From ontogenesis to homeostasis, the phenotypes of complex organisms are shaped by the bidirectional interactions between the host organisms and their associated microbiota. Current technology can reveal many such interactions by combining multi-omic data from both hosts and microbes. However, exploring the full extent of these interactions requires careful consideration of study design for the efficient generation and optimal integration of data derived from (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics. In this perspective, we introduce the holo-omic approach that incorporates multi-omic data from both host and microbiota domains to untangle the interplay between the two. We revisit the recent literature on biomolecular host-microbe interactions and discuss the implementation and current limitations of the holo-omic approach. We anticipate that the application of this approach can contribute to opening new research avenues and discoveries in biomedicine, biotechnology, agricultural and aquacultural sciences, nature conservation, as well as basic ecological and evolutionary research.  Microbiome; Evolutionary Biology These microorganisms associated with plants and animals are now acknowledged as relevant—even essential—assets to many basic biological processes, including nutrient acquisition , immune genomic properties  can be expanded to the holo-omic level by the incorporation of data from multiple omic levels from both host and microbiota domains transcriptomes, (meta)proteomes, and (meta)metabolomes. The same technologies also enable epigenomic and exposomic profiling, which can further contribute to disentangling the biochemical associations between host-microbiota-environment interactions and their effect on host phenotypes . Howeveenotypes . The anaenotypes . This apenotypes  and metaenotypes , but metIn this context, the technology to generate large amounts of data to be used in a holo-omic context is already available, but the analytical tools to reveal and identify host-microbiota interactions are still limited. As a consequence, only a handful of research groups worldwide have been able to effectively implement the holo-omic approach. To contribute to the development of this new field, in this perspective we first revisit the available evidence for the biological importance of host-microbiota interactions. Second, we present how the holo-omic toolbox can be used to study host-microbiota interactions at varying levels of complexity to guide researchers through applying the holo-omic approach. Third, we showcase the potential provided by the holo-omic approach to host-microbiota interactions in both basic and applied biological sciences and finally we identify the limiting factors that currently prevent the widespread implementation of the holo-omic approach and discuss possible solutions to overcome them.The holo-omic approach to host-microbiota interactions relies on three major assumptions: (1) host-associated microorganisms interact not only with each other but also with their host ; (2) theLactobacillus rhamnosus increases the uptake of fatty acids in zebrafish by down-regulating the transcription of host genes related to cholesterol and triglycerides metabolism   . The orime (IBS)  and it sme (IBS) .All these examples highlight the relevance of acknowledging and understanding the biomolecular interactions occurring between different omic levels of hosts and microorganisms. In the following section we will describe how holo-omics can be implemented by addressing different methodological, experimental, and analytical approaches.amplicon sequencing, targeted RNA-sequencing, and western blotting) to characterize the microbial domain. However, untargeted approaches , which non-selectively provide a snapshot of nucleotides, proteins, and metabolites present in a sample, are progressively complementing or replacing targeted approaches. For instance, coupled untargeted host/microbe data from shotgun sequencing offers advantages over targeted approaches, such as the construction of metagenome assembled genomes (MAGs) from metagenomic data , might provide the required resolution when trying to uncover the interaction between well-defined binary interactions, e.g., the effect of microbiota-produced butyrate on host transcriptomics in epithelial cells  have linked specific loci in the host genome to the presence of pathogenic or beneficial microbes (MGWASs) linking specific genes in the metagenome to phenotypic traits of interest in the host Are host-microbiota interactions relevant in the system under study?(2)Is it meaningful to implement a holo-omic approach?Researchers must assess whether host-microbiota interactions are relevant for understanding the system under study. The impact of microorganisms associated with complex hosts is now regarded as almost universal , but theIt is necessary to evaluate whether the implementation of a holo-omic approach is reasonable given the properties of the biological system and its environment. Holo-omics relies on the premise that genomic and metagenomic differences across individuals, treatments, populations, or species affect biological processes and phenotypic outcomes. Thus, the existence of genomic or metagenomic variation in the system is essential. It is also necessary to bear in mind that the capacity to recover genomic and metagenomic signatures is largely affected by environmental variables . The bac(3)Which omic levels are relevant and how to maximize the amount of useful data derived from them?Assessing the economic and technical feasibility of the study is also paramount. This includes acknowledging the genome size of the host, as the genome of some species can be magnitudes larger than