These AZFb deletions are caused by non-allelic homologous recombination (NAHR) events between identical sequence blocks located in the proximal arm of the P5 palindrome and within P1.2, a 92 kb long sequence block located in the P1 palindrome structure of AZFc in Yq11. This large genomic Y region includes deletion of 6 protein encoding Y genes, EIFA1Y, HSFY, PRY, RBMY1, RPS4Y, SMCY. Additionally, one copy of CDY2 and XKRY located in the proximal P5 palindrome and one copy of BPY1, two copies of DAZ located in the P2 palindrome, and one copy of CDY1 located proximal to P1.2 are included within this AZFb microdeletion. It overlaps thus distally along 2.3 Mb with the proximal part of the genomic AZFc deletion. However, AZFb deletions have been also reported with distinct break sites in the proximal and/or distal AZFb breakpoint intervals on the Y chromosome of infertile men. These so called “non-classical” AZFb deletions are associated with variable testicular pathologies, including meiotic arrest, cryptozoospermia, severe oligozoospermia, or oligoasthenoteratozoospermia (OAT syndrome), respectively. This raised the question whether there are any specific length(s) of the AZFb deletion interval along Yq11 required to cause meiotic arrest of the patient’s spermatogenesis, respectively, whether there is any single AZFb Y gene deletion also able to cause this “classical” AZFb testicular pathology? Review of the literature and more cases with “classical” and “non-classical” AZFb deletions analysed in our lab since the last 20 years suggests that the composition of the genomic Y sequence in AZFb is variable in men with distinct Y haplogroups especially in the distal AZFb region overlapping with the proximal AZFc deletion interval and that its extension can be “polymorphic” in the P3 palindrome. That means this AZFb subinterval can be rearranged or deleted also on the Y chromosome of fertile men. Any AZFb deletion observed in infertile men with azoospermia should therefore be confirmed as “de novo” mutation event, i.e., not present on the Y chromosome of the patient’s father or fertile brother before it is considered as causative agent for man’s infertility. Moreover, its molecular length in Yq11 should be comparable to that of the “classical” AZFb deletion, before meiotic arrest is prognosed as the patient’s testicular pathology.Genomic AZFb deletions in Yq11 coined “ The Azoospermia Factor (AZF) locus on the human Y chromosome extends along the euchromatic long Y arm region Yq11). Molecular mapping analyses of Yq11 breakpoint sites on the Y chromosome of infertile men with microscopically visible AZF-Yq11 aberrations revealed distinct breakpoints along the complete Y long arm (Yq11.1–11.23). Histological analyses of their individual testicular pathologies revealed that these Yq11 deletions were associated with disruption of the patients spermatogenesis at different development phases [1. Molecun dinucleotides [It has been shown that complete de-condensation of the long Y arm along its euchromatin in Yq11 in human premeiotic male germ cells  is requileotides . It suggMolecular analyses of small Yq11 deletions on the Y chromosome of men with a normal, 46,XY karyotype, then revealed three groups of small interstitial microdeletions in Yq11, which were “de novo” mutation events, because only found on the Y chromosome of the infertile patient, but not on the Y chromosome of his father, respectively, fertile brother . BecauseToday we know that these AZF subregions are deleted in Yq11 in about 13% of men with non-obstructive azoospermia and in about 7–10% of men with severe oligozoospermia. Thereby AZFc deletions are most frequent (60%) and AZFb deletions occurred in about 6–10% of azoospermic patient groups. However, these frequencies are also known to be variable in distinct human populations [www.ncbi.nlm.nih.gov/nuccore/NC_000024.10) revealed a complex organisation of large repetitive sequence blocks with high sequence homology (99.7%) especially on the long Y arm encompassing the genomic AZFb and AZFc deletion interval. Analysis of the lengths of these so called “classical” AZFb deletions in Yq11, i.e., those causing meiotic arrest [classical” AZFb deletions with loss of 6.23 Mb genomic Y [Classical” AZFb deletions are therefore also called “P5/proximal-P1.2” deletions. They overlap significantly (2.3 Mb) with the proximal part of AZFc deletions www.ncbi.Classical” AZFb deletions include deletion of six protein encoding Y genes, EIFA1Y, HSFY , PRY , RBMY1 , RPS4Y, SMCY  deleted completely in patients with this Y chromosome aberration and additionally, one copy of CDY2 and XKRY located in the distal yel(low)4 amplicon of the P5 palindrome and copies of the AZFc gene families, BPY1 (1 copy); DAZ (2 copies), CDY1 (1 copy) in the distal overlapping AZFb/c interval  have been developed –15 with eletions : frist sclassical” length in Yq11 are expressed predominantly only in testis tissue [HSFY with two copies in the b5 and b6 amplicons of the P4 palindrome encode proteins which are expressed in germ cells  and also in Sertoli cells [SMCY (aka KDM5D) encoded proteins can form a complex with MSH5, a critical meiosis-regulatory protein at a specific meiotic stage of mouse spermatogenesis; immunohistochemical analyses with murine testicular tissue sections revealed co-localization of both proteins [EIF1AY expression looks expressed ubiquitously and seems to be functional equivalent with its X homologue, EIF1AX although with higher transcript levels especially in heart tissue [AZFb Y genes deleted completely in AZFb deletions with the so called “s tissue . What isli cells . SMCY  gene deletions have not yet been found in infertile men. Deletion of the mouse homologue (Kdm5d) did not cause male infertility [HSFY in one azoospermic patient from Romania [HSFY might contribute to meiotic germ cell function . SingleRBMY1 or PRY gene deletions are difficult to assess because the four functional RBMY1 gene copies located in distal u1  and the two functional RBMY1 gene copies (RBMY1F and RBMY1J) located in the neighboured P3 palindrome of AZFb  where are the exact breakpoints located: proximal to, or in the P3 palindrome, or in the distal AZFb-AZFc overlap region, far or near to the P1.2 block in the yel1 amplicon like for the “classical” AZFb deletions; (2) has been clarified whether the observed partial AZFb microdeletion is a “de novo” mutation event thus only found on the patient Y chromosome, or is it “polymorphic”, i.e., also found on the Y chromosome of the fertile father?Indeed, it could be shown that distal breakpoints of partial AZFb deletions associated with variable testicular histology, but not meiotic arrest, are concentrated in the P3 palindrome and the flanking overlapping genomic AZFb-AZFc interval, respectively –41. Thes“classical” AZFb deletion are summarized as “non-classical” AZFb deletions. Depending on the location of their break-sites, they are associated with variable testicular pathologies and patients might be able to produce even mature sperms although only in low numbers and often associated with increased dysmorphologies including the OAT syndrome (see below). We analysed in more detail the genomic Y DNA of a man with OAT syndrome (case ID: AZFG189), because we identified a distal shift of the proximal AZFb break site  were performed near the putative proximal and distal AZFb breakpoint regions. Subsequent sequence analysis of two STS markers across the AZFb breakpoint-fusion region on the Y chromosome of AZFG189 revealed absence of all RBMY1 gene copies in u1 and presence of only the RBMY1F/J gene copies in the P3 palindrome. A non-allelic homologous recombination (NAHR) event between two homologous Tn sequence blocks (n = 19–21) located in the b5 and t1 amplicon, respectively seemed to the molecular origin of this partial AZFb deletion in AZG189  locus [PRY deletions and partial RBMY1 gene copy deletions which looks to be compatible with normal male fertility [However, similar partial AZFb deletions associated with the occurrence of OAT syndrome and including at least part of the P3 palindrome have been also described as “ulations –41. Hot ”) locus . They reertility .RBMY1 and PRY genes but distinct border sites  belongs to the Y lineage R1b* [It has been shown that the first genomic sequence of the human Y chromosome  now alsoage R1b* . The molage R1b* , 49. Our“non-classical” extensions of their AZFb deletions for their Y chromosomal haplogroups and compared it with that of our reference samples defining the “classical” AZFb deletion associated with meiotic arrest testicular pathology [non-classical” AZFb deletions displayed different Y haplogroups  along AZFb and AZFc in fertile and infertile men from China that numerous extensions of AZFb deletions exists in these human population and that these mutation events are indeed associated with distinct haplogroups of the patients’ Y chromosomes . In thisThree large heterochromatic sequence blocks, DYZ17, DYZ18, DYZ19 were mapped in Yq11 by sequence analyses  and whetAlthough nothing is known yet about a putative dynamic de-condensation-condensation cycle of the DYZ19 heterochromatin block during the pre-meiotic X–Y pairing process it has been reported that this germ line specific X–Y chromatin alignment process is distorted in infertile men with AZF microdeletions  and thatBesides DYZ19, probably another putative locus specific chromatin structure can be assumed in the blue amplicons forming the P4 palindrome (b5 and b6) and related with distinct sequence homologies to the b1 b2, b3, b4 amplicons in distal AZFb and AZFc, respectively  . Express


LOXL2) and Electron Transfer Flavoprotein Beta Subunit (ETFβ), in both serum and heart tissue of 6–16-week-old db/db mice, correlated with a reduced left-ventricular diastolic dysfunction as assessed by high-resolution Doppler echocardiography. Principal component analysis of the combined biomarkers, LOXL2 and ETFβ, further displayed a significant difference between wild type and db/db mice from as early as 9 weeks of age. Knockdown in H9c2 cells, utilising siRNA of either LOXL2 or ETFβ, revealed a decrease in the expression of Collagen Type I Alpha1 (COL1A1), a marker known to contribute to enhanced myocardial fibrosis. Additionally, receiver-operating curve (ROC) analysis of the proposed diagnostic profile showed that the combination of LOXL2 and ETFβ resulted in an area under the curve (AUC) of 0.813, with a cut-off point of 0.824, thus suggesting the favorable positive predictive power of the model and further supporting the use of LOXL2 and ETFβ as possible early predictive DCM biomarkers.Type 2 diabetes (T2D) is characterized by metabolic derangements that cause a shift in substrate preference, inducing cardiac interstitial fibrosis. Interstitial fibrosis plays a key role in aggravating left ventricular diastolic dysfunction (LVDD), which has previously been associated with the asymptomatic onset of heart failure. The latter is responsible for 80% of deaths among diabetic patients and has been termed diabetic cardiomyopathy (DCM). Through in silico prediction and subsequent detection in a leptin receptor-deficient db/db mice model (db/db), we confirmed the presence of previously identified potential biomarkers to detect the early onset of DCM. Differential expression of Lysyl Oxidase Like 2 ( Diabetes and its complications are inextricably linked to cardiovascular dysfunction, which is currently the leading cause of mortality worldwide, affecting 17.9 million individuals2. This association was first reported in the Framingham Heart study and since then numerous reports have come to the forefront, confirming a 2–4 times increased susceptibility of diabetic individuals to heart failure (HF)4. Coronary artery disease (CAD) is the major type of CVD responsible for HF in diabetic individuals, however diabetic cardiomyopathy (DCM) is an established complication of diabetes mellitus (DM) existing in the absence of CAD or