold was used as a test set and the remaining k−1 folds were used for training purpose. For each split, a model was trained and validated on the training set using (inner) k-fold cross-validation and tested on the held-out test set. The hyperparameters alpha and lambda were tuned by inner k-fold cross-validation over a grid of values ranging from 0 to 1 with a step-size of 0.1 for alpha and another grid of 100 values ranging from 10−2 to 102 for lambda, respectively. To evaluate model performance, ROC–AUC, balanced accuracy, F1-score, and Kappa metrics were computed. These metrics were chosen because we had imbalanced classes. The average variable importance of a protein was measured as the weighted average of variable importance of that protein in all k predictive models in the outer k-fold cross-validation with weights being the balanced accuracy of each model. This implicates that the importance of a protein in the classification of samples is directly proportional to its occurrence in the predictive models, which can classify the samples better. A schematic diagram of the method used here is shown in Figs. The nested cross-validation approach involves using—(i) For validation of the selected proteins as good classifiers for dengue vs. malaria, FM vs. VM, and CB vs. SA models, we created (i) heat map and (ii) three-dimensional PLS-DA plot Fig. . To arriThe elastic net regularized logistic regression model equations for the final biomarker models identified from MRM were as follows:dengue vs. malaria:Falciparum vs. vivax malaria:pPositive Class is the probablility of event that Y=Postitive Class. For these three models, the hyperparameters  and model performance evaluation metrics  were as follows: (i) dengue vs. malaria: 0.22, 0.02, 0.72, 0.89, 0.46, 0.807 (0.666–0.947); (ii) FM vs. VM: 0.25, 4.47, 0.8, 0.77, 0.6, 0.865 (0.772–0.958); and (iii) CB vs. SA: 0.02, 41.10, 0.8, 0.83, 0.56, 0.944 (0.816–1), respectively. These models serve two purposes, and they are as follows:Cerebral vs. severe malaria anemia:They help assess the impact of a particular protein on the estimated log odds or the estimated odds of an individual, for example, being a dengue patient as opposed to being a malaria patient.For illustration sake, let us consider the model Eq. . Note thThis can be interpreted by observing that the estimated odds of an individual being a dengue patient as opposed to s(he) being a malaria patient increase multiplicatively (decrease multiplicatively) by 9.2999 (0.176) for every unit increase in the value of the protein LRG1 (CP).Note that the equation involving the estimated odds The other two logistic regression models corresponding to falciparum vs. vivax malaria or cerebral vs. severe malaria anemia could be interpreted analogously.85, and Reactome pathway Knowledgebase, version 62 (www.reactome.org)86. A multi-functional online software NetworkAnalyst  was applied for constructing and visualizing the PPI networks. The batch selection option of NetworkAnalyst was used to narrow down the network nodes, and the clusters were specified by highlighting with different colors.In order to investigate the complex interactions among the candidate marker proteins and for prediction of the pathways associated with the differentially altered proteins identified in FM, VM, and DF patients, diverse bioinformatic analyses were carried out. The pathway enrichment was performed using Protein Analysis THrough Evolutionary Relationships (PANTHER) classification system, version 12.0 (www. pantherdb.org)n = 17) of each heavy synthetic peptide was spiked into 10 µg of the digested samples, with 142 peptides having 901 transitions over a 20-min chromatographic gradient with a scheduling method. The total 40 methods were used to accommodate all the transitions (approx. 176 transition/method) for host and parasite proteins. During the first phase of optimization, six proteins were not detected and hence removed those from the list. The refinement of the transitions performed by running different plasma pool samples and some individual samples on different days to check the inter-day variation and stability of the peptides  equipped with an Easy-Spray electrospray ionization ion source (Thermo Fisher Scientific). Peptides separations were performed using a C18 column . The mobile phase consists of Milli-Q water with 0.1% formic acid as solvent A and 0.1% formic acid/80% acetonitrile as solvent B. Following chromatographic conditions were used: 20-min gradient at a flow rate of 300 nl/min starting with 100% A (water), stepping up to 2% B (ACN) in 0 min, followed by 45% B at 16 min, followed by a steep increase to 95% B at 17 min and static for 1 min at 95%. The steep decrease to equilibrate the column 5% of B at 19 and then static for another 1 min at 5% of B. The suitability of the system was evaluated using iRT peptide and digested BSA , 90 fractions; VM: n = 4 experimental sets (TMT 10-plex), 25 fractions, and DF: n = 4 experimental sets (TMT 6-plex), 24 fractions). The