Insertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection.We have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines  and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35 bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels  on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~ 3%), high call rate (~ 98%), and high reproducibility (> 95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels . This “proof of concept” tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50 K SNP arrays.We efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix® Axiom® array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale. Arabidopsis thaliana [In the past decade, there has been growing evidence that structural variations (SVs) are pervasive within plant genomes –9. Inserthaliana , potato thaliana , pigeon thaliana , and sorthaliana . These rthaliana , 19–25 .In maize, BAC sequence comparison first revealed that gene and transposable element content greatly vary between inbred lines , 27. WhoMethods based on SNP array experiments have been developed to detect CNVs and were shown to be more affordable and with higher throughput than CGH arrays –33 . DidWith the emergence of massive parallel sequencing, new methods have been developed to detect structural variations based on the alignment of resequencing reads onto a high quality reference genome sequence. Among these, three have been mainly used : (i) theThe “assembly” (AS) method is able to detect all types of SVs of any size, but is also the most cost and computation-intensive. It is the only method able to detect large insertions with precise breakpoint definition. However, the assembly of large and complex genomes such as maize remains very expensive and computationally intensive, despite recent progress in this area –21. TherTo design a comprehensive InDel genotyping array, we first discovered a set of InDels which would be representative of the maize temperate germplasm. We already had access to sequence data for the European flint line F2, and we benefited from a first set of 42,330 F2-specific sequences, larger than 150pb and totaling 16Mbp. This dataset was derived from the de novo assembly of an F2 paired-end that failed (at least for one read of the pair) to align onto the B73 AGPv2 sequence, and which were totally devoid of coverage by B73 reads . We did not use the “no map” approach for InDel discovery on PH207 and C103, because this approach did not give access to breakpoint resolution, did not allow the discovery of InDels without specific sequence, and was almost redundant with the assembly approach.We first aligned F2, PH207, and C103 sequences against the B73 reference genome sequence in order to detect deletions. Here, the term “deletion” does not reflect any underlying biological process of DNA excision but refers to a sequence of at least 100 bp present in the B73 genome at one locus and absent in another line at the same locus. Deletions were detected for the three lines simultaneously using the “genotyping” option of PInDel , generatNext, we generated a draft genome assembly for each of these lines, which was used as a template for alignment of B73 reads to detect insertions relative to the B73 reference genome , comprising 52,175 deletions and 89,150 insertions. These regions were then used for the design of genotyping probes.Large InDels can be efficiently genotyped with a SNP array using a combination of two types of probes: (i) “external” probes, which target breakpoints using the two flanking sequences of a given InDel (BP probes), and (ii) “internal” probes, which target presence/absence regions (PARs) within the internal sequence of InDels on polymorphic (OTV probes) or monomorphic sites (MONO probes). We define PARs as small portions of DNA sequence of at least 35 bp that were observed present or absent at the genome level, when comparing two individuals. They are thus suitable for the design of presence/absence genotyping probes. Ideally, each InDel should be called by two BP probes on either side and by multiple internal probes, regularly distributed along the internal sequence of the InDel Fig. a. Howevenot possible (18%), not recommended (33%), neutral (15%) and recommended (35%) by this automated pipeline (see Methods for details), leaving 33,430 InDels (51%) that could be targeted by at least one recommended probe.BP probes should behave like classical SNP probes where one allele corresponds to the presence and the other to the absence of the InDel. They are useful to explore the conservation of the localization of large insertion/deletion events across multiple individuals, even when no internal probe can be designed due to the absence of PARs. Among the 141,325 selected variants, 86,406 InDels  had breakpoints defined at base-pair resolution and were suitable for BP probe design. Four different breakpoint types were identified according to the presence of micro-homology and/or shorter non homologous sequence  in placeInternal probes, which should behave like “off-target” variants  where thnot possible (0.05%), not recommended (71%), neutral (14%) and recommended (16%), leaving 17,589 InDels (82%) which could be targeted by at least one recommended probe.For the design of OTV probes, we benefited from the availability of SNPs which had been previously identified from the alignment of resequencing data from a core collection of 25 temperate maize inbred lines against the B73-F2 maize pan-genome from . As a corecommended and allowed to target 62,875 InDels (79%).For the design of BP and OTV probes we could rely on Affymetrix® design pipeline to identify probes localized in PARs and thus suitable for the Affymetrix® Axiom® technology. For the design of MONO probes, we first had to identify such PARs within 141,325 InDels cumulating 133Mbp of sequence. We used sequence masking methods to exclude repeats based on similarity to known maize repeats or on occurrence of 17-mers found within the sequencing datasets we had for B73, F2, PH207, and C103 (see Methods). By doing so, we identified 122,972 PARs, representing a cumulated size of 27Mbp, corresponding to 20.3% of the initial size and allowing the possibility to design MONO probes for 79,987 InDels (56.5%). These PAR sequences were successfully used for the design of 25,735,797 MONO probes, among which 59% were scored as With this combined approach, we designed a total of 26,715,361 probes targeting 117,756 InDels, which represent a cumulated length of 250 Mbp including 27 Mbp of PARs  can only be targeted with either internal or external probes, but not both , and only one hit (for deletions), against the B73 reference genome sequence. We then considered their density with the objective to maximize the number of InDels that could be surveyed, as well as to have an even distribution of probes along targeted InDel sequences (see Methods). We then performed a second selection among not recommended OTV and MONO probes for 4,541 InDels that were still not targeted. After filtering some duplicated probes, we built a final array design containing 662,772 probes targeting 105,927 InDels that represent a cumulated length of 232 Mbp, including 25.9 Mbp of PARs.We used the Affymetrix® recommendations to select the 700,000 probes to be included in the final array, plus some other criteria depending on the probe type. Nevertheless, because of their added value, we decided to keep all BP probes as long as they had less than 3 hits on the B73 reference genome sequence. This first selection consumed 84,994 probes targeting 53,456 InDels, among which 70% could only be targeted by BP probes. Concerning OTV and MONO probes, we first selected The final array design allows genotyping InDels with various sizes, ranging from 37 bp to 129.7 kbp, with a median of 501 bp , 1311 inside a pseudo-gene and 2212 inside a transposable element. InDels and probe density varied across each chromosome  with the observed clustering of inbred lines based on fluorescence intensity and contrast of 445 inbred lines genotyped with the array. For BP probes, we expected at least two clusters corresponding to the individuals homozygous either for presence (“AA” or “BB”) or absence (“OO”). A third cluster could be observed when individuals were heterozygous individuals for presence/absence (“OA” or “OB” hemizygous) Fig. c. For OTWe observed also some unexpected clustering. For 57% of BP probes, we observed an additional off-target cluster  when the sequence was present suggesting the presence of single nucleotide polymorphisms at this position. Among these, we were able to distinguish two types of clustering Table . 4.7% ofFor 18% of OTV  with those from sequencing (GBS: Genotyping by Sequencing) for the 4 lines used for the discovery of InDels . Respectively, 97, 92, and 88% of the BP, OTV, and MONO probes had a genotyping result consistent with results obtained from BLAST alignments against our three draft genome assemblies and the B73 reference genome. We observed a strong asymmetry for concordance rates for internal probes (OTV and MONO) depending on whether the genotype has been called by sequencing as present or absent . This could be partially explained by the higher proportion of GBS present calls in F2 as compared to the three other lines since GBS present calls are more consistent with GBA than GBS absent calls. The median consistency rate of probes within InDels remained relatively high and stable, around 90%, independently of the number of probes per InDel  and 0 (absence) indicated that all probes displayed consistent genotyping for the corresponding inbred line  allelic frequency different from 0 or 1 (FreqDiff01), according to the number of probe interrogating that InDel Fig. . For InDi.e more complex rearrangement), rather than being genotyping errors. Additionally, 35% of InDels called by BP had their FW and REV probes classified differently (e.g. one as BP and the other as OTV). Altogether, these results suggest that some calling inconsistencies between probes within InDels could come from polymorphisms in the flanking sequence while some other could be due to local rearrangements in the genotyped lines as compared to the lines used for InDels discovery.Combining genotyping from multiple probes within InDels greatly improved reliability of InDel genotyping, since it allowed (i) to correct the individual genotyping errors due to polymorphisms within probe sequences, (ii) to reduce the missing data rate due to bad clustering or probes polymorphisms, and (iii) to remove probes displaying highly-divergent genotypes compared to other probes for the same InDel, due, for example, to a bad design of the probes. In order to evaluate the combining of genotypes of several probes on the accuracy of InDel genotyping, we simulated global genotyping error rates for InDels by assigning to each inbred line the most frequent allele, based on the average frequency over all probes from an InDel, with various genotyping error rates , except those for a couple of lines closely related to B73 and F2  the ‘accuracy’ of detecting InDels using whole genome assembly, with the detection of 89,150 insertions and 52,175 deletions from the comparison of three newly sequenced and assembled maize inbred line  genomes and the public maize B73 AGPv2 reference genome, (ii) and the ‘high-throughput’ genotyping utility