hypertension5. Furthermore, DCM is referred to as the silent killer, due to the manifestation of a long subclinical period in which the disease exists with no overt clinical symptoms8.Diabetes affects 463 million people worldwide and this number is said to increase to 700 million by 20459. Also, diabetic patients with subclinical diastolic dysfunction (DD) have a 5-year motility rate of 30% compared to the 12.1% for non-diabetic patients with no diastolic dysfunction10. As DCM enters its later stage, it progresses from DD to overt heart failure with preserved ejection fraction, which has no proven effective treatment nor a biomarker available that can detect the early onset of DCM8. TDI- and STI echocardiograph analysis are the only imaging technologies available to detect DCM in its asymptomatic stage. However, these techniques are not always available, especially in developing countries. Current diagnostic markers include N-terminal pro b-type brain natriuretic peptide (NT-proBNP) and cardiac troponin T (cTn-T). However, none of these markers are able to detect DCM, either in its asymptomatic stage or early enough to have a therapeutic impact.DCM proceeds in three different stages: asymptomatic, diastolic and systolic stages, respectively. In its asymptomatic stage, the disease is characterized by abnormal left ventricular relaxation, referred to as left ventricular dysfunction (LVD). These functional abnormalities can be seen in 40–60% of asymptomatic diabetic cases using Doppler echocardiography, Tissue Doppler Imaging (TDI) echocardiography analysis or Speckle Track Imaging (STI) echocardiographyAs such, there is a need for a prognostic marker that can improve early detection of DCM prior to the onset of irreversible complications. However, despite numerous clinical and pharmacological intervention studies, DCM remains elusive warranting new strategies to identify asymptomatic detection of the disease state. In this study, using an in silico predictive method the combination use of two possible biomarkers was identified, Lysyl Oxidase Like 2 (LOXL2) and Electron Transfer Flavoprotein Beta Subunit (ETFβ). The presence of the identified markers was then screened in the serum of db/db mice with confirmed LVDD.p value < 6 × 10–6), namely LOXL2 and ETFβ, for further downstream analysis  were identified for T2DM and CVD, respectively. Thereafter, using a similar quantile discretization (QD) analysis, T2DM and CVD genes were integrated, and 228 common genes were identified. Due to limited serum availability in the animal study and to prevent bias, the top 2 genes were selected according to their Wilcoxon score (p > 0.05). Fasting blood glucose levels, cholesterol, triglycerides and low-density lipoproteins (LDL) of db/db mice were significantly elevated from 9 weeks onwards when compared to their age-matched controls. The observed effects are characteristic of metabolic syndrome that continued throughout the study.To assess the presence of cardiac risk factors in our db/db model, fasting blood glucose levels (FBG) and serum lipids levels were investigated Table . In thisLOXL2 mRNA expression at 11-weeks  when comparing db/db mice to the db/+ control. This expression difference was maintained at both 14-weeks  and 16-weeks   when comparing db/db mice to db/+ control, after which a tendency to decrease in expression was observed at 14- (1.2-fold) and 16-weeks (1.3-fold), , and 11-weeks (p < 0.01) when compared to age-matched controls, whereas serum levels at 6-, 14- and 16-weeks showed an increasing trend (p > 0.05)  and were significantly reduced at 11- (p < 0.01), 14- (p < 0.001) and 16-weeks (p < 0.05) when compared to the control   Fig. A. Next srol Fig. B. Intere05) Fig. C.Figure p > 0.05). As shown in Fig. DT) and in isovolumic relaxation time (IVRT) in db/db compared to db/+ at both 14 and 16 weeks , while a positive correlation was observed between ETFβ and the E: A ratio   as well as NT-proBNP, using the Pearson’s correlation test, and observed no linear relationship between serum levels of NT-proBNP and the mitral E:A ratio r = 0.05)  and palmitate (PAL). Results obtained showed that HG and PAL significantly increased scr_LOXL2, while decreasing scr_ETFβ when compared to the untreated control (p < 0.001)   when compared to the scrambled  Fig. A,C. Altely) Fig. A,C. Subsled Fig. B. Similaion Fig. D.Figure p < 0.001) between db/+ and db/db mice at each week and compared to that of NT-proBNP. The results obtained showed no significant differences for NT-proBNP and LOXL2 at all the evaluated time points. Conversely, ETFβ showed significant differences at week 11 and 14 (p < 0.01) (Table LOXL2 generated area under the curve (AUC) values that remained non-significant in both specified and unspecified groups (data not shown). However, results obtained for ETFβ demonstrated an AUC of 0.679 when the groups were not specified  and it’s possible use in a clinical setting, a generalised linear model was used to test for significant differences  and NT-pro-BNP are well-established serum markers used to detect cardiac abnormalities, they are unable to predict the early onset of DCM13. This was confirmed in the current study. Furthermore, TDI echocardiography, Phase-Magnetic Resonance Imaging and 2D/3D-Speckle Tracking echocardiography are all non-invasive diagnostic methods with the ability to diagnose and stratify LVDD in diabetic patients. However, these tests are expensive and not done routinely as they are specialized equipment that requires trained personal which is not available in all healthcare facilities. Recently, sodium-glucose linked transporter (SGLT2) inhibitors have been recognized as a new class of drugs that can protect the diabetic myocardium. However, clinicians are currently faced with the dilemma of when to start treating diabetic individuals with SGLT2 inhibitors. Thus, a routine biomarker would greatly aid the confirmation of the presence of cardiac dysfunction and would allow an opportunity for the implementation of corrective therapy.Currently, there is no single prognostic test that is sensitive enough to detect the pre-clinical onset of LVDD15. Pertaining to ETFβ, Lu reported that decreased ETFβ expression was implicated in mitochondrial shift in substrate preference17. Also, in a review conducted by Lorenzo and colleagues, the mentioned disease pathophysiology came under investigation and the authors concluded that both processes  play a major role in the development of diabetes-induced HF18. However, none of the mentioned studies aimed to investigate the combined use of the identified serum biomarkers. As such, our current study is the first to investigate the use of LOXL2 and ETFβ as a combination biomarker for early detection of DCM.In the current study, we confirmed the occurrence of two potential serum biomarkers, LOXL2 and ETFβ, to detect early onset of DCM. The presence of serum LOXL2 was first reported by Yang followed by Zhao who argued that augmented LOXL2 enhances collagen crosslinking that consequently increases cardiac fibrosis20 with increased LDL, cholesterol and triglycerides observed by week 922. We observed that by 6 weeks, diabetic mice weighed significantly more and presented with increased FBG, triglycerides, LDL and cholesterol levels. The latter are known features of DCM and were confirmed in a study conducted by Dludla, where the authors showed that increased FBG and lipid profiles are key risk predictors of cardiac fibrosis and subsequent HF22. Furthermore, it has been argued by Fang that myocardial functional changes do not progress in parallel with histological changes8. Instead, diabetic individuals have a long latent phase in which functional abnormalities, induced by the hyperglycaemic state, precede the onset of any structural changes. This was confirmed by Liu who argued that DCM has a long latent phase in which subtle structural modifications occur in the absence of symptomatic onset of the disease23. The current study confirmed this with echocardiography changes confirming the presence of reduced ejection fraction whilst subtle changes in fibrosis was observed at 16 weeks  patients15. Furthermore, findings from Lu and later studies by Karthik argued that ETFβ may be modulated during the development of insulin resistance in pancreatic β cells whereas the upregulation reduced cardiovascular dysfunction 27. Conversely, Boudina showed that after exposing db/db mice to increased palmitate, protein expression levels of ETFβ were significantly decreased, however no coordinate increase in oxidative phosphorylation was observed28. Nonetheless, more recently in a study done by Ruiz-Pinto, it was found that susceptible variants in the ETFβ is a major contributor to anthracycline-mediated oxidative stress and myocardial injury. Inferring that decreased ETFβ expression is linked to myocardial dysfunction29. Our study made use of a db/db mice model who confirmed previous findings from Yang14 and Lu17, using different models showing that differential expression of LOXL2 and ETFβ was concomitant to increased ROS. Inferring that LOXL2 and ETFβ might be implicated in the disease pathophysiology of DCM.During disease progression, cardiac hypertrophy is usually characterized by an increase in interstitial fibrosis with augmented triglycerides and LDL affecting oxidative phosphorylation, myocardial structure as well as gene and protein expression of essential genes within this process30.During disease progression, increased gluco- and lipotoxicity affect mitochondrial substrate preference ensuing in the enlargement of the heart muscle and hindering the ability of the myocardium to pump blood through the body effectively. As cardiomyopathy progresses, the heart becomes weaker due to reduced ejection fraction and is less able to pump blood through the body effectively. Although the precise age of onset in heart muscle deterioration in DCM is still incompletely understood, the development of LVD can directly be associated with increased fibrosis and development of LV wall thickness and decreased mitral E:A ratio. Mitral E:A ratio and ejection fraction are currently used as measurements of the hearts' ability to function optimally. In our study, we found that our identified serum markers correlated with cardiac dysfunction when the disease was still in its asymptomatic stage. This implies that the change in the circulatory markers (LOXL2 and ETFβ) are as a result of the diabetic state that induces functional changes prior to the onset of any pathological changes. As such, the observed differential expression of the biomarkers is complicit to the maladaptive structural modifications that follow. According to Fuentes-Antras the maladaptive structural changes underlies the observed changes as shown by echocardiography, confirming that the hyperglycaemic state is the cause of LVDCOL1A, a gene associated with increased fibrosis. Increased interstitial fibrosis, in the stressed heart, triggers the formation of enhanced collagen deposition that stiffens the heart muscle, while impairing diastolic relaxation and filling. Increased collagen deposits are not only concomitant to increased COL1A expression but also reduced ejection fraction and LV remodeling. Our data presented, therefore, infers that collagen formation might be modulated by LOXL2 and ETFβ. Our findings are supported by two independent research groups reporting on the role of LOXL2 and ETFβ and their association with increased fibrosis32. In addition, ROC curve analysis strongly supports that the identified markers were sensitive enough to detect the early onset of DCM, as confirmed by the AUC of 0.813 which is an indication of a good predictive test.Furthermore, this study showed that knockdown of either LOXL2 or ETFβ was able to reduce the expression of 28, ETFβ is an electron acceptor that transfers electrons between Acyl-CoA substrates to the main respiratory chain, ubiquinone, which links the oxidation of fatty acids to the mitochondrial