protein-level summarization and significance analysis of the proteins was performed in R using the MSstatsTMT package73. In brief, The PSMs were preprocessed in PD and were converted into the required input format for MSstatsTMT using an in-house R-code. The protein abundance was calculated based on the peptide quantification using the “protein summarization” script (MSstatsTMT). It includes normalization between the MS runs using reference pool channels and the imputation of missing values before summarizing peptide level data into protein-level data. In the protein summarization method, MSstats assumes missing values are censored and then imputes the missing values using the accelerated failure model. Missing value imputation was performed for only of those proteins, which were quantified in ≥60% of the samples. Then, a moderated t-test was performed on quantile normalized values using “Group-Comparison-TMT” script in R and p value <0.05 were considered as statistically significant73. Proteins passing p value <0.05 threshold of moderated t-test was used for data visualization and pathway analysis. However, adjusted p value Benjamini–Hochberg (BH) was also calculated for multiple comparisons using the MSstatsTMT package in R. BH-corrected p value [p value (adjusted) <0.05] was considered in the selection of proteins for machine learning analysis (except for dengue).The proteins that were quantified with ≥1 unique peptide and detected in at least 60% of the samples were selected for the subsequent differential analysis. The PSMs values were exported from Proteome Discoverer 2.2 (PD) for combined analysis for FM, VM, and DF (FM: Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


While aminoglycosides (AG) have been used for decades, debate remains on their optimal dosing strategy. We investigated the international practices of AG usage specifically regarding dosing and therapeutic drug monitoring (TDM) in critically ill patients. We conducted a prospective, multicentre, observational, cohort study in 59 intensive-care units (ICUs) in 5 countries enrolling all ICU patients receiving AG therapy for septic shock.Cmax and Cmin was performed in 437 (47%) and 501 (57%) patients, respectively, after the first dose with 295 (68%) patients achieving a Cmax/MIC > 8 and 353 (71%) having concentrations above Cmin recommended thresholds. The ICU mortality rate was 27% with multivariable analysis showing no correlation between AG dosing or pharmacokinetic/pharmacodynamic target attainment and clinical outcomes.We enrolled 931 septic ICU patients  receiving AG as part of empirical  or directed  therapy. The AG used was amikacin in 614 (66%), gentamicin in 303 (33%), and tobramycin in 14 (1%) patients. The median (IQR) duration of therapy was 2 (1–3) days, the number of doses was 2 (1–2), the median dose was 25 ± 6, 6 ± 2, and 6 ± 2 mg/kg for amikacin, gentamicin, and tobramycin respectively, and the median dosing interval was 26 (23.5–43.5) h. TDM of Short courses of high AG doses are mainly used in ICU patients with septic shock, although wide variability in AG usage is reported. We could show no correlation between PK/PD target attainment and clinical outcome. Efforts to optimize the first AG dose remain necessary.Trial registration Clinical Trials, NCT02850029, registered on 29th July 2016, retrospectively registered, https://www.clinicaltrials.govThe online version contains supplementary material available at 10.1186/s13613-021-00834-4. Early administration of adequate doses of antibiotics is a key issue for the optimal management of sepsis in critically ill patients , 2. OptiThe secondary objectives were to report renal adverse events and clinical outcomes, and to assess the correlation between PK/PD target attainment and these outcomes .This study is reported in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines .A prospective, international, observational cohort design was used. All participating ICUs obtained local ethics committees’ approval [ethics approval for France  ] and theAll adult patients (≥ 18 years) requiring ICU care and receiving AG  therapy for a sepsis according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) were included in the study .Overall, 85 ICUs accepted to participate to the study, and 59 ICUs enrolled patients from May 2016 to August 2017 in five different countries: Australia (5), Greece (5), Chile (4), UK (9), and France (62).For each participating ICU, the following information were recorded: type of hospital, type of ICU, number of beds, number of physicians, number of admissions per year (on the last year), annual ICU mortality, national or local AG guidelines availability, and TDM availability.Data recorded for each patient included were: demographic data , clinical data , and biological data . Additionally, dosing and TDM data when available  and toxicity data  at the Cmax/MIC ratio.The definition used to assess PK/PD outcome was as follows. PK/PD target attainment was defined as AG concentration after the first dose ≥ 