provided by SNP arrays. This approach allowed us to genotype, for the first time, thousands of insertion/deletion variants, including PAVs, on a few hundred maize individuals. Genotyping cost per individual using the InDel array was at least 10–20 fold cheaper than any approach based on sequencing for a species with a genome as complex as maize, at a similar level of reliability . This genotyping cost did not include bioinformatics analysis. Calling SVs from a pan-genome of a species with a large and complex genome, such as maize, was time-consuming and required bioinformatics skills and large informatics facilities, which are costly and not available in all laboratories. In the contrary, the array could be analyzed rapidly on a laptop using a pipeline already implemented for analyzing SNPs and the Hom2OTV R script developed for analyzing MONO probes. Additionally, the array provided a wet-lab validation of the InDel discovery and allowed the removal of putative genotyping errors from sequencing (particularly for PAVs), due to incomplete or bad genome assembly, as we observed in our study. In the end, the probe content of the InDel array can be largely optimized, either to reduce the size of array (and therefore the cost), or to increase the number of SVs genotyped, without losing reliability  by filtering out under-performing probes, by strongly reducing the number of probes per InDel (2–3), and by removing false positive InDels. It would also be easy to design an array combining probes targeting InDels and more classical SNPs, outside of InDel sequences.With the use of breakpoint probes for both insertions and deletions, our approach overcomes some of the limitations of previous CGH or SNP array-based studies, which were only able to call deletions if a few successive probes had lower fluorescent intensity signals –33. UnteAlthough the use of BP probes is clearly the simplest way to genotype InDels using an SNP array, breakpoints are not always available (“no map” approach discovery) or “designable” with 35 bp probes, for instance, the cases where sequences of microhomology at breakpoint site were larger than 5 bp. In order to genotype the 52,471 InDels without breakpoints and explore the genetic diversity within InDels, we also designed 577,778 internal probes both on monomorphic and polymorphic sites in PARs for both insertions and deletions. To genotype PARs in InDel sequences using SNPs, we took advantage of the already available Affymetrix® algorithms to call Off-Target Variants (OTVs), which can detect variation of fluorescent intensity signals for a single probe  the genotypes obtained by array and by sequencing were highly consistent for BP probes (97%) and in a lesser extent with OTV and MONO probes , due to the fact that the genome assembly of sequenced lines were incomplete or incorrect, resulting in high error rates for absent calls using GBS; (ii) the average probe genotyping error rate was estimated at 3% ; (iii) the InDel genotyping errors could be greatly reduced by combining the genotypes of different probes within the InDels (0.02% for 5 probes); (iv) the genotyping results were highly reproducible between DNA replicates of F1 hybrids  and between inbred lines (94.8 to 99.4%); and (v) the call rate for individuals was very high (96 to 99%) and can be increased by combining the genotypes of the probes within the InDels .Our approach is promising as a method to genotype structural variations in maize, as well as other species with complex genomes. We obtained high metrics, comparable to classical SNP arrays, based on Affymetrix® Axiom® Technologies, even though InDels are more complex to genotype. First, call rates are high and quite similar to those obtained for SNP with the 600 K SNP Affymetrix® array (98% against 98.1% in ). NevertWe identified some inconsistencies between genotyping by array (GBA) and genotyping by sequencing (GBS) obtained by aligning probes against our genomes Table . These iWe also found that 20,574 InDels were monomorphic and present across all lines, suggesting they represented false positives from regions not assembled in our draft genomes. To reduce this false positive rate, we strongly advise to not only align B73 reads onto each draft genome assembly but to also align reads from each sequenced genome on each other and against itself. This would have several benefits: (i) it would allow to discover even more and higher-quality InDels, as each putative deletion discovered in one sample could potentially benefit from supporting reads from another sample; (ii) this would simplify the identification of InDels common to more than one genotype; and (iii) it would help to identify and eliminate false positive deletions by the alignment of each sample on its own draft assembly.Nevertheless, the use of incomplete draft genomes does not explain all discrepancies between genotypes obtained by sequencing and by array. First, these discrepancies could also be due to incorrect clustering and assignment of a genotype call (array errors). This was exemplified by some MONO probes classified as SNPs, although the clustering pattern looks like a MONO cluster with a large difference of fluorescence intensity between two clusters  was slightly higher than observed by sequencing a small set of diverse lines , 65. It 1 bp was These potential clustering errors, as well as the incorrect design of some probes, can explain some inconsistent genotypes for presence/absence between probes for the same InDel. Comparison of genotyping across different probes within InDels could help to identify and remove probes displaying highly discordant genotypes, due to errors originating from poor clustering or from poor design. Interestingly, some InDels showed reproducible inconsistent genotypes for presence/absence across their probes in several inbred lines  and C103 (Lancaster). For the F2 inbred line, see [http://www.novocraft.com) (default parameters). Samtools [http://broadinstitute.github.io/picard) version 1.48. PInDel [For the deletion discovery step, F2, PH207, and C103 paired-end reads were aligned against B73 AGPv2 genome sequence using novoalign version 3.01.01  was used to mask redundant regions by similarity using an iterative approach. First, “ALLPATHs-LG assembly” F2 insertions were masked with “ABySS assembly” F2 insertions (at least 95% of identity) to generate a non-redundant set of F2 insertions. Then C103 insertions were masked with F2 insertions (at 90% of identity), PH207 insertions were masked with C103 and F2 insertions (90%), and finally F2 no map specific sequences were masked with PH207, C103, and F2 insertions (90%).The multiple references and approaches used during the InDel discovery step led to a set of InDels with various levels of redundancy. Some “intra-tool” redundancy was found . These “ambiguous” calls were systematically identified using the Bedtools suite version 2.16.1  and elimTo identify presence/absence regions (PARs) within InDel sequences suitable for the design of “off-target” probes, we used the genometools Tallymer utility  version Breakpoints could be targeted by probes Fig. a providein-silico analysis to predict their microarray performance. A p-convert value, which arises from a random forest model intended to predict the probability that the SNP will convert on the array, was determined for all probes. The model considers factors including probe sequence, binding energies, and the expected degree of non-specific binding and hybridization to multiple genomic regions. This degree of non-specific binding is estimated calculating 16-mer hit counts, which is the number of times all 16 bp sequences in the 30 bp flanking region from either side of the SNP have a matched sequence in the genome. These scores were generated both for forward and reverse probes. A probeset is recommended if p-convert> = 0.6 and there are no expected polymorphisms in the flanking region. A probeset is neutral if p-convert> = 0.4, the number of expected polymorphisms in the flanking region is less than 3, and the polymorphisms are further than 21 bp of the variant of interest. Probesets not falling into these two categories are scored as not recommended. Probesets that cannot be designed are scored as not possible.All potential probes were evaluated in an Concerning OTV and MONO probes, we applied three successive filtering steps. First, we selected only probes classified as recommended and neutral based on their scoring, with no more than one hit on the B73 reference genome for deletion probes, and no hit at all for insertion probes. After this step, 204,213 OTV probes and 18,884,827 MONO probes remained. Secondly, only probes with more than 70% in PARs were kept. An additional filtering step was implemented specifically for MONO probes to optimize probe distribution along the targeted PARs. For this step, PARs were split in 75 bp windows using windowmaker (Bedtools) and the MONO probe with the highest p-convert value was selected for each window. If there were InDels with less than 4 MONO probes selected using 75 bp windows, these probes were eliminated and a second iteration was attempted, using 50 bp windows, followed by a last iteration with 25 bp windows. This generated 616,286 probes including BP and OTV probes targeting 108,24 InDels (90% of InDel selected for design). We completed the design by rescuing 6219 OTV and 53,441 MONO probes from InDels or PARs not targeted by any probes, bringing the total number of probes selected to 675,946 to target 109,292 InDel.At the last step, duplicated probeset were removed based on their sequence by Affymetrix® during the chip design procedure, leaving 662,772 probeset  corresponding to 1,404,570 different probes to be tiled on the array.For genotyping, 480 different DNA samples were extracted from leaves following a NaBisulfite method modified from , 76. TheEach type of probe had a dedicated algorithm , except for an empirically determined, more stringent heterozygous variance filter (AB.varY.Z.cut = 2.6). Example of clusters from each classification were visualized using the Ps_Visualization function , MonoHighResolution (MHR), and OTV).Algorithms used to convert BP and OTV were similar, as BP and OTV probes behaved like classical SNPs. For initial genotype calling, a priori (generic) cluster positions were used, since no information about expected positions was available. A first analysis was performed according to Affymetrix® recommendations. Secondly, the level of inbreeding was taking into account for a posteriori cluster definition, because of the high amount of inbred lines in the panel. This parameter took values from 0 for fully heterozygous to 16 for completely homozygous samples. For OTV and BP algorithms, an inbred penalty of 4  was applied to try to re-labelled probes that fall into categories: CallRateBelowThreshold (CRBT), HomHomResolution (HHR), NoMinorHom (NMH), Other and UnexpectedHeterozygosity, after the first cluster analysis  to re-labelled probes displaying higher-than-expected levels of heterozygosity. Finally, the new function called Hom2OTV was implemented to classified probes exhibiting two homozygous clusters, with primarily an intensity difference. This function determined if the intensity difference represents one homozygous cluster (InDel presence) and one OTV cluster (InDel absence), as we expected. There