respiratory system. As such, a loss of function or decreased ETFβ is known to alter cardiac mitochondrial flux, which consequently is the hallmark of DCM. To address this future studies should consider assessing the link between ETFβ and respiratory chain function and mitochondrial ultrastructural abnormalities within the disease state.A limitation to this study was the inability to show the role ETFβ plays in mitochondrial bioenergetics and the effect a loss in ETFβ enzyme activity has on the respiratory chain, and subsequent ATP production. According to Boudina14 and Lu17 it could be suggested that those diabetic patients (in this study reference is made to mice) with early dysregulation of serum LOXL2 and ETFβ levels may be treated prophylactically in order to slow the progress of CVD. The proposed implementation strategy might be cost-intensive, however, in time patients might benefit from such treatment through increased life expectancy. As such, a follow-up human study should be initiated to prove such a hypothesis as the scope of this study did not encompass this important aspect. In the case of whether the identified markers can be used as either a preventative or even therapeutic intervention, it requires further investigation in order to support the clinical implication of the attained results.Furthermore, it would have been of interest to follow disease progression from asymptomatic until a late stage of DCM, using LOXL2 and ETFβ as serum biomarkers. Also, scarring as a consequence of hyperglyceamia is a progressive disease that worsens over time, with no cure and attaining treatment early might slow progression whilst maintaining the quality of life of the affected individual. Based on findings presented including studies performed by Yang28. We observed a significant increase in FBG and serum lipids levels from 9 weeks onwards. This was concomitant with increased LOXL2 and decreased ETFβ serum levels at 9 and 11 weeks, respectively. Confirming a diabetic phenotype that was sustained until termination. As such, it would have been of interest to investigate if the identified changes occur only in the diabetic phenotype, regardless of whether they develop CVD and/or whether only the diabetic animals that eventually develop CVD had differentially regulated levels of the identified markers. Further investigation would need to be conducted to address this issue.It is well known that that increased hyperglycemia and augmented lipid accumulation are the main drivers of myocardial dysfunction and subsequent heart failure. In our study, a change in previously identified possible cardiac markers, LOXL2 and ETFβ, was confirmed in the serum of db/db mice and correlated with increased LVDD, as observed with high-resolution Doppler echocardiography14. Most importantly, our data strongly suggest that the combined use of LOXL2 and ETFβ displayed a good predictive power to detect the early onset of DCM and as such, support that the disease progression can be slowed through early intervention.Furthermore, this study brings further insights to suggest that LOXL2 and ETFβ might be implicated in the pathophysiology of DCM. Mechanistically, we propose that both identified biomarkers were able to regulate the expression of COLA1, a known cardiac fibrotic markerhttps://www.ebi.ac.uk/arrayexpress) was screened for studies with publicly available microarray datasets belonging to Homo sapiens over 35 years of age. These studies included healthy controls and patients suffering from either type 2 diabetes mellitus (T2DM) or cardiovascular disease (CVD). To provide specificity for the disease state of interest (DCM), studies involving ischemia, CAD and hypertension were excluded; while studies inclusive of LVD and left ventricular ejection fraction (EF) were included with no restriction on ethnic group, gender, tissue or cell type.In order to identify candidate genes differentially expressed in diabetic cardiomyopathy (DCM), the ArrayExpress database (GEOquery. Samples within these datasets fulfilling the specified criteria were then Robust-Multi array Average (RMA) normalised using the R package, Simpleaffy. Additionally, studies using a custom spotted oligonucleotide array was also included. These raw datasets were imported into R using the ArrayExpress package, while the Limma package was used for normalisation. Subsequently, microarray probe identifiers (IDs) in all the processed datasets were converted to Ensemble IDs or gene names, using a custom Python script. Probe sets, mapping multiple Ensembl IDs and technical replicates, were averaged to resolve probe redundancy using R and Ruby scripting.For studies, utilising the Affymetrix and Agilent platform, raw datasets were extracted from Gene Expression Omnibus (GEO) using R scripting and the R package, p-value of < 0.05 were regarded as significantly different using the Wilcoxon Rank Sum Test with Bonferroni and Benjamin-Hochberg correction for multiple testing. To prevent bias and due to limited samples, only the top 2 genes based on their Wilcoxon score were used for subsequent biomarker analysis.Differential expression analysis was performed in three stages: 1) to obtain differentially expressed genes (DEGs) for T2DM only, 2) to obtain DEGs for CVD only and 3) to obtain DEGs for integrated datasets from both T2DM and CVD, representing the DCM group. To this end, common Ensemble IDs were combined using a quantile discretization (QD) data binning approach, which integrates different microarray data at the gene expression level. In this study, a QD range of 128 was used to combine datasets in the T2DM and CVD subgroups only for statistical differential expression analysis, while a QD range of 1,028 was used for the integration (i.e. DCM) subgroup. Genes with a Male C57BLKS/J leptin-receptor-deficient mice were obtained from Jackson’s Laboratories  and housed at the Primate Unit and Delft Animal Centre (PUDAC) of the South African Medical Research Council (SAMRC) in a controlled environment with a 12 h light/dark cycle in a temperature range of 23-25ºC (relative humidity: ∼50%). The mice received standard laboratory chow pellets  ad libitum and had free access to water. At 6 weeks, based on their genetic background, mice were divided into 2 groups: (1) homozygous leptin receptor-deficient diabetic  mice and (2) heterozygous leptin-receptor-deficient non-diabetic wild type controls . Each group was then further divided into a total of 5 subgroups , with n = 6–8 animals per subgroup. After each week,  high-resolution Doppler echocardiography was performed, and animals were terminated to collect blood and heart tissue for subsequent serum biomarker analysis. Ethical clearance for the use of animals was obtained from South African Medical Research Council (SAMRC) (ECRA 07/13) and all experiments were conducted in accordance with the National Institute of Health Guide for the Care and use of laboratory animals.The mice were placed in a holding chamber and gassed with 1.0–2.5% isoflurane, whereafter they were removed and positioned in the supine position on a warming pad. Closed chest high-resolution Doppler echocardiography was performed with a VEVO 2,100 ultrasound system  and a 30 MHz linear array transducer at the respective time intervals. Left ventricular internal diameter, interventricular septum thickness, posterior wall thickness were measured in systole and diastole . EF was measured as a marker of systolic function. Mitral E and A wave flow velocity, E:A ratio, deceleration time of mitral E wave velocity and isovolumic relaxation time (IVRT) were assessed as measurements of LVD using Doppler echocardiography . All measurements were made off-line on the mean of at least three consecutive cardiac cycles with the software resident on the ultrasound system.Body weights were measured weekly, while 4 h FBG levels were monitored by tail prick using a OneTouch Select glucometer .On the day of termination, mice were anaesthetised with halothane before blood was collected from the abdominal vena cava for subsequent lipid profile and serum biomarker analysis. Serum was isolated from peripheral blood by centrifugation at 4,000 g for 15 min. Thereafter, the serum was removed and divided into two parts; one part was sent to PathCare Medical Diagnostic Laboratories  for total cholesterol, triglycerides and LDL analysis; while the other half was stored at -80ºC for subsequent serum biomarker analysis.LOXL2, ETFβ and NT-proBNP concentrations were measured with enzyme-linked immunosorbent assay (ELISA) , according to the manufacturer’s instructions.4 cells/well and left for 2–3 days until confluent. On day 4, cells were utilised for transfection experiments using siRNA.H9c2 cardiomyoblasts were cultured in supplemented Dulbecco’s Modified Eagle’s Medium  under standard tissue culture conditions (37 °C in humidified air and 5% CO2). Cells were seeded in a 6-well plate at a seeding density of 2 × 10Knockdown experiments for the two selected candidate biomarkers were done using a Lipofectamine RNAimax reagent , according to the manufacturer’s instructions. Briefly, H9c2 cells at approximately 70% confluence were transfected with either siLOXL2, siETFβ or scrRNA for LOXL2 or ETFβ for 24 h, respectively. Thereafter, transfected cells were exposed to both high glucose  and palmitate  for 6 h. The degree of knockdown after 24 h of transfection was confirmed by RT-PCR. Non-transfected cells, exposed to either 5.5 mM glucose or 33 mM glucose, served as controls, respectively.33. Thereafter, qRT-PCR analysis was performed using 5 µL Taqman Universal PCR master mix, 0.5 µL predesigned and optimized TaqMan gene expression assays  and 1 µL cDNA on an ABI 7,500 Instrument . The PCR reaction was as follows: 50 °C for 1 min and 95 °C for 10 min; followed by 95 °C for 15 s and 60° C for 30 s (40 cycles). Gene expression data was normalized to that of Actin-β (ACT-β) and hypoxanthine–guanine phosphoribosyltransferase (HPRT) and relative gene expression was calculated using the standard curve method.Total RNA was extracted using the TRIzol method , and subsequently purified with an RNeasy mini kit according to the manufacturer’s instructions. The extracted RNA was treated with DNase1  to eliminate genomic DNA contamination and reversed transcribed to generate cDNA with a Superscript first-strand cDNA synthesis kit  as per previously described protocol1 a normal distribution. To evaluate the diagnostic potential of each biomarker, principal component (PCI) analysis was performed by combining the data for the identified biomarkers and receiver operating characteristic (ROC) curve analysis, where an area under the curve (AUC) greater than 0.5 indicated diagnostic value. Subsequently, the ROC curve analysis was used to calculate corresponding sensitivity and specificity. To measure the strength and nature of the relationship between the expression levels of the candidate biomarkers and mitral E:A ratio, correlation analysis was performed using a Pearson and Spearman coefficient test. Statistical analyses were performed using GraphPad Prism software version 5.00 . In all instances, statistical significance was inferred for a p-value less than, or equal to 0.05.All data obtained throughout this study are expressed as the mean ± standard error of the mean (SEM) where appropriate. Results for in vitro experiments were expressed as the mean of three independent biological experiments, with each experiment containing at least three technical replicates, unless otherwise stated. For in vivo experiments, each treatment group included six mice. Differences between the means of groups were first tested for normality with Shapiro–Wilk tests. In the instances where data sets were normally distributed, they were assessed for statistical significance using one-way or two-way multivariate ANOVA and Student t-tests, while non-parametric Mann–Whitney U tests were used to analyse data sets deviating fromSupplementary Information.