others, e.g., amphibians  versus bLastly, researchers should identify which omic levels are the most relevant, both by considering the biological and technical features of the experimental system and their relevance to the research questions. The omic levels selected for analysis will largely determine the number of samples to include , where aThe methodological, experimental, and analytical approaches mentioned above are challenged by the high costs of data generation and the complexity of downstream analyses. This requires that researchers consider at least three fundamental questions about the system under study before taking on a holo-omic study .Box 2ThrThe holo-omic approach outlined above can be implemented in many basic and applied biological research fields to address relevant scientific questions concerning host-microbiota interactions. In the following, we showcase and discuss the application of the holo-omic approach in diverse fields of biological sciences. For some fields, we include boxes containing case examples to better illustrate its potential implementation.Box 3(1)Are host-microbiota interactions relevant in the system under study?Chickens are an important source of high-quality protein for a large proportion of the human population. The gut microbiota of broilers (chicken bred for meat production) is highly variable since they are slaughtered before reaching an age in which the microbial community dynamics stabilize . Althoug(2)Is it meaningful to implement a holo-omic approach?The controlled environmental conditions in intensive poultry production systems, which use the same feeding strategy and environment for all individuals, indicate that the likely reason behind variation in the chicken performance and their gut microbiota when administered pre- and probiotics might be explained by microbial founding effects and microgenomic variation of broilers ((3)Which omic levels are relevant and how to maximize the amount of useful data derived from them?Pre- and probiotics interact with native gut microorganisms as well as with the host. The gut microbiota of broilers is relatively simple because of the closed environment where the broilers are reared. The genetic diversity of conventionally bred broilers is low, yet even small interindividual differences can be crucial and might have wide implications on the response to pre- and probiotics. These system properties allow the successful application of the holo-omic framework for obtaining relevant microbe-microbe and microbe-host interactions, which can help researchers optimize feed additives design, production, and administration, thereby preventing production inefficiency driven by gut dysbiosis.If associations are detected between the (meta)genome and host phenotypic traits, the study of transcriptome, metatranscriptome, and metabolome can unveil the nature of microbe-microbe and host-microbe interactions and how they affect the host. Detecting molecular pathways that are activated or deactivated in the presence of pre- and probiotics can enable researchers to identify production-related phenotypic changes in the host.The holo-omic approach could prove a meaningful tool in developing animal and plant production as microorganisms are increasingly considered essential assets to improve efficiency and sustainability . Among oThe holo-omic approach could also contribute to developing and optimizing biotechnological solutions. For instance, it could be used to better understand host-microbiota systems capable of enzymatically degrading complex polysaccharides  in the sBox 4(1)Are host-microbiota interactions relevant in the system under study?The use of fecal transplants, i.e., transferring fecal material from a healthy donor to a patient with a gastrointestinal disorder, is now becoming a promising treatment for multiple gastrointestinal disorders . Althougepigenotype of the human host ((2)Is it meaningful to implement a holo-omic approach?The success of fecal transplants relies on the capability of beneficial microbes from the donor fecal sample being able to colonize and establish themselves in the gut environment of the recipient. One can hypothesize that the probability of success relates to differences among patient gut “environments” that depends not only on the existing microorganism community but also on the genotype or Clostridioides difficile ((3)Which omic levels are relevant and how to maximize the amount of useful data derived from them?The information gained from a holo-omic approach will ultimately lead to more efficient treatments by, for example, optimizing the biological match between a fecal donor and recipient. For example, a holo-omics analysis in a controlled cohort can reveal concrete genotypes of a host that are associated with the gut microbiota composition. Then, once we have accumulated knowledge about specific candidate genes directly associated with composition and function of gut microbiota, we can screen these genes to optimize the genetic match between donor and recipient, thereby improving the odds that the recipient is likely to adopt the healthy microbes from the donor and thereby counteract the negative effect from microbes such as ifficile .