8 times the MIC where AG plasma concentrations were available. Breakpoints from the EUCAST database were considered . Where iData collection was performed by trained staff at each participating center. Data were entered into a structured electronic password-protected and secured web-based case report form (eCRF). The eCRF was developed using the open-source REDCap Data Management Platform hosted at the Nîmes University Hospital .Data monitoring was handled by the coordinating center . Outstanding queries regarding the completion of the CRF were undertaken with each participating center where necessary to ensure accuracy of data.th–75th percentile) for continuous variables. For categorical variables, frequencies and proportions were given. Two secondary outcomes were explored separately: ICU mortality and clinical success. We used multivariate regression models involving eight covariates: admission type , age, gender, body mass index , SOFA score on the day of AG initiation, creatinine plasma concentration (µmol L−1) on the day of AG initiation, bacteremia, and directed or empirical antibiotic treatment. A 9th variable related to AG treatment was considered. We constructed a model for each way we considered AG: the mg/kg dose using either the total body weight (TBW), the ideal body weight , or the adjusted body weight [ABW: given by the TBW when the BMI was below 30 kg m−2 and when the BMI was over 30 kg m−2 calculated according to the following formula: IBW + 0.4 x (TBW-IBW)], the peak concentration (mg L−1), the attainment of a peak concentration between 60 and 80 mg L−1 for amikacin, and between 20 and 30 mg L−1 for gentamicin. Because practices could differ between hospitals, we needed a model accounting for correlation within centers. For this purpose, we chose a GEE  model with an exchangeable correlation structure. This method allows parameters of generalized linear model to be estimated in clustered data and returns a marginal model that fits in with our purpose   mg L−1. Among these 128 patients, 76 had both MIC and Cmax available, with a median Cmax/MIC ratio of 25.7  and for 65 patients Cmax/MIC ratio was above 8.Among the 931 included patients, 14 (1%) received tobramycin, 303 (33%) received gentamicin, and 614 (66%) received amikacin. Practices of AG administration are described in Table se Figs. . After tAntimicrobial therapy was considered successful in 621/843 (74%) patients. Relapse and superinfection were observed in 42/843 (5%) and 71/843 (8%) patients, respectively, whereas in 17 patients, emergence of multidrug resistant (MDR) pathogens was noted.Concomitantly to AG, 361 (39%) patients received at least one nephrotoxic agent among non-steroid anti-inflammatory drug, iodine, angiotensin-converting enzyme, and vancomycin. On the last day of therapy, AKIN score was The ICU mortality rates were 27%, respectively. The median ICU and hospital LOS were 11 (IQR 5–25) and 24 (IQR 11–43) days, respectively.Cmax attainment was not independently associated with clinical success and ICU mortality. The different other models tested did not find a significant difference in the association between clinical outcomes and the variables of interest related to AG.Independent factors associated with ICU mortality and clinical success are presented in Tables Cmax target attainment and ICU mortality or clinical success  of gentamicin has been observed in sepsis leading to gentamicin underexposure and increasing risk of therapeutic failure . On. OnCmax/patients . As intend costs . Howevernd costs , 42. They for AG .Several limitations of the AMINO III study needs to be acknowledged.First, the distribution of the participating ICUs may not reflect the daily practice of all ICUs for each country as in some countries a small number of patients were included. Second, PK/PD outcome assessment was possible in less than half of the included patients. As a result, correlations with patient and site characteristics factors may have been underestimated. However, these findings highlight that, despite national or local recommendations, aminoglycoside TDM is not systematically performed in septic patients. Third, we only considered AG use in septic patients in the present study precluding to extrapolate these findings to other indications of AG therapy. Fourth, assessment of clinical success was not assessed by an independent committee; however, it was clearly predefined and standardized for improving data quality. Finally, ototoxicity was not assessed due to the observational design of the present study.Cmax target attainment and ICU mortality or clinical success. Efforts to optimize the first AG dose and to perform TDM are still needed.Short courses of high AG doses are mainly used in ICU patients with septic shock, although wide variability in AG usage is reported. In this cohort, we could show no correlation between Additional file 1: Figure S1. Representation of AMINO III study participating sites. Dark gray: study participating countriesAdditional file 2: Table S1.