InflammAGE score) was then assessed in relation to core AD biomarkers of amyloid, tau, and cognition. Both CSF and plasma inflammatory proteins were significantly associated with age in Aβ− CU individuals, with CSF-based proteins predicting chronological age better than plasma-based counterparts. Meanwhile, the Aβ− SCD and validation Aβ− CU groups were not characterized by significant inflammatory aging, while there was increased inflammatory aging in Aβ− MCI patients for CSF but not plasma inflammatory markers. Both CSF and plasma inflammatory changes were seen in the Aβ+ MCI and Aβ+ AD groups, with varying degrees of change compared to Aβ− CU and Aβ− SCD groups. Finally, CSF inflammatory changes were highly correlated with amyloid, tau, general neurodegeneration, and cognition, while plasma changes were mostly associated with amyloid and cognition. Inflammatory pathways change during aging and are specifically altered in AD, tracking closely with pathological hallmarks. These results have implications for tracking AD progression and for suggesting possible pathways for drug targeting.It is unclear how pathological aging of the inflammatory system relates to Alzheimer’s disease (AD). We tested whether age-related inflammatory changes in cerebrospinal fluid (CSF) and plasma exist across different stages of AD, and whether such changes related to AD pathology. Linear regression was first used model chronological age in amyloid-β negative, cognitively unimpaired individuals  based on a collection of 73 inflammatory proteins measured in both CSF and plasma. Fitted models were then applied on protein levels from Aβ+ individuals with mild cognitive impairment  or Alzheimer’s disease dementia  to test whether the age predicted from proteins alone (“inflammatory age”) differed significantly from true chronological age. Aβ− individuals with subjective cognitive decline  or MCI  were used as an independent contrast group. The difference between inflammatory age and chronological age ( Alzheimer’s disease (AD), the most common form of dementia, is characterized in particular by significant brain atrophy beyond what is expected during normal aging, and the discrepancy between biological-based brain age and chronologsical age can both predict future outcomes in individuals at an early stage of the disease, as well as aid in differential diagnosi5.The term “brain age” has emerged in recent years to describe the phenomenon whereby individuals’ brain morphology is highly predictive of chronological age. Using this framework, studies have shown that diseases of the brain exacerabate the normal trajectory of brain aging such that individuas with neurodegenerative disease have a brain structure more akin to a significantly older, healthy individual6. With the increased availability of large-scale proteomic screening tools, for instance, it is now prudent to investigate proteomic aging in the context of AD. Inflammatory proteomics is particularly well-suited to contribute to our understanding of AD by identifying subject-specific inflammatory risk patterns7. The relevance of inflammatory aging in AD is supported by observations of age-related changes in inflammatory pathways and the established connection between increased inflammatory response and AD-related pathophysiological changes9.This brain age paradigm can also be adapted to other types of data in order to describe other types of biological aging10. Chronic inflammation has been implicated in the onset and progression of AD, and a causative role for inflammatory pathways in AD is supported by genetic evidence12. However, most clinical studies rely on a single, broad measure of inflammation and thus it remains unclear which inflammatory pathways in particular are altered by AD progression beyond what is expected in normal aging.Inflammation has been shown to be highly coupled with AD progression, but the role of innate immunity in AD is largely unexplored from a clinical perspectiveIn the current study, we explored accelerated aging of the cerebrospinal fluid (CSF) and plasma inflammatory proteome in both preclinical and clinical stages of AD in a large cohort study of 859 individuals using 73 inflammatory proteins measured in both CSF and plasma. Our main hypothesis was that inflammatory protein levels would be significantly associated with chronological age in healthy individuals without biomarker evidence of AD, and that individuals who find themselves at various stages in the AD continuum would be characterized by biologically older inflammatory proteomes. Furthermore, we hypothesized that key hallmarks of AD, including CSF measures of amyloid-β (Aβ) and tau along with cognitive decline, would be related to the actual magnitude of inflammatory aging at an individual level.The study population came from the Swedish BioFINDER (Biomarkers For Identifying Neurodegenerative Disorders Early and Reliably) study—see biofinder.se for more details. Individuals included in the current analysis include Aβ− cognitively unimpaired , Aβ− subjective cognitive decline , Aβ− mild cognitive impairment , Aβ+ mild cognitive impairment , and Aβ+ AD dementia  individuals. A second group of Aβ− CU individuals (n = 29) independently recruited as controls in a Parkinson’s disease sub-study of BioFINDER served to validate the original models, while individuals in the Aβ− SCD and Aβ− MCI groups served as additional groups to which Aβ+ MCI and Aβ+ AD groups were compared.13. The inclusion criteria were the following: age greater than or equal to 60 years old, Mini-Mental State Examination (MMSE) score at or above 28 during screening and at or above 27 at baseline visit, fluent in Swedish. MCI individuals were recruited consecutively and were thoroughly assessed by physicians with special competence in dementia disorders. The inclusion criteria were: referred to a memory clinic due to possible cognitive impairment, not fulfilling the criteria for dementia, MMSE score 24–30, at least 60 years old, and fluent in Swedish14. AD individuals were also recruited consecutively and fulfilled the NIA-AA criteria for probable AD15. Dichotomized Aβ status was determined using a pre-defined cutoff of the CSF Aβ42/40 ratio (assay described in more detail below)16.CU subjects were originally enrolled from the population-based EPIC cohort and the Nomas 3 studyThe Regional Ethics Committee in Lund, Sweden, approved the study and all subjects gave written informed consent. All procedures were carried out in accordance with relevant guidelines.17. A total of 92 proteins  from the Inflammation I multiplex panel (see https://www.olink.com/products/inflammation/ for more details) were measured in both CSF and human plasma. Protein measurement was conducted using Proximity Extension Assay (PEA) technology following the manufacturer’s protocol and has been described in detail previously17. Protein quantities were produced as normalized protein expression (NPX) values on the log2 scale and measurement values below the assay detection limit were not included in the analysis. No imputation was performed on missing data and thus only participants with all available measurements were included in the analysis.Inflammatory protein concentrations were quantified using the highly sensitive and specific Proseek multiplex immunoassay, developed by Olink Proteomics All proteins in the panel were further classified a priori according to biological process/pathway, disease area, tissue expression, and protein class. The 11 functional pathway classifications used in the present study were as follows: (1) apoptotic process, (2) cell activation involved in immune response, (3) cell adhesion, (4) cellular response to cytokine stimulus, (5) chemotaxis, (6) extracellular matrix organization, (7) inflammatory response, (8) MAPK cascade, (9) regulation of immune response, (10) response to hypoxia, and (11) secretion. Classification of a given protein into one of these 11 inflammatory process occured based on documention for that protein which was available in widely used public-access bioinformatic databases—including Uniprot, Human Protein Atlas, Gene Ontology (GO), and DisGeNET. Nearly all proteins belonged to more than one functional group, with proteins belonging on average to 3.6 different processes. A full list of which proteins belong to each inflammatory pathway is found in Table As a sensitivity analysis and to avoid potential issues with a priori classification, all proteins were included in a single model and the set of proteins which best predicted age in the Aβ− CU group (“data-driven cluster”) were selected using LASSO regression (described in more detail below).18. Cognitive measures included in the analysis were the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), both of which have established relevance for diagnosis and tracking of disease progression, along with use as endpoints in AD clinical trials19.In terms of AD-related biomarkers, CSF levels of the Aβ42/Aβ40 (representing amyloid accumulation in the brain), tau phosphorylated at threonine 181 , and total tau  were measured for all participants using the Euroimmun platformFirst, linear regression was used to establish the relationship between CSF or plasma inflammatory proteins and chronological age in Aβ− CU individuals. All 73 inflammatory proteins were included as predictors in the model along with adjustment for sex, education, and mean protein level. The response variable was true chronological age. The relationship between the overall model and chronological age was assessed for statistical significance using the F-test.InflammAGE score. An InflammAGE score of + 5 for a 65 year old individual, for example, means that this individual’s inflammatory proteome is representative of an amyloid-negative, cognitive unimpaired individual who is 70 years