(3)WhichA holo-omic approach to identify the factors underlying the differential success of fecal transplants could include the patients' genomic and epigenomic features coupled with transcriptomic, metagenomic, metatranscriptomic, and metabolomic variation before and after a fecal transplant. Associating these features with the success of the treatment, and with each other, would shed light on the functional changes introduced by the transplant, which would enable identifying the factors leading to a success or failure of the treatment.Incorporating the holo-omics approach to biomedical research offers an exciting new avenue toward better treatments of many modern human diseases. Most people in industrialized societies exhibit depauperate gut microbiotas , which ihologenome theory of evolution  feeds almost exclusively on conifer needles rich in resin and phenol and low in nutrients ((1)Are host-microbiota interactions relevant in the system under study?In winter, the Western capercaillie (utrients . It has utrients . Failureutrients .(1)Are h(2)Is it meaningful to implement a holo-omic approach?The highly specialized diet with many hard-to-digest components of the capercaillie suggests that the digestion of these compounds might be facilitated by the microbiota. An initial screening using shallow shotgun sequencing will indicate microbial differences between wild and captive capercaillies to identify taxa and functions related to the degradation of resin and phenol that might be missing in captive individuals.(3)Which omic levels are relevant and how to maximize the amount of useful data derived from them?If the captive bred individuals originate from the same population as they are meant to be released in, then the system is relatively simple with two similar genetic backgrounds (wild and captive-bred from the same wild population). This means that the effect of genetics is roughly the same for wild and captive conspecifics, which will allow researchers to study the impact of the environment  on microbiota functionality transcriptome. Screening both the transcriptome and metatranscriptome will allow conservationists to uncover complementary interactions between host and microbiota genes. If the genes of interest are suddenly present and expressed then the dietary change has been enough to provide the captive capercaillies with a “wild microbiota” and released animals can then be monitored and their fitness compared with control animals with a captive microbiota. If captive individuals fail to acquire the needed functionalities through the gradual change to a more natural diet other vectors of enrichment should be tested .Holo-omics could also be relevant for developing optimal active conservation actions, such as captivity breeding and animal translocations . As captAlthough holo-omics represent a valuable tool for many fields, its implementation is still hampered by economic, technical and biological limitations. A main economic hurdle is the high cost of shotgun sequencing. Targeted sequencing or DNA microarrays approaches can be cost-effective alternatives for characterizing (meta)genomes in some cases, although shallow shotgun sequencing can in some instances recover higher taxonomic resolution at the same cost, while also providing direct inference about functionality . TargeteThe holo-omic approach faces essential challenges, such as those linked to the quantity and complexity of the data to be analyzed. The interactions between different microbes, each synthesizing and metabolizing a variety of molecules, and the interactions between microbes and host cells is extremely complex, with the nature of these interactions being far from uniform and linear. This demands an integrative approach that can account for the different data types under the same inference framework. Generative/mechanistic models exist for many of the individual omics data, such as transcriptome, proteome, and metagenome, but integrating these models under a single inference framework is challenging, given the different data types  and the vastly different biological processes that underlie them. Thus, developing mechanistic models for such data are an active area of research. In addition, in most current studies, the holo-omics data contain a lot of missing values, e.g., the transcriptomics and microbiome data may not come from the same individuals, and the generated data fall under the small N (sample size), large P (features) paradigm. That is, the data contain a limited number of independent observations of a large number of features. In the case of holo-omics data, features can include millions of genomic variants, mRNA quantification for thousands of genes, abundance estimates of hundreds to thousands of taxa in the microbiome, and tens to hundreds of phenotypes such as health parameters and growth rates. Unfortunately, the large number of features (P) are not accompanied by a corresponding increase in sample sizes (N), owing to the high cost of generating such comprehensive data for a large number of individuals. Identifying the important determining features in such datasets can be very challenging given the limited number of independent observations. Statistical advances in the last decade including development of deep learning methods are helping address the challenges posed by the high dimensionality and complex correlation structure of the data. Development of such methods is an area of active research where several advances have been made in integrating host-microbiome data .Although still challenged by many limitations, the feasibility to conduct holo-omic research will only increase in the near future, aided by the continuous publication and improvement of macro- and microorganism genomes, the decrease of costs for DNA/RNA sequencing and mass spectrometry, the increase of computational capacities, and the uninterrupted development of analytical tools to analyze the huge amounts of data generated. These trends will allow a broader range of research groups to conduct holo-omic studies and as the need for detailed information on host-microbiota interactions increases in both applied and basic sciences there is no doubt that the holo-omic approach will gain popularity in the future.