Lycium barbarum L. (Solanaceae) polysaccharides (LBPs) are important active constituents that have demonstrated kidney protection. This study investigated the effect of LBPs on hyperuricaemia and explored the underlying mechanism in mice.UA) and urinary uric acid (UUA), as well as xanthine oxidase (XOD) activities. mRNA and protein were quantified by quantitative real-time PCR and Western blotting, respectively. Thirty-six C57BL/6 mice were randomly divided into the control group, hyperuricaemia group, allopurinol group (5 mg/kg) and three LBP groups (n = 6). The LBP groups were treated orally with LBPs at 50, 100 and 200 mg/kg body weight for 7 days. We examined the levels of serum uric acid , respectively, while significantly increased the mRNA and protein expression levels of renal organic anti-transporter 1 (OAT1) and organic anti-transporter 3 (OAT3), and markedly decreased the levels of glucose transporter 9 (GLUT9) (p < 0.05). Additionally, the serum XOD activities were reduced to 31.5 and 31.1 mU/mL, and the liver XOD activities were reduced to 80.6 and 75.6 mU/mL after treatment with 100 and 200 mg/kg LBPs (p < 0.01), respectively. LBPs treatment (100 and 200 mg/kg) significantly reduced the S This study demonstrated the potential role of LBPs in reducing the uric acid level in hyperuricemic mice. A border study population should be evaluated. These results are valuable for the development of new anti-hyperuricaemia agents from LBPs. Hyperuricaemia, characterized by high blood uric acid levels, is a metabolic disease. Under the condition of chronic hyperuricaemia, uric acid is crystallized and deposited as monosodium urate in the joint, being closely correlated with gout. Xanthine oxidase (XOD), the key enzyme in purine metabolism in the liver, is responsible for catalysing purine to uric acid , a traditional Chinese medicine, has the effects of nourishing the liver and kidney and replenishing vital essence to improve eyesight. Lycium barbarum polysaccharides (LBPs) are the main effective ingredient of Lycium barbarum, comprised of glucose, arabinose, galactose, mannose, xylose, rhamnose and fucose , blood urine nitrogen (BUN) and xanthine oxidase activity assay kit were purchased from Abcam Inc. . The RNA isolation kit was obtained from Promega Biotechnology Co., Ltd. . PrimeScriptTM RT reagent kit with gDNA Eraser and TB Green® Premix Ex Taq™ II (Tli RNaseH Plus) was purchased from Takara Biomedical Technology Co., Ltd. . Protease and phosphatase inhibitors were purchased from Thermo Fisher Scientific Inc. . Anti-OAT1, anti-OAT3 and β-actin antibody were purchased from Santa Cruz Biotechnology, Inc. . Anti-GLUT9 was purchased from Abcam Inc. . The Amersham ECL System was purchased from GE Healthcare . Other biochemical reagents and chemicals were of analytical grade.LBPs (≥90%) were purchased from Beijing Solarbio Science & Technology Co., Ltd. . Potassium oxonate (97%) and allopurinol (≥99%) was purchased from Sigma-Aldrich Co. LLC . Uric acid (UA), serum creatinine , weighing 20–25 g, were housed at room temperature (23 ± 1 °C) under standard conditions: humidity of 50 ± 5%, allowing free access to water and food under 12 h light/dark cycle (light at 8:00 am). The study was approved by the Ethics Committee of Central Hospital of Fushun for Animal Experiments and was performed according to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health .Thirty-six mice were randomly divided into the control group, hyperuricaemia group, allopurinol (5 mg/kg) group and LBP groups  (n = 6). The concentrations of LBPs were determined according to the literature , SCR, BUN and urinary uric acid (UUA) according to the manufacturer’s instructions using appropriate kits.The collected serum and urine samples of the mice were used to detect the levels of serum uric acid . Each assay was performed in duplicate. XOD activity (mU/mL) was calculated using the formula provided by the manufacturer’s instructions.Briefly, the liver tissue of the mice was thoroughly homogenized in an ice bath by placing it in sodium phosphate buffer at pH 7.4. The homogenate was centrifuged at 4000 TM RT reagent kit with gDNA Eraser and TB Green® Premix Ex Taq™ II (Tli RNaseH Plus) was performed according to our previous report .Protein extracts were obtained following the collection of kidney tissue in RIPA lysis buffer supplemented with protease and phosphatase inhibitors. Western blotting was performed as previously described . The data were expressed as the means ± standard deviation (SD). Statistical comparisons between the two groups were performed by Student’s UA level was significantly increased in the hyperuricaemia group compared with that in the control group  and correlated with the concentration of LBPs, although no significant difference was found between the 50 mg/kg LBP group and hyperuricaemia group, indicating that LBPs reduce the SUA level in the hyperuricemic mice in a dose-dependent manner , suggesting that LBPs affect the renal excretion of uric acid. What’s more, the acute toxicity study showed no significant changes in body weight , no significant difference was found between the two groups above in the protein expression of OAT1 and GLUT9. However, the protein levels of OAT1 and OAT3 were significantly increased, and that of GLUT9 was decreased, in the LBP groups compared with those in the hyperuricaemia group  and superoxide anion (O2−), are produced during the metabolism of purine (Berry and Hare Allopurinol is another clinical treatment for gout, mainly by inhibiting XOD activity, thereby reducing liver uric acid production. However, studies have reported that allopurinol may cause serious side effects such as rash, liver toxicity or gastrointestinal toxicity (Umamaheswari et al. Our study demonstrated that LBPs exert an anti-hyperuricaemia effect by increasing the expression of renal OAT1 and OAT3, decreasing the expression of renal GLUT9, and inhibiting XOD activity. The results obtained under our experimental conditions are valuable for the development of new anti-hyperuricaemia agents. Therefore, the anti-hyperuricaemia effect of LBPs on other animals should be further investigated.