old. Significantly increased InflammAGE scores compared to zero  was then evaluated across group using analysis of variance (ANOVA) and between each groups using pairwise t-tests.Next, age was predicted for each individual in the other groups—Aβ− CU, Aβ− SCD, Aβ− MCI, Aβ+ MCI and Aβ+ AD—by supplying each individual’s inflammatory protein values to the previously fitted models. The difference between age predicted by the fitted regression model (i.e. inflammatory age) and chronological age was then calculated to determine an InflammAGE score and AD-related biomarkers in CSF  and cognition  was tested using linear regression with the InflammAGE score as predictor and each biomarker separately as dependent variable, with adjustment for chronological age, sex, education, clinical diagnosis, and amyloid status.The association between 20. All tests were two-sided with a significant level set to p < 0.05 and adjustment for multiple comparisons using the Benjamini–Hochberg procedure where appropriate.This analysis was performed on the proteins for each of the 11 inflammatory processes and a sensitivity analysis was performed using sparse LASSO regression to select the optimal set of proteins from the entire group (“data-driven cluster”). All statistical modelling was performed in the R (v4.0.0) programming languageWritten informed consent was received from all individuals prior to participation in the BioFINDER study. Ethics approval was received by the institutional review board at Lund University. regards to cognition, the Aβ+ AD group had significantly lower MMSE scores compared to Aβ+ MCI P < 0.0001), which in turn had significantly lower MMSE scores compared to both Aβ− SCD and Aβ− CU (P < 0.0001 for both). The Aβ− SCD group also had slightly lower MMSE scores compared to Aβ− CU (P = 0.001). With regards to age, there was no difference in age between Aβ+ MCI and Aβ+ AD or between Aβ− groups and Aβ+ MCI, although the Aβ+ AD group was significantly older than the other Aβ− groups (P < 0.001). Additionally, the main Aβ− CU cohort was slightly older than the validation Aβ− CU cohort, although this difference was not significant. With regards to sex, there were more males in the Aβ+ MCI (53.3%) and Aβ+ SCD (56.9%) groups than in Aβ− CU (33.1%) and Aβ+ AD (37.1%) groups, thereby making it necessary to include sex as a covariate in all statistical models. Demographic information is further summarized in Table , which i2) by the CSF-based models ranged from 6 to 41% across inflammatory pathways , “inflammatory response” (R2 = 0.31), and “chemotaxis” (R2 = 0.31). The data-driven protein cluster had the closest association with age . The variance explained , for example3. Our results were carefully controlled for both sex and educational attainment; education was specifically accounted for due to its known association with dementia and because it may even be considered as a proxy for socioeconomic factors that affect baseline cognitive capacity22.In the present study, we measured 73 inflammatory proteins in both CSF and plasma in a large clinical cohort in order to investigate inflammatory pathway changes in AD. Our finding that both CSF and plasma proteins were highly predictive of age in Aβ− CU individuals adds to the established evidence that the innate immune system changes even during healthy aging23. Still, inflammatory proteins measured in plasma are theorized to be a potential source for detecting Alzheimer-related changes based on the idea that AD has a systemic metabolic state which can be observed in the periphery combined with recent improvements in plasma-based assays25. In any case, we hypothesize that plasma and CSF inflammatory proteins in general measure different but at least partially overlapping processes, although the task of identifying where this border lies still remains. By fitting the models in our study to highlight those proteins most closely associated with chronological age in healthy individuals, however, we hoped to thereby maximize the overlap between plasma and CSF.Importantly, the abnormal inflammatory aging we observed in the AD continuum is differentially expressed across specific inflammatory pathways and can even differ depending on whether proteins are measured in CSF or plasma. For instance, our results showing that plasma-based inflammatory aging was elevated in AD patients compared to Aβ− MCI patients suggest that cross-sectional inflammatory levels as measured in plasma may be more AD-specific at a given point in time compared to those measured in CSF. Observing longitudinal changes was unfortunately outside the scope of our current work. Meanwhile, since CSF-based inflammatory aging correlated strongly with core AD biomarkers and predicted chronological age better than plasma in the main Aβ− CU group, CSF inflammatory proteins likely track more closely with those brain-based inflammatory changes which occur during normal aging. This finding may be explained by higher analytical variability in plasma measurements or the fact that plasma inflammatory protein concentrations are influenced by more abundant proteins found in the periphery26.We further found that increased CSF inflammatory levels were observed in Aβ− SCD and Aβ− MCI groups for numerous processes, while such was not the case for plasma. Meanwhile‚ increased plasma-based inflammatory protein levels for various processes were often observed only in the AD group. This may also indicate that CSF inflammatory proteins proceed those seen in respective plasma proteins—a phenomenon which is also seen for core AD biomarkers28. However, the fact that our inflammatory aging models were fit based only on data from healthy individuals across a wide age range and without amyloid accumulation means that the changes we observed in AD patients are those most likely to be normal age-related. An AD patient predicted by our models to have an “older” inflammatory age should therefore be expected have an altered inflammatory profile as it relates to those proteins which change naturally during normal aging.One major assumption of our analysis is that healthy individuals will eventually have similar inflammatory profiles to AD patients as they age normally. However, it is difficult in the current study to separate age- and disease-related inflammation and thus it may be the case that AD patients are simply on a parallel trajectory of inflammatory change due to the disease. Indeed, there are profound differences in both causes and effects between aging-related and Alzheimer-related inflammatory changes30. In terms of drug discovery, our results point out a group of inflammatory pathways whose further study could illuminate the role of inflammation in AD pathogenesis. With regards to study limitations, we acknowledge that having an “older” inflammatory proteome can be explained by numerous factors. Previous studies suggest that the innate immune system decreases in effectiveness and is increasingly activated with age and thus this may be one suitable interpretation31. Methodologically, this study is limited by its cross-sectional in design and the fact that the Olink panel only allows for relative quantification of proteins rather than absolute quantification. Understanding how the inflammatory proteome actually changes in a single individual is therefore not possible within the current analysis. It is also difficult to use such cross-sectional clinical studies to determine whether abnormal inflammatory activation is a driver of AD pathology or simply a side-effect of the disease process12. Additionally, we did not focus on individual inflammatory proteins but rather turned our attention to higher-level inflammatory processes, thus leaving some room for interpretation as to what inflammatory pathways are being specifically targeted in our analysis.Our findings have strong implications for the use of inflammatory proteins as biomarkers to identify or track the progression of AD, particularly as plasma assays for the core AD biomarkers appear to have strong performance in both prognostic and clinical trial scenarios32. Moreover, the biological aging paradigm we employed is particularly well-suited for studies with large control groups such as ours, where a reliable trajectory can be established with regard to how a biological process changes during normal aging6. Our findings can be used as the basis for more detailed studies of inflammatory changes in AD from a fluid biomarker perspective. In the context of existing work on examing inflammatory changes in AD, our study focuses more on pathway-level changes instead of identifying individually altered single proteins as is commonly done33. The strong influence of cytokines and chemokines in our results is in strong agreement with previous studies which suggest a potential protective role such proteins upregulated during AD35. It is also possible that our analysis targeted pathways related to oxidative stress which are known to be altered in AD36. Future work may also involve investigating whether biomarkers of inflammatory alterations in AD provide additional value to tracking AD progression on top of the existing core AD biomarkers of amyloid, tau, and neurodegeneration.Still, the large number of participants in our study combined with core AD biomarker data allowing for an objective, biological approach to patient classification adds great reliability to our resultsOur study demonstrates that inflammatory aging is readily measured in AD and relates directly to pathophysiological changes during the disease process. Our results have strong implications for targeting inflammatory processes for treating AD and for tracking AD progression using inflammatory markers. These findings also suggest that a more nuanced view of inflammation in the context of AD is warranted.Supplementary Information.