Cerebral arterio venous malformations (AVM) are a major causal factor for intracranial hemorrhage, which result in permanent disability or death. The molecular mechanisms of AVM are complex, and their pathogenesis remains an enigma. Current research on cerebral AVM is focused on characterizing the molecular features of AVM nidus to elucidate the aberrant signaling pathways. The initial stimuli that lead to the development of AVM nidus structures between a dilated artery and a vein are however not known.In order to understand the molecular basis of development of cerebral AVM, we used in-depth RNA sequencing with the total RNA isolated from cerebral AVM nidus. Immunoblot and qRT-PCR assays were used to study the differential gene expression in AVM nidus, and immunofluorescence staining was used to study the expression pattern of aberrant proteins in AVM nidus and control tissues. Immunohistochemistry was used to study the expression pattern of aberrant proteins in AVM nidus and control tissues.The transcriptome study has identified 38 differentially expressed genes in cerebral AVM nidus, of which 35 genes were upregulated and 3 genes were downregulated. A final modular analysis identified an upregulation of ALDH1A2, a key rate-limiting enzyme of retinoic acid signaling pathway. Further analysis revealed that CYR61, a regulator of angiogenesis, and the target gene for retinoic acid signaling is upregulated in AVM nidus. We observed that astrocytes associated with AVM nidus are abnormal with increased expression of GFAP and Vimentin. Triple immunofluorescence staining of the AVM nidus revealed that CYR61 was also overexpressed in the abnormal astrocytes associated with AVM tissue.Using high-throughput RNA sequencing analysis and immunostaining, we report deregulated expression of retinoic acid signaling genes in AVM nidus and its associated astrocytes and speculate that this might trigger the abnormal angiogenesis and the development of cerebral AVM in humans.The online version contains supplementary material available at 10.1186/s12974-021-02094-2. Cerebral arterio venous malformations (AVMs) are an important cause for intracranial hemorrhage (ICH) and neurological morbidity in young adults . The AVMCerebral AVMs can develop de novo and could also be congenital in origin. The long-term notion is that AVMs are congenital in nature, and limited studies are available on the pathogenesis of this disease to genes involved in early vasculogenesis and angiogenesis. Recent reports suggest an association of cerebral AVMs with various pathological stimuli such as trauma/brain injury, viral infections, neuronal abnormal development, and migration disorders in the brain, which may occur prior to clinical manifestation of AVM , 21. An Emerging high-throughput technologies open new avenues to identify the genetic defects and pathobiology of diseases at genome scale. Using microarray, the differential gene expression analysis of AVM nidus has revealed that Ephrin A1 is a candidate gene for AVM pathogenesis . The traThe cerebral AVM nidus structures were collected from the patients who underwent resection of AVM at Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST) between 2015 and 2017. The study was approved by the Institutional Ethical Committees of both SCTIMST and Rajiv Gandhi Centre for Biotechnology (RGCB). The control tissues consisted of vascular brain tissue from epileptic patients who had corrective surgery. Both AVM tissues and control tissues were stored immediately after resection in RNA later solution . The transcriptome sequencing analysis was done in three independent cerebral AVM nidus and three control tissue samples, and validation studies were done using 10 AVM and 10 control tissue samples. For immunofluorescence and immunohistochemical analyses, tissues were stored in 4% paraformaldehyde (PFA).g for 10 min and the supernatant was collected in a new tube. The chloroform was added to the supernatant, and the mixture was vigorously shaken for 15 s and was allowed to stand at room temperature for 15 min. Centrifugation was again done at 12000g for 10 min, and the supernatant was transferred to a new tube. An equal amount of isopropanol was added to the solution and incubated at − 20 °C for 2–4 h for RNA precipitation. Later, centrifugation was done at 12000g for 10 min and the supernatant was decanted. Subsequently, washing of RNA was carried out in 70% ethanol and the RNA pellet was resuspended in 20 μl of 1/10 Tris EDTA buffer. The RNA concentration and quality were determined using a nanodrop spectrophotometer. A260/A280 ratio between 1.9 and 2.1 was taken to denote the quality of isolated RNA. Further, electrophoresis was carried out in formaldehyde-denatured gel and the quality of the isolated RNA was confirmed. The RNA sequencing libraries were then prepared from the isolated total RNA using Illumina True Seq mRNA standard library preparation kit; the quality of the libraries was verified using agilent tap station, and cluster generation and sequencing using NextSeq 500 were proceeded.RNA was isolated using TRIzol reagent  as per the manufacturer’s protocol. Briefly, for up to 1 g of tissue, 500 μl of TRI reagent was added and triturated using mortar and pestle. Later, the triturate was centrifuged at 12000http://www-huber.embl.de/users/anders/DESeq/). Genes were classified as up- and downregulated genes based on their log fold change (FC) value calculated by FC = Log2 (treated/control) formula. A heat map was constructed to show the level of gene abundance. The heat map was constructed using the log-transformed and normalized value of genes based on Euclidean distance as well as based on the average linkage method.Differential expression analysis was performed by employing a negative binomial distribution model (DESeq v1.8.1 package http://biit.cs.ut.ee/gprofiler/). The g:Profiler internally uses g:GOSt which takes a list of genes and does functional profiling of them using different