Arabidopsis thaliana across the Iberian Peninsula, the region with the largest genomic diversity. To that end, we sampled, genotyped with genome-wide SNPs, and analyzed 1772 individuals from 278 populations distributed across the Iberian Peninsula.Disentangling the drivers of genetic differentiation is one of the cornerstones in evolution. This is because genetic diversity, and the way in which it is partitioned within and among populations across space, is an important asset for the ability of populations to adapt and persist in changing environments. We tested three major hypotheses accounting for genetic differentiation—isolation-by-distance (IBD), isolation-by-environment (IBE) and isolation-by-resistance (IBR)—in the annual plant A. thaliana. In other words, dispersal limitation, genetic drift, and to a lesser extent local adaptation to environmental gradients, accounted for the within- and among-population distribution of genetic diversity. Analyses applied to the four Iberian genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region, showed similar results except for one cluster, in which IBR (a function of landscape heterogeneity) was the most important driver of genetic differentiation. Using spatial hierarchical Bayesian models, we found that precipitation seasonality and topsoil pH chiefly accounted for the geographic distribution of genetic diversity in Iberian A. thaliana.IBD, and to a lesser extent IBE, were the most important drivers of genetic differentiation in A. thaliana.Overall, the interplay between the influence of precipitation seasonality on genetic diversity and the effect of restricted dispersal and genetic drift on genetic differentiation emerges as the major forces underlying the evolutionary trajectory of Iberian  Genetic diversity is an important asset for the ability of populations to adapt and persist in changing environments –8. The sThe inherent spatial nature of genetic differentiation defines the theoretical and methodological framework of three models, which are not mutually exclusive, testing the major drivers of genetic differentiation: isolation-by-distance (IBD hereafter), isolation-by-environment (IBE hereafter) and isolation-by-resistance (IBR hereafter) models. In the classical IBD, genetic differentiation among populations exhibits a positive relationship with geographic distance –18. In tIn contrast, the IBE model deals with the effects of environmental differences on genetic differentiation. IBE posits that gene exchange is strongest among populations located in similar environments, which would be mediated by environmental heterogeneity, the extent of local adaptation and spatial variation in gene flow across space , 21, 22.Finally, the IBR model takes environmental heterogeneity across landscapes into account as a modulator of gene flow and its effects on genetic differentiation . The IBRArabidopsis thaliana across the Iberian Peninsula. This is the region of the species’ distribution harboring the largest genomic diversity [A. thaliana occurs in a wide array of natural environments practically across the whole region, spanning from seaside to sub-alpine locations [A. thaliana’s history in the Iberian Peninsula [A. thaliana. In fact, habitat suitability of relict populations has been associated to more stable vegetation dynamics since the Last Glacial Maximum and during the Holocene in the Iberian Peninsula [A. thaliana an appropriate study system to disentangle the drivers of genetic differentiation at a regional scale.Here, we tested these three hypotheses to identify the major drivers of genetic differentiation in the annual plant iversity –30. In aocations –33. Theseninsula , 31, 34,eninsula , 34 alsoeninsula . OverallA. thaliana populations collected over a decade. About six individuals per population, totaling 1772 individuals, were genotyped with genome-wide, putatively neutral SNPs to estimate genetic diversity, differentiation and structure. We hypothesized that IBD and IBE largely accounted for genetic differentiation for two reasons. First, IBD is a common result in A. thaliana genetic studies—including the Iberian Peninsula [A. thaliana shows significant adaptive variation in fitness-related traits across environmental gradients [A. thaliana populations. For the sake of completeness, we also examined the geographical distribution of genetic diversity in Iberian A. thaliana. To this end, we employed a spatial hierarchical Bayesian model to identify regional hot and cold spots of genetic diversity and their potential environmental predictors. Overall, we stress the importance of identifying the drivers of genetic differentiation among populations, but also of pinpointing the forces that determine the amount of genetic diversity within populations, to understand the evolutionary dynamics of any organism.Given the fact that a small sample size seriously reduces power and accuracy of spatial analyses , IBD, IBeninsula –33—due tradients , 37, 38.A. thaliana in the Iberian Peninsula. In this set of populations, the percentage of polymorphic loci (PL) ranged between 0 and 56.7% (mean ± SD = 24.4 ± 17.7%), the mean number of observed alleles per locus (na) between 0.95 and 1.56 (mean ± SD = 1.23 ± 0.18), and mean gene diversity (HS) between 0 and 0.224 (mean ± SD = 0.09 ± 0.07). HS exhibited a bimodal distribution clearly differentiated in two groups of populations. On the one hand, 66 populations had very low HS values .We genotyped 240 genome-wide SNPs in 1172 individuals collected from 278 populations  in the 1772 individuals, which showed an average proportion of allelic differences between haplotype pairs of 0.30 ± 0.05. We only found five non-redundant multilocus genotypes in different populations. In particular, two (separated by 0.6 km) and three populations (separated by 7.6–12.8 km) included the same non-redundant multilocus genotypes.Overall, we found 1613 non-redundant multilocus genotypes .To assess whether populations experienced major or sudden disturbances that could affect their genetic diversity and differentiation during the last decades, we retrieved temporal series of publicly available orthophotographs from all populations. The analyses of the habitats interpreted from digitalized orthophotographs indicated that land-used changes and major disturbances  over the time considered did not affect landscapes. In particular, the average change per habitat type between year intervals was concentrated around zero Fig. d, indicaHS in A. thaliana populations, we used a spatial hierarchical Bayesian modeling to explain HS as a function of environmental variables. The model considered simultaneously the populations with zero (N = 66 populations) and non-zero (N = 212 populations) HS values. The best fitting model indicated that the occurrence probability of populations with non-zero HS values (populations with genetic diversity) was concentrated towards the center and eastern areas of the Iberian Peninsula , precipitation of the warmest quarter (BIO18) and topsoil pH. The contributions of precipitation variables were negative for the occurrence of populations with no genetic diversity, as well as for the genetic diversity of populations with non-zero HS values  the species is very rare .The environmental predictors with more influence on the spatial distribution of ality BIO5, precipality BIO5, precipA. thaliana populations in different genetic clusters with the 1613 non-redundant multilocus genotypes using Bayesian , occurred scattered across the Iberian Peninsula  were homogeneous, with 118, 44, 35 and 33 belonging to genetic clusters NW-C1, NE-C2, relict-C3 and SW-C4, respectively  to evaluate simultaneously the effects of the geographic (IBD), environmental (IBE) and resistance (IBR) variation on the genetic differentiation among Iberian le Table a. In addle Table b. These We found that the single model including IBD and IBE was ranked as the top model for three genetic clusters  with no models differing less than 2 in AIC Table a. AlthouA. thaliana.Genetic differentiation is a dynamic process because any population is constantly under the effect of ecological, genetic and evolutionary forces modifying the amount of genetic diversity within and among populations. Disentangling such forces accounting for genetic differentiation, which underlies major evolutionary processes from local adaptation to speciation, has long promoted the development of a strong theoretical and methodological framework since practically the birth of the Modern Synthesis. Here, we addressed this question by testing three hypotheses  accounting for regional-scale genetic differentiation in Iberian A. thaliana was mostly accounted for by IBD, and to a lesser extent by IBE , indicated that genetic differentiation in IBE Fig. a. In otheninsula –33 and eeninsula , 40–48.  ability , 47–49.A. thaliana. We believe that the lack of IBR found across the Iberian Peninsula is likely due to the species’ cosmopolitan habit and a distribution without major discontinuities . Thus, most population pairs are connected by relatively high habitat suitability, diminishing resistance distances. The demographic history, characterized by the presence of the relict African lineage and other non-relict Eurasian lineages [A. thaliana.In contrast, IBR did not account for genetic differentiation at a regional scale Fig. a, suggeslineages , 35, 50,lineages , 51–55, We also explored the effect of IBD, IBE and IBR for each of the four genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region , 39, 56.A. thaliana. For example, previous studies showed that geographic variation in fitness-related traits in Iberian A. thaliana (flowering time and seed dormancy) strongly co-varied as a function of temperature and to a lesser extent of precipitation [A. thaliana adjusts its life cycle by advancing flowering time and increasing seed dormancy as the