are no parameters in this function. The lower intensity homozygous cluster is recalled as OTV.BP and OTV probes that exhibited only two clusters (AA or BB and OTV) should fall into the monomorphic classification and be considered as not recommended. A new MONO algorithm was developed Fig. , becauseWe compared the genotyping for 479,027 probes from the InDel array (Genotyping By Array: GBA) with the genotyping from sequencing (Genotyping By Sequencing: GBS) of 4 inbred lines used to discover the InDels: B73, F2, PH207, and C103. Genotyping by sequencing was built from the alignment of probe sequences on the reference genome B73 and the de novo assembly of 3 inbred lines  with Blast software. Sequences were considered present in lines when the probes were aligned with less than 5% of mismatch or otherwise considered absent.Genotyping consistency for B73, F2, PH207, and C103 was calculated between GBS and GBA according to genotype calls “present” or “absent”, produced by GBS Table . For thii.e the proportion of lines where the average frequency across all probes is 0 or 1. We also calculated frequency of inbred lines that have a least one probe with an inconsistent genotype (FreqDiff01), i.e the proportion of lines where the average frequency across all probes is not 0 or 1. To evaluate the effect of the probe numbers on the frequency of lines inconsistent within InDels, we analyzed the variation of frequency of lines not fully consistent (FreqDiff01) with relation to the number of probes within the InDels, by estimating median and average FreqDiff01 for each probe count  that originated from different seed sources  the clustering observed were consistent with expectation (Table We estimated two kinship matrices between 362 lines using “identity by descent” estimators (IBD) based on 57,824 InDels and on 28,143 prefixed Panzea SNPs from the Illumina 50 K  Probes recommended for genotyping, B) Probes not recommended for genotyping. Table S3. Classification by the Affymetrix® pipeline of 163,278 OTV probes based on cluster number, separation, variance, and call rate. A) Probes recommended for genotyping B) Probes not recommended probes for genotyping. Table S4. Classification by the Affymetrix® pipeline of 414,500 MONO probes, based on cluster number, separation, variance, and call rate. A) Probes recommended for genotyping B) Probes not recommended for genotyping. Table S5. Effect of probe number within InDels on average percentage of missing data, of genotypes absent and genotypes not fully concordant. Table S6. Simulation of genotyping error rates for 362 lines and 10,000 InDels called by various numbers of probes with a probe genotyping error rate ranging from 1 to 10%. Table S7. Comparison of reproducibility between 5 DNA replicates of hybrid F1 according to probes type and observed clustering. Table S8. Mendelian inheritance of 12 hybrids F1 derived from 9 different parental inbred lines for 46,382 BP probes passing Affymetrix quality control and polymorphic. Table S9. Comparison of the reproducibility of InDels and SNP genotyping between 13 maize varieties replicated on 50 K Illumina SNP and Affymetrix® Axiom® InDel arrays.Additional file 2: Figure S1. Description of two approaches used to discover InDels using resequencing data of DNA. Figure S2. Number and complementarity of A) deletions and B) insertions regarding B73 reference genome discovered between F2, PH207 and C103 inbred lines and B73. Figure S3. Schematic representation of four different breakpoint types identified by PINDEL at InDel breakpoints according to the presence of micro-homology sequence or not in place of the deleted sequence. Figure S4. Distribution of probe number per InDel for 105,927 InDels genotyped with the array. Figure S5. Relationship between probes number genotyping the InDel and A) the InDel length B) cumulated length of specific sequence (PARs) within InDel. Figure S6. Variation of probes and InDels density across the 10 maize chromosomes. Figure S7. Three dedicated Affymetrix pipelines used for calling InDel polymorphisms from the fluorescent intensity variation of BP probes (A), OTV probes (B) and MONO probes (C). Figure S8. Example of clustering based on probes fluorescence (intensity in y-axis and contrast in x-axis), for 14 different classifications of probes assigned by the Affymetrix® algorithm. Figure S9. Example of clustering for 6 randomly probes in different classifications. Figure S10. Variation of the distribution of the average consistency rate (%) of InDels between expected and observed genotyping of probes according to number of probes within the InDel. Figure S11. Haplotype of two InDels genotyped with multiple probes (in column) for 362 individuals (in rows). Figure S12. Effect of average frequency of absence across 362 lines on consistencies between probes genotyping within InDels. Figure S13. Comparison of kinship between 362 inbred lines estimated with 57,824 InDels and with 28,143 SNPs from the 50 K Illumina genotyping array. Figure S14. Principal coordinate analysis on the genetic distance between 360 inbred lines from an association panel (B73 and F2 were excluded) estimated by A) 57,824 InDels and B) 28,143 SNPs.


Heart transplant (HTx) and left ventricular assist device (LVAD) implant are the best options for symptomatic end stage heart failure, but LVAD patients show lower rehabilitative outcome than HTx patients. To investigate the causes, we compared biomarkers levels and their association with rehabilitative outcome in 51 HTx and in 46 LVAD patients entering the same cardiac rehabilitation program. In both groups, routine biomarkers were measured at start (T1) and end (T2) of cardiac rehabilitation while homocysteine, leptine and IGF-1 were measured at T1 only. HTx patients had lower lymphocyte, platelets, glucose, total proteins and albumin at T1; differences with LVAD patients vanished during rehabilitation when new cases of diabetes were observed in HTx. By contrast, total cholesterol, LDL and HDL fractions, leptin and IGF-1 were higher in HTx patients. The increase from T1 to T2 in six-minute walking test distance, measure of functional rehabilitation outcome, was positively associated with homocysteine and IGF-1 levels in HTx patients. In conclusion, during rehabilitation care should be paid to the early occurrence of dyslipidemia and hyperglycemia in HTx patients, which also require a proper protein dietary support. IGF-1, dangerously low in LVAD patients, might contribute to their lower rehabilitative outcome. When end-stage heart failure patients are symptomatic despite optimal medical therapy, Heart Transplantation (HTx) is the best option in terms of survival or quality of life. However, HTx is contraindicated over 70 years of age and many patients die before a suitable heart becomes available. An alternative option is the long-term mechanical circulatory support, and the implant of a left ventricular assist device (LVAD) is always more extensively implemented, given the good results of clinical trials2. However, we recently showed a lower rehabilitative outcome in LVAD implanted patients than in HTx patients, even after correction for demographic and anthropometrics characteristics and for the duration of rehabilitation3. This suggests that other factors may be responsible for the better rehabilitative outcome in HTx patients. Understanding these mechanisms might help to better target the rehabilitation program according to the type of surgery, possibly improving the rehabilitative outcome.Cardiac rehabilitation improves functional capacity and health status both for patients after HTx or LVAD implant and its feasibility, safety and potential for benefit have been documentedMain aim of this study is to highlight the possible differences in hematological and biochemical markers between HTx and LVAD patients entering a cardiac rehabilitation period few days after surgery; secondary aim is to identify possible associations between the levels of the investigated markers and the rehabilitative outcome. In perspective, this type of information might help to improve the best clinical approach in LVAD and HTx patients, tailoring the rehabilitation program to their needsThis open-label observational study was carried out at the Cardiology Rehabilitation Department of the Don Gnocchi Foundation’s Santa Maria Nascente Institute in Milan, Italy, according to the principles of the Helsinki Declaration and Good Clinical Practice and in observance of anti-discrimination regulations and standard privacy procedures. The protocol was approved by the Institute’s Ethics Committee. The study was performed after all the patients involved gave their written informed consent.3, consecutively admitted to the Cardiac Rehabilitation Unit of our Institute as in-patients after HTx (n = 51) or LVAD implant (n = 46) along a period of two years. All patients were Caucasians admitted as in-patients the day of the discharge from the transplant Centre located in Milan. No exclusion criteria was established with the exception of refusing the consent.We considered the 97 adult patients of our previous study3). The groups also differed for the underlying heart diseases, being dilated cardiomyopathy more frequent in HTx patients, and post-ischemic cardiomyopathy more frequent in LVAD patients. Implanted LVAD were the Heartmate II device  and the Heartware system .Differences between groups in demographic and anthropometric characteristics are reported in Table During the study period, all the patients received a standard diet with controlled caloric support , delivering 20% dietary proteins, 30% fat, and 50% carbohydrates. The diet included fruits and vegetables. Dietary intake was checked by nurses.4. In the LVAD group the antithrombotic treatment included the combination of double antiplatelet therapy and oral vitamin K antagonist anticoagulants, in accordance with international guidelines5.All drugs taken during the study are reported in Table 6. It consisted of incentive spirometry, breathing exercises, sub-maximal gradually incremental endurance training at least 30 minutes/day, walking on treadmill or cycling on cyclette, as previously described3. Muscular activities included limbs flexion, extension and abduction, neck flexion and extension, trunk flexion, extension and rotation, performed with aerobic exercises. When necessary, a pointed physical therapy was focused on improving balance to obtain independent functional mobility.Both groups attended the same rehabilitation program. The program, which planned duration of at least 3 weeks, was continued throughout the period of hospitalization until discharge. The standardized and supervised rehabilitation program was performed in accordance with the international guidelines7. The test provided the distance walked in six minutes that we expressed as absolute distance in meters (6MWD) and as percentage of the predicted distance for an individual of the same sex, weight and height (6MWD%), according to the statistical model described in8. The test was repeated twice: upon study entry (T1) and the day of discharge (T2). The rehabilitative outcome was quantified as increase of 6MWD% from T1 to T2. The score of the Barthel scale was recorded at T1 and at T2 as measure of performance in daily-life activities.The six-minute walk test was performed to measure the functional