Sarcomas constitute a heterogeneous group of tumors with different clinical behaviors and variable responses to systemic therapies. Recent immunotherapy studies with PD1 inhibitors (PD1i) show promising results with use in certain soft-tissue sarcomas; however, the clinical and molecular features that best predict response to PD1i remain unclear.Demographic, imaging, histologic, and genetic sequencing data was collected for sarcoma patients who received nivolumab or pembrolizumab (PD1i) treatment at our institution between January 1st 2015 and April 23rd 2018. The primary objective was to determine progression-free survival (PFS) in patients with advanced sarcomas receiving PD1i. Secondary objectives included determining overall survival (OS) and assessment of characteristics associated with response to PD1i. Fifty-six patients who were treated with PD1i therapy met inclusion criteria for this study.Partial response towards PD1i treatment was seen in 3 in 26 evaluable patients, but no complete responses were observed . Within this group of patients, the 90 day PFS was found to be 48.8%. In patients in whom PD1 expression was known, there was a statistically significant positive correlation between expression of PD1 and longer PFS and OS rates. Patients that were treated with more than four cycles of PD1i therapy were also more likely to have a greater OS.This study suggests activity of PD1i in a pretreated cohort of advanced sarcoma patients, particularly for the subset of patients with PD1 positive tumors. Our results highlight the importance of further research to better target the optimal patient population and markers of response. Sarcomas represent a diverse group of soft-tissue and bone neoplasms of mesenchymal origin, with different morphologic and genetic features as well as variable clinical behaviors for which there are currently a limited number of therapeutic options . There aCheckpoint inhibitors  have become an appealing new option for the treatment of several advanced cancers, and are now first-line and/or second-line therapies for non-small cell lung carcinoma, melanoma, and renal cell carcinomas –31. A stSo far, there have been two landmark studies of immunotherapy use in sarcoma. First, SARC0238, a phase II, single-arm study was conducted on soft-tissue and bone sarcoma patients who received pembrolizumab treatment every 3 weeks and monitored for disease progression and overall mortality . This stFurther studies are essential to assess the response of sarcomas to checkpoint inhibitors as well as determine patient factors that are associated with checkpoint inhibitor response. We performed a retrospective analysis of 56 sarcoma patients treated at our institution with PD1 inhibitors (PD1i), determined progression-free survival (PFS) and overall survival (OS), and correlated patient demographic factors with their survival rates.This was a single-institution, retrospective cohort study of adult patients with any stage sarcoma treated at The Ohio State University Medical Oncology Sarcoma Clinic between the dates of January 1st, 2015 and April 23rd, 2018 who received at least one dose of PD1i (pembrolizumab or nivolumab). Patients who were receiving both PD1i with a secondary cancer treatment (e.g. tyrosine kinase inhibitor) were allowed. Exclusion criteria included current clinical trial enrollment at the time of their treatment with checkpoint inhibitor treatment. Treatment history, demographic, genetic, pathologic, and radiologic information of these patients were retrospectively collected through a review of their electronic medical records. Intratumoral PD1 and PDL1 expression were considered positive if ≥1% of cells collected stained positive for these markers. Ethical approval of this study was obtained from The Ohio State University Comprehensive Cancer Center institutional review board (IRB protocol No. 2017C0063).Tumor measurements of lesions identified on patient CT or MRI imaging were made by a board-certified radiologist (SL), and clinical responses to PD1i treatment were assessed according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1). In order for a patient to be included in PFS analyses, it was required that they have a baseline scan within 6 weeks of initiating PD1i. Additionally, a follow-up scan at least 4 weeks after initiation of PD1i therapy was required, and imaging progression must have been deemed eligible for analysis as per RECIST 1.1. Best tumor response was evaluated by RECIST 1.1 criteria and defined as the most significant decrease in target tumor burden. Kaplan-Meier analyses were used to compare PFS and OS rates. For analysis of PFS in patients who were lost to follow-up or who were alive without PFS at the end of this study, data was censored at the time of their last tumor imaging. OS was calculated from the day that their PD1i was started until their reported death date. For analysis of OS, patients who were known to have been alive at the end of the study period were censored at this endpoint . Patient groups were compared by log-rank (Mantel-Cox) analysis. All statistical analyses were completed through use of GraphPad software .A total of 56 patients were included in this study. Among these participants, 58.9% were men, the median age was 55.5 years old, and the vast majority identified as Caucasian 91.1%) (Table .1% (Tabln = 11), followed by leiomyosarcoma (LMS) (n = 7), synovial sarcoma (n = 4), chordoma (n = 4), spindle cell sarcoma (n = 4), osteosarcoma (n = 3), UPS (n = 3), and other (n = 20). The most common primary site of these tumors was intra-abdominal (n = 15), followed by the lower extremity (n = 13), trunk (n = 11), intra-thoracic (n = 4), head/neck (n = 4), uterus (n = 3), upper extremity (n = 2), and other (n = 4). The majority of patients who received immunotherapy had already been treated with chemotherapy and had a median of two prior regimens (range of 0–8). Thirty patients received nivolumab and 20 received pembrolizumab. Six patients received a combination of nivolumab and ipilimumab (a CTLA-4 targeted checkpoint inhibitor), with two of these patients initially starting on nivolumab alone before switching to this dual checkpoint inhibitor treatment; one of these six patients transitioned to nivolumab treatment alone. Patients received a median of three cycles of PD1i treatment, most commonly discontinued due to progression of disease and/or death.The majority of patients had metastatic disease at the time PD1i therapy was initiated (91.1%), however, five had localized disease only (8.9%). The histologies of these patients sarcomas were diverse; the largest single subset was LPS  were seen in three patients; one patient with LPS who was treated with single-agent nivolumab (unknown PD1 and PDL1 status), one with inflammatory myofibroblastic sarcoma who was treated with single-agent nivolumab , and one with sarcomatoid carcinoma who was treated with single-agent pembrolizumab (positive PD1 and PDL1 status)        Fig. b. On the91) Fig. c. Of notp = 0.4004)        being the most common, followed by hypothyroidism (n = 2), colitis (n = 2), transaminitis (n = 2), endocarditis (n = 1), pleural effusion (n = 1), pneumonitis (n = 1), and uveitis (n = 1) (Table n = 5), colitis (n = 1), transaminitis (n = 1), hypocalcemia (n = 1), hyponatremia (n = 1), pancreatitis (n = 1), rash (n = 1), and pneumonitis (n = 1), all of which have been documented in previous studies with PD1i therapy [n = 1), enteritis (n = 1), and psoriasis (n = 1).In accord with previous studies of PD1i treatment, we found that adverse events using immune checkpoint inhibitors are moderately common , 38, butdism n = , colitis therapy , 38. TheMost importantly, there was a grade 5 adverse event (death), which was reported as likely attributable to autoimmune hemolytic anemia (AIHA) secondary to initial treatment with nivolumab and ipilimumab. This patient was a 32-year-old male with a history of epithelioid sarcoma of the right gluteus who was previously treated with surgical resection, chemotherapy, and radiation before initiation of PD1i therapy. He received one dose of ipilimumab and nivolumab and presented the following day with AIHA; this anemia was found to be refractory to treatment with high-dose steroids and intravenous immunoglobulin. Plasmapheresis treatment was planned; however, the patient suffered disseminated intravascular coagulation and cardiac arrest prior to this which led to his death.There remains a strong and unmet need regarding treatment options available for advanced sarcoma patients. Recently, checkpoint inhibitors have become an attractive option in a variety of cancers. Prior studies of checkpoint inhibitors in advanced sarcomas have shown promising results, especially in some sarcoma subtypes, such as UPS and LPS , 39–41. In this study, we retrospectively analyzed the outcomes of sarcoma patients who received PD1i therapy and correlated their demographic and disease-specific information to their PFS and OS rates. Looking at the patients eligible for PFS evaluation, three unique patients were found to have partial responses to PD1i. The patient with the most robust recorded response (44.8% tumor regression) was a 21-year-old woman with metastatic inflammatory myofibroblastic sarcoma (with ALK fusion) who received three cycles of nivolumab plus crizotinib before progression. Interestingly, our patient’s tumor was PDL1 negative and PD1 positive. While no prospective studies have described increased PD1 positivity in these tumors, nor has the therapeutic use of immune checkpoint inhibitors have been reported, multiple case reports have found inflammatory myofibroblastic sarcomas to express PDL1 at a significant level and have notable CD8+ tumor-infiltrating lymphocyte populations , 43.The patient with the next best response (39.2% tumor regression) was a 56-year-old woman with metastatic carcinosarcoma of the alveolar ridge of the jaw who received 13 cycles of pembrolizumab plus pazopanib prior to progression. This patient’s tumor was positive for both PD1 and PDL1 expression, which follows suit with prior studies . It shouThe last patient to show a substantial response (39.1% tumor regression) to PD1i was a 62 year old woman with localized de-differentiated LPS who received at least 12 cycles of nivolumab (patient was lost to follow-up/screened after she moved re-establishing with new oncologist). The lymphocytic make-up of these tumors has been explored and due to their immunogenic nature, it has been strongly suggested that checkpoint inhibitors may be a treatment option of interest . The SAROf the patients studied, there was a median PFS rate of 11.3 weeks, with 48.8% having a 90 day PFS rate. Using the previously recommended three-month PFS rate of ≥40% being suggestive of modest drug activity for sarcoma second-line therapy . AlternaThe median OS in our study was 37.4 weeks, which contrasts with the median overall survival of 49 weeks reported in the SARC028 trial  and the This same association was not seen with PDL1, possibly because patients were treated with a PD1 as opposed to a PDL1-targeted therapy. As the most common reason for PD1i discontinuation was progression of disease (only four patients needed to be discontinued from therapy due to adverse events secondary to treatment), it was somewhat expected that patients treated with a fewer number of PD1i cycles had lower rates of PFS and OS. Sex and age differences were also examined; however, no relationship between these variables and PFS or OS was apparent. It should be noted that 14 25%) patients received secondary agents in addition to PD1i in this study. This group of patients was directly compared to the PD1i alone group and no significant differences in PFS and OS were found (data not shown). Additionally, removal of these patients from the data sets shown in Figs. 5% patienOverall, the adverse events noted in this study were consistent with the types, frequencies, and grades of adverse events described in other clinical PD1 studies , 51. ForSoft-tissue sarcomas represent a diverse group of tumors with a wide range of survival times, such as seen with this study’s patient group (time from diagnosis to death ranging from 5 months to 28 years). Due to the small number of patients studied on PD1i therapy thus far and our ever-growing understanding of checkpoint inhibitor pharmacology, it is difficult to determine which sarcoma subtypes may benefit optimally from therapy and which are most likely to have significant adverse events. To date, checkpoint inhibitor responses have been documented in many sarcoma pathologies, including clear cell sarcoma, synovial sarcoma, osteosarcoma, chondrosarcoma, gastrointestinal stromal tumor (GIST), UPS, LMS, angiosarcoma, and myxofibrosarcoma , 39, 53.This study supports prior research that PD1i therapy has an important role in the treatment of sarcomas; however, it is also essential to evaluate what may render PD1i even more efficacious. For example, breast, non-small cell lung, and other cancers have National Comprehensive Cancer Network-approved indications for checkpoint inhibitor use in combination with chemotherapy , 60. TheThis study had several limitations. First, this was a single-institution, retrospective analysis with a relatively small sample size. Second, some patients were treated with a variety of PD1 therapies and/or other agents  too varied to account for in our analyses. Additionally, this study is limited in that over 50% of the patients who received immunotherapy did not have tumors that were tested for PD1 or PDL1 status; therefore, our results are only representative of a small group of sarcomas. The actual percentage of sarcomas expressing PD1 and/or PDL1 is also unknown, with studies suggestion that 4–12% of sarcomas have positive expression of these markers , 65. It This retrospective study suggests activity of PD1i in a pretreated cohort of advanced sarcoma patients, particularly in those with PD1 positive tumors. Our results highlight the importance of further research to better identify the optimal target population and biomarkers of response.Additional file 1. Rates of survival analyzed by treatment regimen. Kaplan-Meier graphs are shown demonstrating the progression-free (A) and overall (B) survival rates of each of the denoted groups while on PD1i. Each hash mark denotes when a single patient was censored from analysis. Patient groups were compared by log-rank (Mantel-Cox) analysis.