environment becomes warmer, drier and more seasonal [Finally, we also conducted NMLPE using principal component (PC) axes to pinpoint the major environmental predictors underlying IBE. We found that the three PCs depicting gradients of temperature and/or precipitation across the region made substantial contributions to IBE in NW-C1, NE-C2 and relict-C3 Iberian clusters Fig. b, probabpitation , 62, 63.seasonal , 55, 63.A. thaliana that are worth considering. For example, analyses of the genetic diversity in this large number of populations showed that 24% of them had no or very low genetic diversity  Heyhn. (Brassicaceae) across the entire Iberian Peninsula , The CORINE Land Cover 2000 , and The Soil Geographical Database from Eurasia v.4 .We sampled 278 natural populations of the annual plant Geographic distance among populations varied between 1 and 1059 km  and Sierra de Grazalema Natural Park . Based on the Royal Decree of the Spanish legislation , the genetic resources included in this study fall within the definition of “exclusively for taxonomic purposes” as they were used for scientific, educational or non-commercial purposes.We collected all seeds from wild populations. Recent historical changes in land use or large-scale perturbations may dramatically affect the amount of genetic diversity in plant populations. Such changes can be a misleading factor when attempting to evaluate the joint effects of IBD, IBE and IBR on genetic differentiation. This is because IBD, IBE and IBR do not contemplate perturbations producing sudden or erratic changes in genetic diversity that eventually may mask the patterns of interest. To make sure that historical factors were not an issue in this study, we estimated the temporal landscape dynamics of all study populations. We examined all aerial orthophotographs available for each population (spanning between 1945 and 2016), which were retrieved from different public regional administrations in Spain  with a tN = 2252 orthophotographs) with QGIS v.3.4 [To quantify temporal landscape dynamics, a circular area (500 m radius) around the GPS coordinate was divided into a regular grid of 80 squares (100 m side) for each aerial orthophotograph available per population. Based on vegetation and land use, each square was categorized as forests, scrublands, dehesa , bare soil, crops, urban, infrastructures, and water, when one of these categories occupied at least more than half of the square. Categories were assigned to each square by digitalizing manually all squares from all aerial orthophotographs available  through the CEGEN Genotyping Service (www.usc.es/cegen/). These genome-wide SNPs are frequent polymorphisms in Central Europe, the Iberian Peninsula, and worldwide collections, which altogether minimized ascertainment bias [A total of 1772 ent bias , 32, 34.A. thaliana population, we calculated the percentage of polymorphic loci (PL), the mean number of observed alleles per locus (na) and mean gene diversity (HS) using FSTAT v.2.9.3 [FST values according to Weir and Cockerham [For each  v.2.9.3 . In addi v.2.9.3  to estimockerham , which rA. thaliana populations across the Iberian Peninsula was examined using two different methods: the Bayesian clustering method implemented in STRUCTURE v.2.3.3 [P < 0.005; Wilcoxon tests for related samples) gave the final K number. This was supported by a high similarity among the ancestry membership matrices from different runs of the same K value (H′ = 0.99). We used CLUMPP v.1 [http://www.cesga.es/).The genetic structure of  v.2.3.3 , 84 and  v.2.3.3  availabl v.2.3.3 . In the UMPP v.1  to calcuFor the DAPC analysis, we assessed the number of clusters using the ‘find.cluster’ function in adegenet, which runs successive K-means clustering with an increasing number of clusters to determine the best supported number of genetic clusters using the Bayesian Information Criterion (BIC). The K value with the lowest BIC value represents the optimal number of clusters, although BIC values may keep decreasing after the true K value in case of genetic clines and hierarchical structure . TherefoA. thaliana’s distribution [We modeled the effects of environmental heterogeneity on the spatial distribution of genetic diversity by using a modified version of a spatial hierarchical Bayesian model recently used to model the effects of warming on the Iberian ribution . The aimA. thaliana, 66 of 278 populations had very low mean gene diversity (HS) values (HS < 0.009), whereas the rest of populations exhibited non-zero HS values  was modeled with a Bernoulli distribution, whilst the continuous component (presence of genetic diversity) was modeled with a Beta distribution (see the full development of the model in the Supporting Information section). The predictors of the model were the 19 bioclimatic variables available from WorldClim and topsoil pH available from The Geographical Database from Eurasia. The Watanabe–Akaike information