exercise capacity, in accordance with the guidelines of the American Thoracic SocietyRoutine chemistry tests on urine and venous samples by UniCel DxC 800 Synchron  included measures of Creatinine, Glucose, Total Protein, Albumin, Cholesterol, Cholesterol HDL, Triglycerides, Vitamin B12, C-Reactive Protein, Folic Acid, Alanine Aminotransferase, Aspartate Aminotransferase, Gamma Glutamyl Transferase, Total Bilirubin and Homocysteine. The blood sample for homocysteine was kept on ice until the analysis. Red and white blood cells were counted by Sysmex XE-2100 . Blood samples were centrifuged at 3000 rpm for 10 minutes to obtain serum or plasma that were frozen and stored at −70 °C. Serum concentrations of Leptin and Insulin-like Growth Factor 1 (IGF-1) were determined by ELISA  with minimum detectable dose of 7.8 pg/ml for Leptin and 0.026 ng/ml for IGF-1.The clinical protocol included the collection of a spot urine and venous sample for the above measurements at T1 and at T2, with exclusion of Homcysteine, Leptine and IGF-1, which were measured only at T1, for the whole group of 97 patients. However, due to a procedural change in the way patients data were transcribed in their clinical records, measures of few biomarkers were available on a lower sample of patients. These markers were: Leptin and IGF-1, available on 63 of 97 patients; vitamin B12 and Folate, available on 55 patients; Homocysteine and Ferritin respectively available on 52 and 50 patients.LVAD and HTx groups were compared by two-tailed Mann Whitney U test at T1 and at T2 separately, with threshold for statistical significance at p < 0.05. To identify a statistical association between any of the markers measured at T1 and the outcome of the rehabilitation program, the Kendall’s tau correlation coefficient was calculated between each biomarker and the increase in 6MWD%. Positive coefficients mean that greater values of the marker at T1 are associated with higher functional increments at the end of rehabilitation.The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.Admission to cardiac rehabilitation occurred 42 ± 44 (LVAD) and 28 ± 15 (HTx) days after surgery; discharge occurred 35 ± 16 (LVAD) and 40 ± 17 (HTx) days after admission. Three HTx (5.9%) and twelve LVAD (26.1%) patients were diabetic at T1. The number of HTx diabetic patients increased to six at T2. Cardiac rehabilitation was interrupted for less than two weeks in two HTx patients due to graft rejection episodes. Four LVAD patients had complications  interfering with the rehabilitative programme or leading to its temporary interruption.Anaemia, indicated by mean values of red blood cells count and haemoglobin lower than the normality range, was detectable both at T1 and T2, without significant differences between LVAD and HTx groups  than the LVAD  group. The Barthel score was similar at T1, being 80 (80–85) points, as median (first-third quartile), both in HTx and LVAD patients. At T2, the score reached 100 (100–100) in HTx patients and 100 (95–100) in LVAD patients. The improvement of Barthel score after rehabilitation was +15 (10–20) points in LVAD patients, and slightly greater, +20 (15–20) points in HTx patients (p < 0.07).Table To the best of our knowledge, this is the first study comparing biochemical makers at admission of cardiovascular rehabilitation and their association with the improvements in rehabilitative outcome in heart failure patients treated with HTx or LVAD. We found some substantial differences between the two groups in inflammatory, haematological and nutritional markers: these differences only in part decreased after rehabilitation. We also found some associations between the final rehabilitative outcome and the biomarker levels before rehabilitation.Anemia and elevation of inflammation markers at T1 are common findings after heart surgery. However, it is unexpected that biomarkers of anaemia are similar in the two groups at T1 given the high frequency of haemolysis caused by the LVAD centrifugal pump. This is probably due to the recent technical improvements of LVAD devices. The lower lymphocyte count in HTx patients at T1 could be consequence of their immune-suppressive treatment, and the progressive reduction of immune-suppressive medications during rehabilitation may explain the minor difference between groups at T2. Also platelets count was lower in HTx patients at T1 but not at T2. This could be consequence of a slight decrease of platelets count in LVAD patients, due to their larger consume of low molecular weight heparin and other antithrombotic drugs to prevent pump thrombosis and stroke, or consequence of an increased breakdown of circulating platelets induced by shear stress and trauma caused by the rotor pump.3. The accurate monitoring of protein dietary intake during rehabilitation may have favoured an increase of protein levels in the HTx group, so that the difference between groups vanished at T2. This is clinically important because hypoalbuminemia is associated with poor outcome in advanced heart failure9. Probably because of the proper dietary intake administered during rehabilitation, mean levels of vitamin B12 were normal, without any signs of the decrease previously reported at the end of rehabilitation in heart surgery patients10.Total proteins and albumin lower in HTx patients Fig.  confirm 11 and ischemic heart disease was recorded as more prevalent in the LVAD group.Folate levels were higher in HTx patients at T1, while homocysteine concentrations were similar in both goups, but with high standard deviation in LVAD patients due to very high levels in some patients. Hyper-homocysteinemia is a cardiovascular risk factor involved in the pathogenesis of atherosclerosis12. IGF-1 levels were higher in the HTx group and the higher levels may have provided anabolic and cellular proliferative stimuli, which likely favoured the improvement of nutritional markers and skeletal muscle mass increase, positively contributing to the rehabilitation process. Leptin levels, higher in HTx patients, generally reflect body fat stores, being the adipose tissue the major source of circulating leptin. However, this cannot be the cause of their higher levels in our HTx patients, because both body mass index and body fat stores were higher in LVAD patients3. More likely, the prednisone treatment in the HTx group might have had an effect on leptin levels elevation because corticosteroids may stimulate leptin release from adipocytes13.IGF-1 levels were lower than the normality range in LVAD patients. This is a potentially harmful condition that may increase mortality14. More patients were treated with statins in the LVAD (19.6%) than in the HTx (7.8%) group, but the groups differed also considering only patients not treated with statins. After rehabilitation the difference between groups in LDL was no more significant, likely because of the beneficial effect of aerobic exercises performed during rehabilitation15. These results show that hypercholesterolemia tends to occur in HTx patients within one month after surgery, and physicians should take care of this clinically relevant issue since the beginning of cardiac rehabilitation. This is a challenging problem, considering that in HTx patients the diet is difficult to establish and has scarce influence on cholesterolemia, and that the treatment with statins is delicate because of the high incidence of myositis and rabdomyolysis16.The plasma lipids at T1 were higher in the HTx group probably because the immunosuppressive therapy with cyclosporine or tacrolimus induces hypercholesterolemia as side effect of calcineurin inhibitors15. The prevalence of diabetes mellitus was high in LVAD patients (26%) and the mean glucose plasma levels higher than in HTx patients. However, we also found a high incidence of new cases of diabetes mellitus in HTx after rehabilitation, so that at T2 the prevalence of diabetes was relatively high also in the HTx group (12%) and the difference vs. LVAD patients in glucose levels no more significant. The increase of blood glucose after rehabilitation in the HTx group is likely due to the prednisone therapy as part of immunosuppressive treatment. These findings warn cardiologists suggesting the need of immediate preventive dietary measures and strict blood glucose monitor in HTx patients receiving immunosuppressive treatment. HTx patients also had liver enzymes and bilirubin higher than LVAD patients, suggesting a possible hepatotoxic effect induced by immune-suppressive medications.Diabetes increases the risk of infections and vascular complications3.Homocysteine levels at T1 were associated with 6MWD% increments in HTx patients. The positive correlation coefficient indicates that higher was the biomarker at T1, larger was the functional increment. Patients with elevated homocysteine levels at T1 might have entered the cardiovascular rehabilitation program in an overall worst cardiovascular condition, responsible for a low performance at the six-minute walk test. Thus, an efficient rehabilitation program may have produced the larger percent increments in these patients. To support our hypothesis, we separately considered the 6MWD of HTx patients with homocysteine below or above 14 μmol/L (average level over the group). Patients with homocysteine >14 μmol/L walked a significantly lower distance than patients with homocysteine <14 μmol/L at T1 . This was not the case at T2, when the performance was similar in the two groups . From a rehabilitation perspective, this finding suggests that HTx patients with a poor 6MWD evaluation and with hyperhomocysteinemia are likely to retrieve most of their lost functional capacity with a standard cardiovascular rehabilitation program, while this is less likely to occur in LVAD patients, explaining why we previously reported a greater functional performance in HTx than in LVAD patients after rehabilitationIn HTx patients we also found a positive association between IGF-1 and 6MWD% increments. HTx patients had higher IGF-1 mean levels Fig.  and we m4 for selecting between HTx or LVAD treatments, and results might differ if other selection criteria are followed. Furthermore, results at T2 could not be extended to patients following different types of cardio-respiratory programs and, since all our patients completed the cardiac rehabilitation , it was not possible to separately quantify the effects of cardiac rehabilitation from the effects of a 3-week recovery period after surgery.As a limitation, patients were surgically treated in a single cardiac transplant centre based on the ISHLT guidelinesIn conclusion, our work suggests that end stage heart failure patients treated with HTx or LVAD device may require different dietary and pharmacological approaches to better focus the cardiac rehabilitation on their needs. Care should be paid to promptly detect the early occurrence of dyslipidemia and hyperglycemia, or renal distress, in HTx patients (probably favoured by their immune suppressive therapy), modifying their diet or tailoring pharmacological treatments during rehabilitation. Furthermore, a good protein dietary support should be provided particularly to HTx patients. Finally IGF-1 seems to be an important biomarker to be monitored in LVAD patients, where it appears to be dangerously low, possibly contributing to the lower functional outcome in these patients.