kinds of biological evidence. The tool performs statistical enrichment analysis to find information like Gene ontology terms and biological pathways.Gene ontology (GO) annotations of the genes were determined by the g:Profiler program (http://www.genome.jp/kegg/ko.html). Subsequently, each gene was provided with KO (KEGG Orthology) assignment, and association of the gene with KEGG metabolic pathway was determined.The functional annotations of genes were carried out against the curated KEGG genes database using KAAS (KEGG Automatic Annotation Server),  primers and M-MLV reverse transcriptase as per manufacturer’s  protocol. The cDNA preparations were used for the amplification of transcripts with primers specific for the genes. GAPDH was used as the endogenous control, and the expression of all genes were measured with Power SYBR Green Master Mix. The GAPDH and list of gene-specific qRT-PCR primers were used for this analysis  and sectioned using a microtome. The sections were fixed in acetone and suspended in PBS. Antigen retrieval was done by incubating the tissues in citrate buffer at 97 °C for 20 s. After blocking of non specific binding sites using 3% bovine serum albumin (BSA), overnight incubation with primary antibody was carried out. For single immunofluorescence staining with ALDH1A2 antibody overnight incubation (1:100 dilutions) was done. For double and triple immunofluorescence staining, all the primary antibodies in 1:100 dilutions, anti GFAP , anti Vimentin , anti ALDH1A2, and anti CYR61 were added simultaneously and incubated overnight.Excess primary antibody was washed off using PBST, and incubation with Alexa Fluor conjugated rabbit/mouse/goat secondary antibodies  was done for 1 h. The Hoechst dye was used for staining the nucleus, and mounting was done using fluorochrome; the images were collected using a fluorescent microscope .t test was done to analyze difference in mRNA and protein expression levels. The P value of less than 0.05 was the criterion to be statistically significant.Graph Pad PRISM version 6.07 was used for statistical analysis. All the results were expressed as mean ± SEM. Student’s To identify the aberrant molecular signatures in the cerebral AVM nidus structures, we employed in-depth RNA sequencing with RNA isolated from cerebral AVM nidus. The quality of the RNA was verified by formaldehyde-denatured agarose gel  in the three AVM samples when compared to control samples  in AVM nidus tissues. These CDEGs could be classified into two groups: 1) genes related to inflammation and immune response and 2) genes regulated by retinoic acid signaling. Earlier reports have suggested the presence of inflammatory cells in AVM tissues. Although not considered as an inciting event in cerebral AVM pathogenesis, the role of inflammation in the clinical course of this disease is widely recognized. Inflammation is associated with disease progression and rupture of AVM , 23. It The role of retinoic acid signaling in regulating angiogenesis is not studied in detail. A few reports have suggested that retinoic acid has both anti-angiogenic and pro-angiogenic effects , 19, 26.Upregulated expression of ALDH1A2 in astrocytes in the immediate neighborhood of cerebral AVM nidus structures is an interesting finding of our study. This observation prompted us to analyze the nature of the astrocytes surrounding the AVM, and we observed that the astrocytes of the AVM nidus had high expression of Vimentin and GFAP, which suggest the abnormal nature of these astrocytes. There was an increased expression of Vimentin, GFAP, and ALDH1A2 in cerebral AVM-associated astrocytes. It is known that astrocytes of the normal adult brain have very less or undetectable levels of ALDH1A2, whereas astrocytic gliomas and in proliferative states of astrocytes contain elevated levels of ALDH1A2 .Among the differentially expressed genes in AVM vascular structures, the CYR61, one of the retinoic acid target genes, was highly deregulated in the cerebral AVM-associated astrocytes and vascular cells. The CYR61 protein controls the critical regulators of the angiogenesis process. Angiogenesis is under the control of vascular endothelial growth factor (VEGF) which in turn is regulated by CYR61 . The CYRAnother interesting observation of our study is the increased concomitant expression of GFAP, Vimentin, ALDH1A2, and CYR61 in the cerebral AVM-associated astrocytes. The CYR61 is a matricellular protein that is secreted by astrocytes during development. Expression of this protein is reduced in the adult brain. It is known that the CYR61 is upregulated during tissue remodeling, axon regeneration, and angiogenesis. Expression of CYR61 has also been found to be increased in various types of tumors such as pancreatic cancers, melanomas, mammary tumors, and various grades of primary gliomas . RecentlAdditional file 1:Supplementary Figure 1. (A) Formaldehyde denature agarose gel depicts the quality and intactness of RNA isolated from control and test (AVM nidus) tissues. (B) The library prepared from fragmented RNA of control sample and the quality of the library was verified using Agilent tap station. (C) The library prepared from fragmented RNA of AVM nidus sample and the quality of the library was verified using Agilent tap station. Supplementary Figure 2. (A) WEGO plot representing the up-regulated genes in AVM tissue compared to control tissue. (B) WEGO plot representing the down-regulated gene in AVM tissue compared to control tissue. Supplementary Figure 3. The KEGG pathway analysis of AVM tissue sample and control tissue sample of RNA sequencing data. Supplementary Figure 4. The immunofluorescence analysis with the secondary antibodies as control for AVM tissues. All the three secondary antibodies used in the analysis has zero background signal. The Hoechst staining depicts the nuclear staining of the intact cells.Additional file 2:Supplementary Table 1. List of primers used for qRT-PCR analysis in this study.