Variation in γ was most strongly associated with differences in photosynthetic capacity in penultimate leaves, rather than with flag leaf photosynthesis or canopy light penetration. Preliminary genome-wide association analysis identified nine strong marker-trait associations with this trait, which should be validated in future work in other environments and/or materials. Our modelling suggests canopy photosynthesis could be increased by up to 5 % under sunny conditions by harnessing this variation through selective breeding for increased γ.Suboptimal distribution of photosynthetic capacity in relation to light among leaves reduces potential whole-canopy photosynthesis. We quantified the degree of suboptimality in 160 genotypes of wheat by directly measuring photosynthetic capacity and daily irradiance in flag and penultimate leaves. Capacity per unit daily irradiance was systematically lower in flag than penultimate leaves in most genotypes, but the ratio  leaves. We examined, for the first time, whether this suboptimal pattern differs within species, by measuring light capture and photosynthetic capacity in flag and penultimate leaves across 160 wheat genotypes. We found wide variation in suboptimal N distribution, mostly driven by variation in plants’ ability to move N from penultimate to flag leaves as flag leaves develop. Selecting for less-suboptimal N distribution could increase canopy photosynthesis by several percent. Yet croxylation  and photg leaves . This is (Am/id) . Real ca (Am/id) , 2012, iAm distribution and its implications for fitness have long been debated, with no clear resolution. Hypotheses include systematic depression of stomatal conductance in upper canopy locations by water transport limitations  in 160 genotypes of wheat grown under field conditions in eastern Australia, and used a genome-wide association study to identify preliminary genetic markers linked to variation in the ratio of capacity per unit irradiance between flag and penultimate leaves (γ). Our data—the first survey of within-canopy variation in capacity per unit irradiance across diverse genotypes, and also the widest direct survey to date of photosynthetic capacity across genotypes of any given species grown under field conditions—revealed more than 2.5-fold variation in γ across genotypes, and identified seven chromosome locations potentially linked to this variation.Here we report the first assessment of genetically linked variation in this trait across diverse germplasm. We measured Wheat was planted in 2 × 6 m plots with five sowing rows per plot. Two weeks before measurements began, access lanes were mowed between ranges of plots, leaving each plot 2 × 4 m in size for measurement and later harvest. Two to three buffer rows and ranges were planted at the outer margins of the planting area. Two hundred fifty genotypes were planted, with two plots per genotype. Two hundred fifty plots (one per genotype) were planted in a block of 17 rows × 16 ranges, including one range of buffer. Another 250 plots (a second replicate for each genotype) were planted in an adjacent block immediately south-east. Triticum dicoccum, 100 primary synthetic wheats with their original genome donors, synthetic-derived materials developed from crosses of primary synthetics with Indian and Australian cultivars and over 1000 fixed hexaploid progeny of T. dicoccum crossed with hexaploid wheat. Many of the derived genotypes are high yielding, semi-dwarf varieties. We included 34 lines from the CSIRO four-way MAGIC population. This population was developed from four Australian commercial parents , each having a low co-ancestry, by intercrossing the parents to maximize genetic diversity and recombination. Each single seed was then selfed to produce pure lines  population created by CSIRO. The 119 Sydney lines were selected from a larger population that included 160 genotypes of DNA of a subset of 118 of the 119 Sydney genotypes was extracted following the CTAB described by 2 assimilation under saturating light and CO2 concentration and denoted Am. It is important to distinguish Am from Vcmax and Jmax . We chose to measure Am rather than Vcmax or Jmax because the latter require response curves of A vs. intercellular CO2 concentration (ci) and A vs. photosynthetic photon flux density (PPFD or i), which are very time-consuming and would have reduced throughput by nearly an order of magnitude, making this study impossible with the resources available to use. In this context it is worth noting that, although Vcmax and Jmax provide more information than Am, Am does have some advantages: because Vcmax and Jmax are inferred from models fitted to A vs. ci and A vs. i curves, inferences of Vcmax and Jmax are laden with assumptions of those models, and also assumptions used to infer ci itself from gas exchange measurements. By contrast, Am is a direct measurement of actual photosynthetic potential.We measured photosynthetic capacity, defined here as the net rate of COAm using OCTOflux, an open-flow single-pass differential gas exchange system with eight leaf chambers (5 × 11 cm). This system is described elsewhere  and mixed in a large buffering volume containing a powerful fan , before splitting into eight sample streams and a reference stream. Each sample stream runs through a mass flow meter  to a leaf chamber and back, and then solenoid valves are used to either vent the stream to the atmosphere or direct it through the sample cell of a differential infrared gas analyzer . One half of the reference gas stream runs through the IRGA reference cell; the other half is either vented to the atmosphere or directed through the sample cell to match the IRGA. The system is interrogated and controlled with a Microsoft Excel program that communicates with instruments via VB.NET interface functions that communicate with USB DAQ boards  and via an RS-232 serial connection (to the IRGA).We measured Am in both the flag and penultimate leaves (the leaf rank immediately below the flag leaf) of the same tiller, for two tillers per plot and two plots per genotype. Tillers were cut in the field, immediately recut under distilled water, returned to the laboratory (about 1 km away) and kept in darkness for 0–30 min before measurement. Each leaf was enclosed in a leaf chamber and exposed to saturating PPFD (1700 μmol m−2 s−1) and CO2 (4800–5000 ppm), and then allowed to adjust to these conditions until net CO2 assimilation rate (A) was stable . Chamber flow rate was 1 L min−1 and leaf temperature averaged 26.0 ± 1.7 °C (mean ± SD). Photosynthetic induction was assessed by continuously monitoring A of one leaf. Once gas exchange rates stabilized, the gas stream from each leaf chamber was sequentially passed through the IRGA sample cell for 1 min, and Am was taken as the average of A over the last 40 s. One measurement cycle (eight leaves plus matching) took 28.7 ± 5.8 min (mean ± SD). The leaf segment enclosed in each chamber was marked and photographed on a template, and its area was measured using ImageJ and then used to correct calculated gas exchange rates.We measured 2 levels to ensure that photosynthesis was truly saturated by CO2, obviating the need to measure CO2 response curves to eliminate effects of varying stomatal conductance. This greatly increases throughput, but with two trade-offs. Firstly, it gives a value only for Am, and not more specifically for carboxylation capacity (Vcmax) and electron transport capacity (Jmax). However, the ratio of Vcmax and Jmax is highly conserved, both within and across taxa -limited at these high CO2 levels, necessitating validation to ensure that the assimilation rate under such conditions is a reliable proxy for the ‘true’ maximum value of A, which occurs at the transition between limitation of photosynthesis by RuBP regeneration and TPU. We validated our estimates of Am by comparison with values inferred from A vs. ci curves made on a subset of the leaves used in this study, and found high correspondence between the two values   was not controlled by OCTOflux. To minimize temperature fluctuations, the system was operated in an air-conditioned workshop. To correct Am values to a common temperature of 25 °C, we determined the relationship between Am and T  in each of 10 leaves using a calibrated IRGA . For each leaf, the function Am(T) = a·exp(b·T) was fitted to the data, Am at 25 °C (Am25) was computed as a·exp(b·25) and each Am value was expressed relative to its Am25 (Arel = Am(T)/Am25). Arel values were compiled across leaves for each temperature, the function Arel(T) = a′·exp(b′·T) was fitted to them  above the canopy, and we used handmade ceptometers (‘PARbars’) placed between the flag and penultimate leaves, and below all leaves, to measure id above the penultimate leaf and below the canopy, respectively. The PARbars are described elsewhere (id in 34 plots simultaneously (two ranges of 17 rows) each day. The equipment was moved south-east to the next pair of ranges after sunset each day.We used a quantum sensor  placed above the canopy to measure daily irradiance . Mean ± SE for head-layer transmittance was 0.841 ± 0.027. We subsequently calculated flag leaf daily irradiance (idf) as above-canopy PPFD multiplied by 0.841.Photosynthetic photon flux density measured above the canopy will not generally be equal to that experienced by flag leaves, because flowering heads above the flag leaves intercept some light. To estimate light interception by heads, we placed PARbars above the flag leaves and below the heads in six plots, each of a different variety selected from the CSIRO lines grown in this study, grown in 2018 near Canberra. We calculated transmittance through the ‘head layer’ as the ratio of 2). Finally, we computed LAI as the product of total leaf area per tiller and number of tillers per ground area. We calculated canopy transmittance, τcanopy, as the ratio of id(bottom) to id(top) , and computed the effective canopy extinction coefficient, kcanopy, as ln[1/τcanopy]/LAI.We measured leaf area index (LAI) in each plot as follows. We harvested five tillers per plot within 3 days of the photosynthesis and ceptometry measurements made in that plot, measured the total area of all leaves on those tillers using a leaf area meter  and divided the result by five to give average leaf area per tiller. We then measured the number of tillers per ground area in each plot by harvesting all tillers on a 1 m length of a single row, counting them and multiplying the result by the ratio of planting row length per plot (5 rows × 4 m length per row = 20 m) to area per plot (8 m2 concentration (ci) was 280 ppm . Internal standards with known nitrogen percentage  and known isotopic composition were used, or were calibrated against primary isotope standards from the IAEA against VPDB for δ 13C: IAEA-CH-6 (−10.449‰) and Beet sucrose (−24.62‰). The precision of the analysis was below 0.12‰ for δ 13C and below 0.17 % for %N analysis.Leaf segments used for gas exchange were sampled for nitrogen and carbon isotope analyses. Samples were oven-dried at 80 °C overnight. 