Average firing rate determined the ATP cost across firing patterns by controlling the overall number of spikes. Varying intraburst frequency or spike number in each burst influenced the metabolic cost by altering the interactions of inward and outward currents on multiple timescales, but temporal pattern contributed substantially less to the metabolic demand of neural activity as compared to average firing rate. These predictions should be considered when interpreting findings of functional imaging studies that rely of estimates of neuronal metabolic demand, e.g., functional magnetic resonance imaging.Thalamocortical (TC) relay cells exhibit different temporal patterns of activity, including tonic mode and burst mode, to transmit sensory information to the cortex. Our aim was to quantify the metabolic cost of different temporal patterns of neural activity across a range of average firing rates. We used a biophysically-realistic model of a TC relay neuron to simulate tonic and burst patterns of firing. We calculated the metabolic cost by converting the calculated ion fluxes into the demand for ATP to maintain homeostasis of intracellular ion concentrations. Most energy was expended on reversing Na Differences in metabolic demand between these firing modes are critical to interpreting functional brain imaging methods that infer neural activity from related metabolic mechanisms, for example functional magnetic resonance imaging (fMRI). Specifically, in vivo recordings suggest a tight correspondence between blood-oxygen-level dependent (BOLD) fMRI signals and neural activity9. However, it is not clear how the temporal pattern of neural activity, as opposed to the average firing rate of neurons, contributes to metabolic demand, and therefore might influence measures in functional brain imaging.Thalamocortical (TC) relay neurons exhibit two distinct firing patterns - tonic and burst2+ entry, and vesicle cycling11. An energy budget of neural computation showed that most signaling-related energy was expended to pump Na+ and Ca2+ ions out of cells against their electrochemical gradients10. By converting ion fluxes into the number of ATP molecules, the metabolic cost of APs was determined in different cell types and species, and the effects of AP shape12, temperature13, channel densities and kinetics14, cell size15, and dendritic nonlinearity16 were considered. The metabolic cost of a single AP was dependent on the firing frequency16, and computational models predicted that metabolic cost per spike varied inversely with the firing rate during spike frequency adaptation17. Indeed, experimental recordings made in lateral superior olive, hippocampal CA1 region, and cerebral cortex indicated that the kinetics of ATP consumption and regeneration of energy equivalents, including nicotinamide adenine dinucleotide, flavin adenine dinucleotide, and O2 concentration, were correlated with the frequency of neural activity19.Processes consuming metabolic energy include maintaining rest potential, restoring ionic concentration gradients after action potentials (APs), reversing transmitter-evoked ionic fluxes through postsynaptic receptors, neurotransmitter recycling, reversing presynaptic AP-triggered CaThe objective of this study was to estimate the metabolic cost of different temporal patterns of neural activity across a range of average firing rates. We used a computational model to simulate tonic and burst patterns of firing in TC relay neurons. Our results revealed that the metabolic demand to restore ionic concentration gradients during neural activity was determined by average firing rate, and the pattern of activity contributed little to the energy cost.20. We examined the metabolic cost of neural activity in the cell body, as due to accessibility, this was the common site of recording in most experiments. There were three compartments in the soma , delayed rectifier K+ (IKdr), T-type Ca2+ (ICaT), hyperpolarization-activated cation (Ih), slow K+ (IKs), Na+ linear leakage (INaL), and K+ linear leakage (IKL) currents, and membrane capacitance (Cm). The details of the model followed McIntyre et al.21, and the model replicated a wide range of electrophysiological properties of TC neurons.We used a computational model to simulate different temporal patterns of neural activity in TC relay neurons. The model included a dendritic tree, a cell body, and a myelinated axon, and its morphology was reconstructed from a filled TC cell from rat ventrobasal nucleusoma Fig. , which w+ and Ca2+ ions out of the cell. Since the Na+/K+ ATPase pump did not differentiate between Na+ channel subtypes, we determined Na+ influx by summing INaL and INaf10. Ca2+ entry during APs or membrane hyperpolarization occurred through T-type Ca2+ channels, and to restore and maintain intracellular Ca2+ levels, the Ca2+-ATPase or Na+-Ca2+ exchanger pumped the Ca2+ entry out of the cell11. Therefore, the calculation of ATP consumption included the Na+ influx during APs, the Na+ influx at rest, and Ca2+ entry.The metabolic cost of different patterns of neural activity was determined by the number of ATP molecules expended to pump Na+ entry was computed by integrating the area under INaf(t) and INaL(t) curves. Since one Ca2+ ion has two positive charges, the Ca2+ entry was computed by integrating ICaT(t) curve, divided by 2. The Na+/K+ pump hydrolyzes one ATP molecule for every three Na+ ions extruded, and the Ca2+-ATPase or Na+-Ca2+ exchanger consumes one ATP for every Ca2+ ion extruded11. Thus, the number of ATP molecules consumed in each compartment was calculated by: com was the membrane area of the compartment (ionic currents I were current densities), NA was Avagadro’s number, and F was Faraday’s constant. The total metabolic cost was the sum of the ATP consumed in each of the three somatic compartments. This value was then divided by the time duration of the simulation to get a rate of metabolic energy consumed (in mol/s) during different rates and patterns of neural firing.The total Na2 at rest, there were still ion fluxes through relevant channels. Specifically, the Na+ influx mainly occurred through Na+ leakage channels (INaL = 1.10 × 10−3 mA/cm2), and the fast Na+ current (INaf = 3.17 × 10−6 mA/cm2) made little contribution. The Ca2+ influx occurred through T-type Ca2+ channels (ICaT = 1.65 × 10−4 mA/cm2). To maintain the resting potential, the Na+/K+ and Ca2+ pumps reversed these ion fluxes to maintain the concentration gradient for each ion, which consumed the ATP at a rate of 8.7770 × 107 mol/s. 81.51% of the ATP was consumed to reverse the Na+ leak, 18.31% was to pump the Ca2+ ions, and less than 1% was consumed to reverse leakage through the fast Na+ channels.The resting potential was −70.40 mV in TC model neuron, which was an emergent property dependent upon all conductances and reversal potentials that were represented in the model. Although the net membrane current was 0 mA/cm22, and the simulations were run with a time step of 0.025 ms. We quantified the metabolic cost of five temporal patterns of neural activity, which are illustrated in Fig. faver and intraburst frequency fintra were used to quantify the burst patterns, and faver was used to quantify the tonic patterns. faver referred to the average firing rate during the whole stimulation train, and fintra was the firing rate within each burst rst Fig. . We usedstim to the cell body to generate the five firing patterns. We recorded transmembrane potential and transmembrane currents in the soma. We examined the effects of faver and fintra on the metabolic cost of each pattern of firing. Pulse amplitude was 40 nA and pulse width was 0.1 ms, which were suprathreshold for activation of APs in the cell body as faver or fintra varied. The current pulses controlled the firing patterns in the cell body while themselves producing little effect on the metabolic cost of neural activity. Thus, this approach allowed us to quantify how the average firing rate and temporal pattern influenced the metabolic cost of neural activity.We applied depolarizing pulse trains ITonic and burst firing in TC relay neurons is typically generated by a large number of synaptic inputs arriving at the dendrites. We conducted additional simulations to quantify the metabolic cost of the five firing patterns generated by synaptic inputs, rather than by current injection into the soma. Both approaches – synaptic inputs distributed across the dendritic tree and somatic current injection – generated identical patterns of neural activity  in TC neurons3, and also results in burst patterns of firing. During Ca2+-mediated spikes, the intraburst frequency fintra was dominated by the rebound depolarization, and we only examined the effects of faver on the metabolic cost of neural activity. We applied constant hyperpolarizing current pulses to the cell body3 and 50 ms pauses in the hyperpolarizing current  resulted in low-threshold rebound spikes , the inter-pulse interval was long enough that there was little change in the INaf underlying the two spikes , the afterpotentials attenuated the INaf underlying the 2nd spike, thereby reducing total Na+ influx and the ATP cost of burst patterns.At rns Fig. , left. Arst Fig. , left. Tval Fig. . The Na+kes Fig. , and thun 5 Fig. , center,fintra on the metabolic cost of five temporal patterns of activity generated by applying depolarizing pulses to the cell body. Compared to varying faver, increasing fintra produced much smaller effects on the ATP demand. A 100 Hz increment in fintra resulted in less than 10% change of the mean ATP cost across firing patterns  but reduced the energy consumption at high fintra. With fintra ≤75 Hz, the tonic patterns consumed less ATP than burst patterns. Note that the higher ATP cost required to reverse Na+ leakage at faver = 10 Hz arose from the long inter-burst intervals. At faver = 100 Hz, the ATP cost of burst patterns decreased as fintra increased, and increasing number of spikes in each burst reduced the metabolic cost of neural activity , and, as well, the ATP consumed on extruding Ca2+ entry was increased from pattern 1 to pattern 5 . At high fintra, the effects of afterpotentials overpowered the effects of slow IKs, and the ATP cost per spike in each burst were all lower than that of tonic patterns . Since the ATP cost from ICaT was reduced from pattern 1 to pattern 5 at fintra ≥100 Hz  during rebound bursts following de-inactivation of ICaT, and with the same faver, increasing numbers of spikes in each burst resulted in more APs falling within afterpotentials  current, voltage-activated K+ (IM) current, and dendritic high-voltage activated Ca2+ current. Our simulations indicated that varying the intraburst frequency altered the slow K+ and T-type Ca2+ currents underlying the APs in each burst, and these currents interacted with fast Na+ currents to contribute to the small differences in metabolic cost between firing patterns. These findings highlight the importance of slow ionic currents in determining the spike pattern-related energy demand in the brain.Earlier studies predicted that temperature2+ influx via T-type Ca2+ channels is a key regulator of cellular excitability28 and controls the switch between tonic and burst firing modes in TC relay neurons1. Further, intracellular Ca2+ signaling powerfully regulates the metabolism in multiple subcellular process29. The Ca2+ ATPase