KDM2B‐associated paunch calf syndrome (PCS) in Romagnola cattle was identified in Marchigiana cattle.Chianina, Romagnola, and Marchigiana are the 3 most important Italian breeds of cattle raised in the Apennine Mountains. Inherited disorders have been reported in the Chianina and Romagnola breeds but not in the Marchigiana breed. Recently, a case resembling recessively inherited To characterize the features of the observed congenital anomaly, evaluate its possible genetic etiology, and determine the prevalence of the deleterious allele in the Marchigiana population.A single stillborn Marchigiana calf was referred for clinicopathological examination because of the presence of PCS‐like morphological lesions.KDM2B genotype and 114 Marchigiana bulls were genotyped.The animal was necropsied and the calf and its parents were genotyped. A PCR‐based direct gene test was applied to determine the KDM2B missense variant was confirmed. Both parents were heterozygous for KDM2B and the prevalence of carriers in a selected population of Marchigiana bulls was <2%.The pathological phenotype included facial deformities, enlarged fluid‐filled abdomen, and hepatic fibrosis. The affected animal was the offspring of consanguineous mating and homozygous presence of the KDM2B variant initially detected in Romagnola cattle also occurs in the Marchigiana breed.The characteristic malformations and genetic findings were consistent with the diagnosis of PCS and provide evidence that the deleterious  AIartificial inseminationOMIAOnline Mendelian Inheritance in AnimalsPCSpaunch calf syndromePMTcongenital pseudomyotonia1Chianina, Marchigiana, and Romagnola are the 3 most common Italian cattle breeds in the central part of the Apennine Mountains. Although used in the past mainly as draft animals, in recent decades they have been included in intensive selective breeding programs to increase their use for beef production.ATP2A1 gene, encoding the SERCA1 pump, was reported to cause autosomal recessive pseudomyotonia (PMT), a congenital muscle function disorder (OMIA 001464‐9913).ATP2A1‐associated PMT also was reported in the Romagnola breed associated with allelic heterogeneity of the ATP2A1 gene.ATP2A1 missense variants (c.632G>T and c.857G>T) also were found to be causative for the disorder.In Chianina cattle, the c.491G>A missense variant in the ABCA12 gene  is another lethal recessively inherited disorder that occurs in the Chianina breed.NID1 gene.KDM2B gene.KDM2B gene encodes a histone demethylase that acts as an important transcription regulator affecting organ development and cell differentiation. The disease‐associated variant leads to an amino acid exchange in a highly conserved domain, thus explaining the phenotypic effect of the genetic variant.Ichthyosis fetalis caused by a missense variant in the KDM2B causing this disease entity in Marchigiana cattle.Recently, a Marchigiana calf with a complex congenital malformation phenotype resembling PCS of Romagnola cattle was presented for examination. Our aim was to describe the clinicopathological phenotype associated with the inherited disorder of 2A full‐term stillborn, 55 kg, male Marchigiana calf delivered after dystocia was referred to the teaching hospital of the Department of Veterinary Medical Sciences of the University of Bologna for evaluation of multiple congenital malformations.A complete phenotype study was performed. Representative samples from the liver were collected, fixed in 10% buffered formalin, embedded in paraffin, and processed for histological examination. Five‐micron histological sections of the liver were stained with hematoxylin and eosin, Masson's trichrome and rhodanine, and specific immunohistochemical methods, using the streptavidin‐biotin peroxidase technique, including vimentin  and α‐smooth muscle actin  were applied to formalin‐fixed liver samples.KDM2B gene, as previously describedKDM2B genotyping, including semen samples from 87 Marchigiana adult bulls that were classified as suitable for reproduction and an additional 27 EDTA blood samples from young males selected for performance testing.Additionally, a pedigree analysis of the affected calf was performed. We harvested cutaneous tissue from the calf, as well as EDTA blood from its parents. Desoxyribonucleic acid was extracted and the 3 subjects were genotyped for the c.2503G>A variant in the 33.1The affected calf had an enlarged head caused by a SC swelling that was especially evident in the periocular and the submandibular regions. The splanchnocranium was shortened and the frontal region asymmetric. The tongue was swollen and protruded from the mouth. The eyes were enlarged with evident scleral injection and conjunctival edema. The ventro‐abdominal region was enlarged and pendulous and generalized swelling was noticed. The hind legs also were swollen and the right fetlock was externally rotated Figure .3.2The swellings were caused by generalized SC infiltration of serosanguineous fluid, as evidenced at necropsy. Similar fluid collection also was present in the peritoneal, pleural, and pericardial cavities.The liver was larger