1.3 mg of homogenously ground material was weighed into tin capsules  and inserted into a FlashHT modified to a dual reactor setup (reduction reactor at 680 °C and oxidation reactor at 1000 °C), coupled to a Delta V Advantage isotope ratio mass spectrometer (IRMS) by a Conflo IV interface . δ 13C on photosynthetic capacity using linear mixed models with genotype as a random effect, fitted using the lme4 package  of Am2, Amf, Amf/Am2 and if/i2. All random effects were assumed to be normally and independently distributed and genotype was considered as both a fixed effect to estimate BLUE and as a random effect to estimate heritability as σ2g/(σ2g + ν/2), where σ2g is the variance component of genotype, and ν is the mean variance of a difference of two BLUE (http://pngu.mgh.harvard.edu/~purcell/plink/) to maintain SNPs with call rates greater than 40 % (http://cnsgenomics.com/software/gcta/) following the procedure of μ), fixed SNP effects and the genomic relationship matrix (GRM) to account for population structure. Thus, the model used to explain population structure was y = μ + SNP + random(GRM), where y represents population structure, μ the overall mean, SNP the fixed SNP effect and GRM the genomic relationship matrix. Following the linkage disequilibrium analysis those marker/trait associations with a −log10(P) value > 4 were retained as significant.We tested for effects of leaf N and δ  package  to computwo BLUE . Polymorγ, we used variance partitioning analysis as proposed by relative importance (R) of variation in each of several variables, x1, x2, ... xn, for driving variation in their sum, To identify the most important drivers of the observed variation in the coordination of photosynthetic capacity with light environment, as gauged by our phenotyping parameter xi, xj)| is the absolute value of the covariance of xi and xj. In the present context, the variables of interest are the components of γ: Amf, Am2−1 and id2/idf. The natural logarithms of these terms together add up to ln(γ). Thus, we applied Equation x1 = ln(Amf), x2 = ln(Am2−1) and x3 = ln(id2/idf).where |cov(Am and daily irradiance (id) in 160 genotypes of wheat. This included Am in 1300 leaves—the flag and penultimate leaf on each of 650 individual tillers, for four tillers per genotype —and id above both flag and penultimate leaves and below all leaves in 320 plots (two per genotype). Because above-canopy id varied from day-to-day, we express all values relative to above-canopy id, and thus report only the ratio of id between flag and penultimate leaves (idf/id2). Original data for this ratio, as well as for flag- and penultimate leaf photosynthetic capacity, Zadoks score and yield, are provided for each genotype in We measured Am was greater in flag leaves  than in penultimate leaves (Am2 = 30.35 ± 0.37 μmol m−2 s−1)  = 64.3, P < 0.001), and the two Am values were positively but weakly correlated  averaged 1.21 ± 0.02, but the ratio of daily irradiance in these two locations (idf/id2) was greater, at 1.41 ± 0.01  = 168, P < 0.001). As a result, capacity per unit irradiance was smaller in flag leaves than in penultimate leaves, such that the ratio of capacity per unit irradiance between flag and penultimate leaves, or γ ([Amf/idf]/[Am2/id2]) was less than unity, in 137 of 160 genotypes  = 1.23, P < 0.05). Capacity per unit irradiance was weakly correlated between flag and penultimate leaves in the same tiller .enotypes , and γ d2 m−2; seeτcanopy = 0.286 ± 0.038; seekcanopy = 0.55 ± 0.13 m2 m−2; mean ± SD). kcanopy was uncorrelated with γ[see], indicating that differences in the coordination of photosynthetic capacity with light were not strongly associated with differences in canopy structure that influence light penetration.Leaf area index varied among genotypes (mean ± SD: 2.39 ± 0.57 mγ in driving variation in γ across genotypes. We found that penultimate leaf photosynthetic capacity was by far the most important driver of variation in γ (relative importance [R] = 0.467)—more than twice as important as id2/idf (R = 0.225) and more than 1.5 times as important as flag leaf Am (R = 0.308)  .Z) contributed to γ, by regression analysis. γ was not significantly correlated with Z , the correlation was even weaker .Because the phenotyping campaign extended over 12 days, and also because genotypes may differ in flowering time and therefore in the timing of shifts in resource allocation among leaf layers, we also tested whether phenology  > 4 will need to be confirmed in other materials and environments, and thus should be considered preliminary. Nevertheless, there are markers on chromosomes 1B, 6B and 7A, as well as several unmapped markers, that had a positive effect on Am2 that could potentially be targeted in plant breeding to improve canopy photosynthesis.The heritability of photosynthetic capacity varied by trait, with the highest value of 0.425 observed for penultimate leaf photosynthetic capacity, γ . % gain approached zero in genotypes with γ close to 1.0  or 0.0–4.0 %  if photosynthetic N were redistributed between penultimate leaves and flag leaves so as to maximize the sum of daily mean assimilation rates at both positions . Differee to 1.0 . Before ariation . For cloariation .Am in flag leaves, and was significantly but weakly related to Am in penultimate leaves  and in both leaf classes combined  [see]. Similarly, the difference in δ 13C between flag and penultimate leaves was very weakly related to γ  [see]. Finally, yield was also unassociated with γ ; excluding one low-yielding outlier on the basis of a significant Grubbs test (P = 0.01), the relationship weakened further  [see].Leaf N content was not significantly related to Am) in 160 genotypes of wheat—a wider range of genetic diversity than previously examined for this trait in wheat, to our knowledge (cf. 108 accessions (γ (the ratio of photosynthetic capacity per unit daily irradiance in flag and penultimate leaves). Our results extend and clarify earlier reports of poor coordination between photosynthetic capacity and the local light environment , or by using genotypes with high γ as genetic source material for crossing. Further pre-breeding advancements could also arise from intensive study of genotypes with contrasting γ to determine the underlying physiological and genetic mechanisms. A challenge facing any attempt to harness variation in γ will be to phenotype this parameter in large breeding populations. Our direct approach would be impractical for large populations with many hundreds of lines. Higher-throughput proxies could be helpful; for example, leaf N content could substitute for Am, and differences in capacity per unit irradiance between canopy layers might be reflected in differences in carbon isotope discrimination (δ 13C). However, we found poor correspondence between Am and leaf N [see], and between γ and the difference in δ 13C between flag and penultimate leaves [see]. The development of higher-throughput methods to estimate γ is an important goal for future research.Our phenotyping parameter, canopy-scale source strength rather than single-leaf photosynthesis, as noted previously .Our findings do not discount the importance of sink strength. Indeed, the activity of sink tissues and their effect on photosynthesis remain poorly studied, in part due to a lack of phenotyping methods that can measure sink development at appropriate scales  had more nearly optimal distributions of photosynthetic capacity (larger γ) [see]. We found instead that the strongest driver of variation in γ was the photosynthetic capacity of penultimate leaves, with Am in flag leaves a distant second and light penetration to penultimate leaves the least important  for every measured tiller and found no relationship between γ and Z.The importance of considering canopy-scale photosynthesis in addition to single-leaf photosynthesis has long been acknowledged  a practical upper limit on the magnitude of leaf N content and/or leaf mass per unit area : wheat flag leaf Am is commonly well over 40 μmol m−2 s−1  to quantify deviations from optimal N partitioning, and our preliminary GWAS identified several molecular markers potentially associated with traits governing variation in γ. Our modeling predicts that potential increases in canopy-scale carbon capture are significant and would contribute to increased yield potential. Our results also support recent evidence that efforts to improve crop photosynthesis must look beyond the flag leaf, and consider heterogeneity within the canopy.The present study has shown that genetic variation exists in the coordination of photosynthetic capacity with local light environment within wheat canopies, and that total photosynthesis for flag and penultimate leaves combined could be increased by harnessing this variation through directed breeding. We characterised a novel metric , canopy transmittance and effective canopy extinction coefficient.Figure S8.γ vs. kcanopy.Figure S9.γ vs. Zadoks score.Figure S10. Manhattan plot for genome-wide association analysis (GWAS) results.Figure S11. Photosynthetic capacity vs. leaf N content.Figure S12. Difference in δ 13C vs. γ.Figure S13. Grain yield vs. γ.Table S1. List of genotypes.Table S2.γ and its components, Zadoks score and yield for 160 genotypes.Appendix S1. Explanation of simulation methods.plaa039_suppl_Supplementary_Figure_S1_S12Click here for additional data file.plaa039_suppl_Supplementary_Table_S1Click here for additional data file.plaa039_suppl_Supplementary_Table_S2Click here for additional data file.