and the Na+-Ca2+ exchanger both participate in the control of neuronal Ca2+ signaling11. The former consumes one ATP for extruding one Ca2+ ion and predominates at low Ca2+ load. The Na+-Ca2+ exchanger uses the energy stored in the Na+ gradient to drive Ca2+ extrusion, which predominates at higher Ca2+ loads. It exports one Ca2+ ion in exchange for the import of three Na+ ions, which requires additional ATP for the extrusion of extra Na+ by the Na+/K+ pump. Our simulations showed that Ca2+ extrusion made a significant contribution to the metabolic cost of neural activity when the T-type Ca2+ current was de-inactivated. Intracellular Ca2+ signaling also participates in ATP production in mitochondria27, and we did not consider this in our simulations.The Ca32. fMRI is based on the changes in local circulation and metabolism33, and is used to measure and interpret changes in brain activity during DBS. fMRI studies of thalamic DBS reported that the evoked BOLD responses were sensitive to the stimulus frequency36. Our studies showed that the average firing rate dominated the metabolic cost of neural activity in TC relay neurons, suggesting that metabolism-dependent functional imaging methods may be sensitive to stimulation frequency-dependent changes in neural activity. In vivo experiments also indicate that the temporal pattern of DBS is a factor determining its effectiveness39, but we did not observe the significant effects of firing pattern on ATP demand. However, this does not necessarily mean the temporal patterns may not differentially affect the BOLD signal. It is possible that the burst and tonic modes of firing evoke different neurovascular coupling mechanisms and lead to distinct blood flow responses, which would be apparent in fMRI.Quantifying the metabolic demand of neural activity influences the interpretation of functional brain imaging data through related metabolic mechanisms. Deep brain stimulation (DBS) is a neurosurgical method successful in treating movement disorders and alters the temporal patterns of neural activity40, and these differences may influence the overall metabolic cost of different patterns of firing. Third, we only considered the metabolic cost of tonic and burst patterns in a TC relay neuron model. These firing patterns may also be generated in other cell types, and their ionic currents and morphologies are substantially different than TC relay neurons, thus influencing the metabolic cost of firing activity. Fourth, the synaptic conductances were set such that when we used synaptic inputs to generate the desired firing patterns each presynaptic event evoked a spike in the soma. In our simulations there were only 165 excitatory synaptic inputs on the dendrites, and this represented only a small fraction of the total synapses on TC relay neurons, estimated to be 5584–8797, with ~65% being non-GABAergic42. Thus, the individual synaptic conductance for each modeled synapse was larger than that measured for single synapses on thalamic neurons, estimated to be 9–15 pS44. Finally, we did not include synaptic transmission in metabolic cost calculation, and this could influence overall ATP consumption. Pre- and post-synaptic mechanisms mediating synaptic transmission made a significant contribution to the overall usage of signaling-related energy in neurons and glia45.There were several limitations of our modeling approach. First, due to the limited data available on the magnitude and location of all the voltage-gated channels underlying the neural activity in TC cells, there may be over- or under-estimates of absolute metabolic costs, but this should not impact our comparisons of relative metabolic demands across firing rates and patterns. Second, we only examined the metabolic cost of firing patterns in the cell body. There are substantial differences in the energy efficiency of APs in the dendrites, soma, axon initial segment, and nodes of RanvierIn summary, we quantified the metabolic cost of different rates and patterns of activity in a model TC relay cell. The average firing rate determined the ATP demand of neural activity by directly determining the number of APs. The temporal pattern contributed to the metabolic energy by altering the interactions of ionic currents on multiple timescales, but made a much smaller contribution than average firing rate to the overall energy demand. These predictions are important for understanding information processing in TC relay neurons and critical for interpreting the signals from metabolism-dependent modalities of functional brain imaging .Supplementary Information


P < 0.05. ELISA validation supported the results of the antibody array. More importantly, most of these eleven cytokines, including IFNGR1, TNFRSF19L, GHR, SLAMF8, FR‐beta, and integrin alpha 5 were discovered to be elevated in gastric cancer serum samples for the first time in this study, suggesting that these proteins may serve as novel biomarkers for the early diagnosis and prognosis determination of gastric cancer.Gastric cancer is a leading cause of mortality due to neoplastic disease. Although early detection of gastric cancers can decrease the mortality rate, it remains a diagnostic challenge because of the lack of effective biomarkers. In this study, fifteen gastric cancer patients and ten healthy subjects were recruited to assess novel serum biomarkers for gastric cancer using antibody microarray technology. ELISA was utilized to validate the antibody array results. As a result, compared to the controls, eleven cytokines were found to be significantly increased in gastric cancer, including interferon gamma receptor 1 (IFNGR1), neurogenic locus notch homolog protein 3 (Notch‐3), tumor necrosis factor receptor superfamily member 19L (TNFRSF19L), growth hormone receptor (GHR), signaling lymphocytic activation molecule family 8 (SLAMF8), folate receptor beta (FR‐beta), integrin alpha 5, galectin‐8, erythropoietin‐producing hepatocellular A1 (EphA1), epiregulin, and fibroblast growth factor 12 (FGF‐12) with  However, this procedure relies on the skill of the operator, is invasive and expensive, and causes the patients discomfort and anxiety. Although there have been some advancements in the development of molecular biomarkers for the early detection of gastric cancer in recent years,There are proteomic alterations in the development and progression of diseases. Antibody microarrays are a novel technology rapidly detecting multiple proteins in parallel, and have been applied in many cancers, such as breast cancer and bladder cancer, for biomarker screening.22.1In this study, 15 individuals diagnosed with stage IA (T1N0M0) gastric cancer, based on biopsy specimen analysis and according to the Tumor Node Metastasis (TNM) classification, and who were hospitalized in the Gastrointestinal Surgery Department of Lishui Central Hospital  in 2017 were recruited. The patients had not yet received chemotherapy or radiotherapy. 10 individuals were recruited as control subjects. The clinical information of the study participants is shown in Table 2.2A human cytokine antibody array  that simultaneously detects 40 cytokines was utilized. Briefly, serum samples were incubated with 40 primary antibodies in the arrays overnight at 4°C. The next day, the arrays were washed, a biotin‐conjugated detection antibody mix was added to the array pools and the arrays were incubated for 2 hours at room temperature. The slides were washed again, and Cy3‐conjugated streptavidin was used to bind biotin‐conjugated detection antibodies for a further 2 hours at room temperature. The slides were scanned with an InnoScan 300 Microarray Scanner . The signal values were read using Mapix software and were normalized using internal positive controls in the array using the RayBiotech analysis tool, which was specifically designed for the Human Cytokine Antibody Array Glass series 11. This experiment was performed once.2.3In order to validate the antibody array results, serum samples from 20 control subjects and 20 gastric cancer patients . Fold change (FC) was calculated to indicate the expression tendency of cytokines in the gastric cancer group.Statistical analysis was carried out using Mann‐Whitney U test with the Statistical Package for Social Science Statistics version 20 software . Differences between the two groups were considered significant if 33.1P values and fold changes are shown in Table The signal values of 40 proteins were statistically analyzed by Mann‐Whitney U test to seek proteins differentially expressed between the two groups. As a result, eleven cytokines were identified that were significantly increased as compared to the control group Table . The gen3.2Of these significantly increased cytokines, IFNGR1, Notch‐3, GHR, TNFRSF19L, FR‐beta and SLAMF8 were selected for ELISA validation due to the limits of sample volume and funding. The results of the ELISA validation are shown as a scatter plot Figure , which a4Serum is an important aspect of clinical diagnosis, as serum is readily available and procuring it is a noninvasive procedure. The ease of access to serum simplifies clinical diagnosis of diseases, provided effective biomarkers are utilized. Because of the low specificity and sensitivity of currently available serum biomarkers for gastric cancer, identification of more clinically relevant biomarkers remains an urgent need. In this study, a high‐throughput solid antibody array was utilized to screen serum biomarkers with higher clinical specificity and sensitivity and, to our knowledge, this is the first study to use antibody array techniques to identify serum biomarkers for gastric cancer.The antibody array utilized in this study simultaneously detected 40 cytokines and identified eleven cytokines  significantly elevated in gastric cancer serum when compared to the controls by Mann‐Whitney U test analysis. Additionally, the unsupervised‐hierarchical cluster analysis of the microarray data accurately differentiated the gastric cancer and control groups, further detailing the differences in cytokine expression between the gastric cancer and control groups. More importantly, the significant changes of some cytokines were validated by ELISA, using fresh samples, and found to be identical to the results of the antibody array, suggesting that these cytokines increased reliably in gastric cancer serum and that these cytokines may be used as serum biomarkers of gastric cancer.γ signaling pathway. Canedo et alIFNGR1 is a key molecule of the IFN‐In conclusion, in this study, we found eleven cytokines  that were elevated in gastric cancer serum, suggesting that these cytokines may participate in the occurrence and development of gastric cancer. Among these cytokines, Notch‐3, galectin‐8, EphA1, epiregulin, and FGF‐12 have been reported to be upregulated in gastric cancer. However, the other six cytokines, including IFNGR1, TNFRSF19L, GHR, SLAMF8, FR‐beta, and integrin alpha 5 are reported to be elevated in gastric cancer for the first time in this study, suggesting that these proteins may be novel serum biomarkers for the early diagnosis and prognosis of gastric cancer.5All procedures using humans in this study were approved by the Ethics Committee of the Lishui Central Hospital. All participants approved participating in the study and signed written informed consent forms.The authors declare that no conflicts of interest exist.