than normal, with an irregular surface and firm consistency. Upon sectioning, the cut surface of the liver had numerous bands of whitish, collagenous connective tissue, and a lobular structure partitioned by fibrous septa, which also were visible inside the lobules. Two round 5‐mm diameter cysts, containing reddish fluid, were present on the peritoneal surface of the left lobe of the liver.Histopathology identified extensive distortion of the lobular architecture by slight to moderate fibrosis in the portal fields and, in some cases, around the central space of the liver lobule (hematoxylin and eosin staining). The fibrosis was more evident in sections stained with Masson's trichrome, and was characterized by the formation of thin portal‐portal and portal‐central fibrotic septa. In some lobules, fibrosis extended to the perisinusoidal spaces. Fusiform cells were observed in the inner wall of the sinusoids. Enlarged sinusoids, cellular hepatic degeneration or atrophy, and capsular fibrotic thickening were detected. No copper deposits responsible for fibrogenesis were detected on rhodanine staining. Immunohistochemical examination (vimentin and α‐smooth muscle actin) identified slight immunoreactivity for laminin intermediate filaments. The fusiform cells, positive using antivimentin and anti‐α‐smooth muscle actin antibodies and observed within the sinusoids in fibrotic areas, were considered myofibroblasts actively involved in fibrogenesis Figure . Histopa3.3KDM2B variant subsequently was confirmed in the affected animal, and both parents were confirmed as heterozygous carriers of this deleterious allele. Evaluation of the prevalence of this pathogenic variant in Marchigiana cattle identified a frequency of 1.75% (2 of the 114 tested bulls were heterozygous carriers).Pedigree analysis of the stillborn Marchigiana calf disclosed the presence of a consanguineous mating, because the sire of the affected calf was also the sire of its dam Figure . In ligh4KDM2B homozygous mutant case of PCS in Marchigiana cattle, confirming an identical genetic etiology for a disorder that has been known to exist for many years in Romagnola cattle. It is possible that the origin of this rare disease‐causing KDM2B variant in Marchigiana breed is based on either accidental crossbreeding or targeted introgression of Romagnola cattle. In the available pedigree data for the presented case, no indication of cattle other than the Marchigiana breed was found. Present day Marchigiana cattle are derived from Podolian cattle, a stock typical of the Italian Marche region that originated in the region of Podolia in present day Ukraine. The breed was introduced to Italy after the fall of the Roman Empire and in the past was used for draft work. In the late 19th and early 20th centuries, the Marchigiana breed was improved first by crossing with the Chianina breed and successively by further crossing with the Romagnola breed . Since the 1930s, the breed has undergone appropriate breeding strategy that has directed it toward beef production, resulting in 1 of the most competitive beef breeds in Italy that also is exported throughout the world.The clinical and pathological findings described in the calf of our study completely overlapped with the phenotype reported for PCS of the Romagnola breed. Therefore, our report constitutes the first description of a KDM2B variant was introduced into the Marchigiana breed. In fact, it cannot be excluded that the variant already was present before the Marchigiana breed was established as distinct breed, and therefore it may represent the identical variant as in the Romagnola breed. To the best of our knowledge, no other cases have been reported, and it is possible that the frequency of the deleterious allele is very low.Unfortunately, no information is available to determine when the PCS‐associated This speculation was confirmed by the genotyping of 114 bulls used for AI. Nevertheless, additional data might be useful to make a more precise estimate. For this purpose, DNA isolated from skin biopsy samples or semen should be used to determine the individual's genotype without concern about cattle‐specific leukochimerism possibly causing a false genotype.Compared to the substantial allele frequencies of the PCS variants in Romagnola cattle ,Our report serves to alert breeders of Marchigiana cattle about the possible emergence of PCS in the future and will permit the avoidance of carrier matings by systematic genetic testing of potential sires.Authors declare no conflict of interests.Authors declare no off‐label use of antimicrobials.This study was not based on an invasive animal experiment and used a naturally occurring case, therefore there are no associated permit numbers. The blood used for the genetic analysis derives from samples obtained for sanitary controls or for other reasons not related to this investigation. The investigation cannot be considered including “animal experiment” according to the exemptions contemplated by the Italian legislative decree n. 26/2014 .Authors declare human ethics approval was not needed for this study.