criterion (WAIC) [We used a Bayesian approach to handle the particularities of genetic diversity data, such as the semi-continuous nature of the variable. In the case of Iberian ues Fig. c. To hanan model  to separn (WAIC) ; was comn (WAIC) .A. thaliana as follows. IBD was based on geographic distance. However, instead of using the Euclidian distance among population pairs, we created a raster by assigning a 0.5 value to all 1-km2 pixels [A. thaliana populations employing the new raster. We did not use the Euclidean distance to compute IBD because straight lines between population pairs may introduce some bias when such straight lines traverses natural barriers for the study organism (i.e. the coastline or open sea) [We estimated the ecological, genetic and evolutionary drivers of genetic differentiation of Iberian 2 pixels , 91 of tpen sea) .N = 278; r = − 0.95; P < 0.001; Pearson’s correlation) and nearly summed to 100%. Finally, a topsoil pH layer was also used. Combining these 21 environmental variables, we conducted a principal component analysis (PCA) with varimax rotation using the software SPSS v.25 . Thus, we obtained the PC scores of the first three principal components (PC) for each population. We then used the PC scores to calculate environmental dissimilarity between populations as Euclidean distances derived with the ‘dist’ function in R. Furthermore, we also calculated dissimilarity matrices among populations for each PC axis separately, as they were interpretable in terms of environmental gradients.IBE was based on the 19 bioclimatic variables. In addition, we used The CORINE Land Cover to obtain the percentage of human-modified habitat  within a 500 m radius from the population GPS coordinate . The perA. thaliana across the Iberian Peninsula with the maximum-entropy modeling technique implemented in the software Maxent 3.3.3 k [A. thaliana populations available to date , and the same environmental variables used in a previous habitat suitability model [A. thaliana with lower sample sizes [To compute IBR, we first estimated habitat suitability for  3.3.3 k , 94. To ty model . In partle sizes , 56.A. thaliana (higher resistance) and vice-versa. Using circuit theory [A. thaliana populations was accounted for by IBR. We used Circuitscape v.4.0 [Next, we transformed the resulting habitat suitability into resistance values as 1 minus the value of each pixel. Thus, greater pixel values represented lower occurrence probability of t theory , 25, we pe v.4.0  to calcuFST values) among A. thaliana populations, we fitted maximum-likelihood population effect models (MLPE) [A. thaliana populations, all MLPE considered spatial locations. To this end, we used a modification of the MLPE model incorporating the correlation between pairwise measurements due to comparison of populations and spatial locations (nested MLPE or NMLPE) [FST values. We conducted NMLPE on the complete dataset (N = 278 populations) as well as on the subset of those populations exhibiting HS different from zero (N = 212 populations with HS > 0.009). The results were consistent between the two datasets, showing that the 66 populations with no genetic diversity were not biasing the results.To quantify the relative contributions of IBD, IBE and IBR on the genetic differentiation (pairwise s (MLPE) . Code imr NMLPE) . Since tA. thaliana in the region. We considered each population to belong to a specific genetic cluster when the average value of the membership proportions of its individuals was ≥0.3 for that specific cluster. Given the marked genetic structure of the study system, threshold values of 0.25 and 0.40 were consistent, but 0.3 provided a clearer classification of individuals into genetic clusters. The majority of populations were assigned to a single cluster (N = 230). We assigned populations reaching the minimum average membership proportion for more than one cluster to multiple clusters. Overall, 47 populations were assigned to two clusters, whereas only one population had average membership proportions higher than 0.3 for three clusters.In addition, we ran NMLPE for each genetic cluster to evaluate whether the effects of IBD, IBE and IBR on genetic differentiation differed among genetic clusters. This is because the number of genetic clusters represents the outcome of the demographic and evolutionary history of r < 0.6), created with the ‘dredge’ function from the R package MuMIn v1.4 [β) with standard errors for the explanatory variables included in all top-ranked models also using MuMIn. We considered effect sizes as significant when 95% confidence intervals did not overlap zero.We also ran NMLPE on the complete dataset and each cluster separately, but replacing IBE by the PC axes. Thus, we used IBD, PC1, PC2, PC3 and IBR as independent variables. We used the Akaike information criterion (AIC) to compare models containing all possible combinations of non-collinear predictions (MIn v1.4 . In thos