Vessel blood flow was monitored, and time to vessel occlusion was analysed as a marker for arterial thrombogenesis. In addition, regional myocardial I/R injury was induced by temporary left coronary artery occlusion in rats pretreated with or without fibrinogen supplementation. In additional groups, ischaemic preconditioning (IPC) was induced by 3 cycles of 5 min of ischaemia/reperfusion. In all groups, myocardial infarct size was determined by triphenyltetrazoliumchlorid staining. Arterial thrombogenesis was not affected by fibrinogen pretreatment. No differences in time until vessel occlusion between Con, Fiblow and Fibhigh groups were observed. In addition, fibrinogen supplementation in low and high concentrations had no effect on infarct size after regional myocardial ischaemia and reperfusion . IPC reduced infarct size from 62 ± 14% to 34 ± 12% (p < 0.05 vs. Con). Furthermore, both fibrinogen concentrations did not affect cardioprotection by ischaemic preconditioning . Haemotherapy with fibrinogen did not affect arterial thrombogenesis, myocardial infarction and the cardioprotective effect of ischaemic preconditioning.Major blood loss during cardiac surgery is associated with increased morbidity and mortality. Clinical pilot studies indicated that preoperative fibrinogen supplementation reduces postoperative blood loss without increasing thrombotic complications. However, an increase in fibrinogen concentration might rather aggravate pre-existing thrombosis than increase the incidence of thrombotic events. Therefore, we investigated, in the present study, whether fibrinogen supplementation influences (1) arterial thrombus formation, (2) the extent of myocardial infarction and (3) the cardioprotective effect of ischaemic preconditioning. Arterial thrombogenesis of the femoral artery was induced by topic FeCl Severe bleeding during cardiac surgery is associated with increased morbidity and mortality ,2. ThereThe current investigation was conducted in accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health  and was performed after obtaining approval from the Animal Ethics Committee of the University of Düsseldorf, Germany. The experiments were performed and the results are reported in accordance with the ARRIVE guidelines. Male Wistar rats , weighing 250–300 g, were housed on a 12:12 light/dark schedule with free access to standard chow and water. All chemicals were purchased from Sigma-Aldrich .In our in vivo rat model, both the surgical procedure and myocardial infarction might cause a change in “endogenous” fibrinogen concentration that possibly influenced our study outcome. Therefore, pilot experiments were conducted, and fibrinogen concentrations were analysed before and after surgical procedure, including thoracotomy and myocardial ischaemia/reperfusion (I/R) injury  on the vessel for 60 s induced a lesion with subsequent thrombosis . After endotracheal intubation, the right jugular vein was cannulated for administration of anaesthetics and fibrinogen, and the left carotid artery was cannulated for measurement of aortic pressure. ECG, arterial pressure and heart rate were continuously monitored and recorded on a personal computer  at a sampling rate of 500 Hz using an analogue-to-digital converter . After surgical preparation of the femoral artery, an ultrasonic transducer was placed around the femoral artery for blood flow measurement. Application of iron (III) chloride  Fiblow: Fibrinogen administration (60 mg/kg BW i.v.) before FeCl3 induced vessel lesion with following thrombosis and (3) Fibhigh: Fibrinogen administration (120 mg/kg BW i.v.) before FeCl3 induced vessel lesion with following thrombosis. Fibrinogen was administered as continuous infusion over 5 min starting 20 min before the initiation of thrombogenesis by FeCl3. Fibrinogen was dissolved in 0.9% saline; animals of the control groups received saline.Rats were randomised to one of the following experimental groups A: (1) Colow, 60 mg/kg BW) or a high concentration . Control animals received saline. IPC was induced by 3 cycles of 5 min myocardial ischaemia and reperfusion prior to 25 min of ischaemia and 120 min of reperfusion. The duration of ischaemia was chosen based on our experience that this protocol results in an expected infarct size in nontreated control rats of about 55–60% of the left ventricle, i.e., values that allow the detection of both protective and adverse effects on infarct size. At the end of the experiments, hearts were excised with the occluding suture left in place and mounted on a modified Langendorff apparatus for perfusion with ice-cold normal saline via the aortic root at a perfusion pressure of 80 cm H2O in order to wash out intravascular blood. After 5 min of perfusion, the coronary artery was re-occluded, and the non-occluded coronary arteries were perfused through the aortic root with 0.2% Evans blue in normal saline for 10 min. Intravascular Evans blue was then washed out by perfusion with normal saline for 10 min. This treatment identified the area at risk as unstained. The heart was cut into 2 mm thick transverse slices. The slices were stained with 0.75% triphenyltetrazolium chloride solution for 10 min at 37 °C and fixed in 4% formalin solution for 24 h at room temperature. The area of risk and the infarcted area were determined by planimetry using SigmaScan Pro 5® computer software .Anaesthesia, endotracheal intubation and cannulation of the vessels were performed as described above. Surgical preparation for temporary occlusion of the left coronary artery (LCA) was performed as described previously . Here, an = 8  in thrombus formation experiments, and a required group size of n = 6 for infarct size experiments . For infarct sizes and thrombus formation, differences between groups were analysed by one-way ANOVA with Tukey post hoc test. Comparisons of haemodynamics between groups or between different time points within a group were performed with a two-way ANOVA followed by Tukey’s post hoc test. Statistical data analysis was performed using GraphPad StatMate™ Version 1.01  or SigmaPlot 13 . Differences were considered statistically significant when p values were less than 0.05.Data are presented as mean ± SD. For thrombus formation as well as myocardial infarction experiments, the sample sizes were calculated a priori using the ANOVA sample size analysis software tool of SigmaPlot 13 . This calculation resulted in a required group size of Fibrinogen levels were determined at baseline and after the surgical procedure, including thoracotomy and myocardial ischaemia/reperfusion injury in rats in vivo. No changes in endogenous fibrinogen concentration were observed within the time window that is relevant for our study protocol (low). In addition, groups with animals receiving higher fibrinogen doses of 120 mg/kg body weight Fibrinogen (Fibhigh) were performed in the arterial thrombus formation experiments as well as in the myocardial ischaemia and reperfusion experiments to test the potential occurrence of prothrombotic side-effects limiting the therapeutic window of fibrinogen supplementation.The required amount of fibrinogen leading to an increase of fibrinogen in plasma of ~60 mg/dL was determined. This substitution corresponds to an intraoperative substitution of 2 g fibrinogen in humans . Our res3 lesion of the femoral artery  nor in the high concentration . Furthermore, neither the low nor the high fibrinogen concentration affected the cardioprotective potential of ischaemic preconditioning (Fiblow + IPC: 34 ± 11%, Fibhigh + IPC: 31 ± 13%; each ns vs. IPC, Ischaemic preconditioning reduced infarct size by ~45% compared to the nonsupplemented control group (Con + IPC: 34 ± 12%, In addition, the areas at risk were not different between experimental groups, indicating comparable occlusion regions in all experiments (Haemodynamic variables are summarised in This study examined potential prothrombotic side-effects of fibrinogen supplementation on arterial thrombus formation, myocardial I/R injury, and the efficacy of cardioprotection by IPC in the rat in vivo. As fibrinogen supplementation did not influence arterial thrombogenesis, myocardial infarct size after I/R or cardioprotection by IPC, the results suggest that fibrinogen at the doses tested and in the setting described does not bear an increased risk of prothrombotic adverse effects. The absence of those adverse effects is essential for the clinical potential of prophylactic fibrinogen therapy to reduce perioperative blood loss.Fibrinogen is a 340 kDa glycoprotein synthesised in the liver. It is critically involved in haemostasis and the first coagulation factor to reach a critical level in major bleeding ,16. DuriInfusion of coagulation factors may induce a state of hypercoagulability with an increased risk of thromboembolic events and thus may result in delayed or diminished reperfusion of the ischaemic myocardium in cardiac surgery by extended thrombus formation. Epidemiologic studies have documented an association of elevated plasma fibrinogen levels with ischaemic heart disease ,23. ElevA potential adverse effect of fibrinogen supplementation cannot only be caused by an aggravation of a thromboembolic event but also by a loss of protective properties. For example, prophylactic treatment with fibrinogen might affect the infarct size reducing effect of IPC. Murry et al. first described the phenomenon of IPC in 1986, where short sublethal periods of myocardial ischaemia and reperfusion before a distinct I/R injury can reduce infarct size up to 75% . InteresOur findings show that fibrinogen supplementation in a clinically relevant dose using an in vivo rat model has no adverse effects on arterial thrombogenesis, myocardial ischaemia and reperfusion injury, and the cardioprotective potency of IPC.


Elevated arsenic and trace metal contamination of the terrestrial food chain represents one of the most significant environmental risk exposures for human populations in developing countries. Metalloid and metal contamination in horticultural crop produce such as fruit is a public health concern in Nigeria. Local fruits are cheap sources of vitamins and minerals for the resident population and pose an important dietary threat of metal(loid) toxicity through consumption.Market basket investigation of five locally grown  and imported (apple) fruits was conducted to measure the total concentrations of arsenic (As), mercury (Hg), copper (Cu), and lead (Pb) present in these fruits from southeastern Nigeria .Fruits were analyzed for As and the three metals using atomic absorption spectrophotometry. Moisture content of fruits was determined and used to transform metal concentrations in dry weight to wet weight and compared to Codex food grade standards and assorted (sub)tropical fruits, edible and inedible peels.−1 for As, 0.02±0.02 – 0.89±0.33 μg g−1 for Hg, 0.11±0.01 - 0.18±0.40 μg g−1 for Cu, and <0.001 – 0.03±0.05 μg g−1 for Pb. The As concentrations (wet weight) in fruits were ~32–166 orders of magnitude higher than Codex Alimentarius Commission (Codex) maximum As food grade levels. Guava and apple methyl Hg concentrations were ~6–~1 orders of magnitude higher than Codex maximum levels, while the content of Cu and Pb in fruits were within acceptable standard limits.The mean ± standard deviation of elemental concentrations in dry weight ranged from 20.0±0.71–96.84±0.00 μg gThe significant concentrations of As and Hg in the examined fruits indicate a potential public health threat. Efforts are needed to initiate and sustain continued monitoring of trace elements in fruits and food sold to consumers due to variation in contaminating sources to ensure food safety. Although a great deal of information exists on Hg toxicity, research on metalloids such as As remains limited in Nigeria and no reliable guidelines exist. Further research is recommended to determine the ecotoxicity of As in Nigeria. Food systems in developing countries are not always as organized as in developed countries due to problems associated with a burgeoning population, rapid urbanization, and shortfall of resources needed to handle food processing.6While trace metals  have been studied extensively and reliable guidelines developed in many jurisdictions, information on metalloid ecotoxicity is relatively limited and not fully understood.CodexCodex Alimentarius CommissionCRMCertified reference materialsdwDry weightwwWet weightCitrus sinensis), pawpaw (Carica papaya), guava (Psidium guajava), apple , and pineapple (Ananas comosus) commonly supplied in the Awka region where there has been a history of metal smelting activities.In Nigeria, there are poor food quality controls and existing mechanisms are not very effective at protecting consumers. Deleterious effects of food contaminants have been underreported in the country as a result of inadequate risk assessment, communication and resource deficits.Figure 1) and experiences two distinct seasons brought about by two predominant winds that rule the area: the southwestern monsoon winds from the Atlantic Ocean and the northeastern dry winds from across the Sahara Desert. Seven months of heavy tropical rains (April–October) are followed by 5 months of dryness (November–March). Awka South is generally hot and humid with a temperature range of 27–28°C from July through December but rising to 35°C between February and April.Samples of locally supplied fruits were collected from the largest market in Awka South, which serves the entire Local Government Area and surrounding towns of Awka North. Awka South is in Anambra State of southeastern Nigeria. It is located within ~6.642° N and ~7.067° E , including fruits sold openly on market tables and shelves.Fruit samples were taken to a laboratory in an air-tight container to prevent further atmospheric deposition that might interfere with elemental analysis. Samples were neither washed nor peeled prior to preparation. The intent was to reflect dietary exposures and common consumer food handling strategies, where fruits are purchased from an open market and consumed without washing. Samples were finely diced in a food processor, which was thoroughly cleaned with 10% extra and rinsed in distilled water after processing each sample. Ground samples were then frozen until further processing.All samples were analyzed for total As, Hg, Cu, and Pb using established methods with modification.Each analytical batch of ten runs was accompanied by an acid blank, and three certified reference materials (CRM): apple leaves,  Standard Reference Material 1515); pine needles, ; and rice flour,  to monitor for instrument accuracy and method extraction efficiency. Mixed element internal standards including all examined metals were measured as part of the analytical quality control. Thus, a total of 25 independent digests including reagent blanks and CRMs were analyzed. Mean recoveries were in an acceptable range (75–98.5%) compared to the CRM theoretical or certified values for the elements. All glassware used in the analyses were cleaned with a detergent-free solution, soaked in acid (10% hydrochloric acid) and then rinsed with metal-free distilled water. Metal concentrations were reported in μg/g dry weight at instrumental detection limit of 0.001 μg/g.We determined the percentage (%) moisture contents of all the fruits examined to enable transformation of metals concentrations from dry weight (dw) to wet weight (ww) basis, and facilitate the comparison of our data with those of Codex Alimentarius Commission (Codex) guideline values . Approximately 10 g of each fruit was weighed into aluminum foil, and dried for 48 hours in a 105°C oven. At the end of the drying period, samples were reweighed and % moisture content was calculated using Data generated from the study were subjected to analysis using IBM SPSS Statistics computer software program  and Microsoft Excel (version 2016) at an alpha error of 0.05 and 95% confidence interval. The normality of data distribution was objectively evaluated by the Shapiro-Wilk test, and homogeneity of variance by Levene's test.5). The mean, minimum, and maximum total As, Hg, Cu and Pb concentrations (dw) for the independent fruit samples collected from Awka market are presented in Table 1). Comparatively, in wet weight, the level of As was significantly higher (P < 0.05) than the Codex maximum level for As in fruits .The percentage moisture contents of all fruit samples examined were guava, 80.8%; apple, 84%; pineapple, 87%; orange, 87%; and paw paw, 85.5%. The data values were subsequently used to transform metal concentrations from dry weights to wet weights .5). Apple, pineapple, orange, and paw paw had Pb concentrations of <0.001 μg/g.The Codex provides a guideline level for methyl Hg of 0.10 μg/g ww. Based on the supposition that methyl mercury is ~83% of total mercury, as presented in The present study investigated concentrations of As, Hg, Cu, and Pb in commonly consumed horticultural fruits to assess food quality and make recommendations based on an explicit understanding of human health risks. Metal contamination of edible fruits poses a potential health risk to both humans and animals. Awka South was selected for the current survey because of the open market system in the area, and because physical structural changes are rapidly taking place within its urban ecology, such as continuous road and building construction. These activities are, in many cases, not rigorously planned. Dust pollution and fog are common weather events that characterize the region, and markets are indiscriminately located along roads. Thus, monitoring of food contamination by metals and hazardous substances is necessary for the assessment and reduction of dietary exposures.512 as of the 2006 census) and it is near the metropolitan area of Onitsha which has a much higher population density  and industrial activities.64The examined fruits had significantly higher total As concentrations ~32–166×) than the Codex maximum food grade level (0.5 μg/g ww). While fruit Cu and Pb concentrations were below the Codex standards, we found that Hg in guava and apple were 6.1 and 1.3 times higher than safe levels for consumption (0.1 μg/g ww), respectively.6× than tMercury is one of the most toxic elements among the studied trace metals and exposure to high levels could permanently damage the brain, kidneys and developing fetus.76The present study shows that the concentrations of common metalloids and trace metals in fruits is varied and highlights the potential impact of anthropogenic activities on metal mobility, deposition, and food contamination. The elevated As and Hg concentrations measured in the fruits indicate potential dietary exposure to metals, and may be of great public health concern. Although Pb concentrations were lower than Codex standards, there is a need to investigate whether these low exposures may contribute to risks of adverse health effects given recent studies indicating health effects at relatively low concentrations. This is a pilot-based screening; larger-scale monitoring is necessary to assess fruits not included in the current survey and correlate contamination levels with metal concentrations in farm soil and atmospheric dust. Food intake assessment methods based on a more comprehensive determination of food types and consumption patterns of the local population need to be implemented and individual consumption data generated to facilitate the risk assessments of metals. Although the present investigation did not examine the effects of adulterants on imported apples, the screening system should be expanded to monitor and control their use on fruits, as there is evidence of adverse health effects associated with adulterants in the food industry.