Malnutrition is highly prevalent in people with head and neck cancer (HCN) and is associated with poorer outcomes. However, variation in malnutrition diagnostic criteria has made translation of the most effective interventions into practice challenging. This study aimed to determine the prevalence of malnutrition in a HNC population according to the Global Leadership Initiative on Malnutrition (GLIM) criteria and assess inter-rater reliability and predictive validity. A secondary analysis of data available for 188 patients with HNC extracted from two cancer malnutrition point prevalence studies was conducted. A GLIM diagnosis of malnutrition was assigned when one phenotypic and one etiologic criterion were present. Phenotypic criteria were ≥5% unintentional loss of body weight, body mass index (BMI), and subjective evidence of muscle loss. Etiologic criteria were reduced food intake, and presence of metastatic disease as a proxy for inflammation. The prevalence of malnutrition was 22.6% . Inter-rater reliability was classified as excellent for the GLIM criteria overall, as well as for each individual criterion. A GLIM diagnosis of malnutrition was found to be significantly associated with BMI but was not predictive of 30 day hospital readmission. Further large, prospective cohort studies are required in this patient population to further validate the GLIM criteria. Head and neck cancer (HNC) had an age-standardised incidence rate of 16.6 cases per 100,000 persons in Australia in 2016 ,5,6,7,8.The variation in malnutrition prevalence in people with HNC may be explained by the heterogeneity of the cohorts studied, including stage of disease, at varying stage of diagnosis, and different treatment modalities. The variation may also be explained by the numerous criteria used to diagnose malnutrition. Percentage loss of weight ,5,15, boIn 2018, the Global Leadership Initiative on Malnutrition (GLIM) published a global consensus-based definition of malnutrition .Currently, only one study has used the GLIM criteria to diagnose malnutrition in HNC patients . This stThis study was a secondary analysis using previously collected data from two multi-centre cancer malnutrition point prevalence studies conducted in 2016 and 2018. These studies were undertaken in participating public and private Victorian health services in Australia: 16 sites in 2016 and 19 sites in 2018.Data collection was conducted over a common four-week period for each study: November to December 2016 and July to August 2018. At each participating health service, experienced clinical dietitians or trained dietitian students who had passed their clinical competencies, collected data for multi-day inpatients, ambulatory radiotherapy patients and chemotherapy day patients at both time points.Eligible participants were aged 18 years and over; admitted to hospital for cancer treatment (including surgery) or related management for a minimum of 2 nights; or ambulatory patients attending for radiotherapy or intravenous chemotherapy or immunotherapy. Patients were excluded when they were attending for oral chemotherapy or maintenance/hormonal treatment only; were attending for day surgery or medical review only; were admitted to an ICU or emergency department on the day of data collection; were terminally ill with a life expectancy of less than one month; or were unable to provide verbal consent.Only HNC patients recruited to the point prevalence study were included in this secondary analysis.Demographic data collected for each participant included age, sex and living situation . Clinical data included presence of metastatic disease, current cancer treatment , treatment setting (inpatient or ambulatory), and treatment location .Each participant was screened for malnutrition risk using the malnutrition screening tool (MST) ,21, vali2 if <65 years or <22 kg/m2 if ≥65 years), healthy weight (18.5–24.9 kg/m2 if <65 years or 22–27 kg/m2 if ≥65 years), and overweight (≥25 kg/m2 if <65 years or >27 kg/m2 if ≥65 years) [Body mass index (BMI) was calculated from height and weight recorded using usual equipment within each hospital or as reported by the participant. BMI was categorised as underweight  had a subjective assessment of muscle stores completed on a minimum of 4 of the 7 muscle sites specified in the PG-SGA tool . A minimParticipants were asked whether their food intake had reduced. Where a reduction in food intake was reported, the degree of reduction compared to usual intake  and duration  was recorded.As no objective marker of inflammation was available, the presence of metastatic disease was collected from the medical record and used as a proxy for inflammation. The blanket assignment of inflammation due to a cancer diagnosis is not recommended to be used for this etiologic criterion as it does not indicate the severity of the disease burden .The GLIM criteria were applied by two independent dietitians, both with over 10 years clinical experience in oncology nutrition, using the three phenotypic and two etiologic criteria. How the data from the present study were applied using the GLIM criteria is presented in Severity of malnutrition, moderate or severe, was graded according to the extent of weight loss, BMI cut offs and extent of muscle deficit as described in Thirty days following the initial data collection, data were collected from the medical record on any unplanned admissions to the same hospital and patient status  for each participant.p < 0.05 indicated statistical significance for all tests. Inter-rater reliability was assessed using the Kappa statistic. Values greater than 80% represent ‘excellent’ agreement, >60% ‘substantial’, 40–60% ‘moderate’ and <40% ‘poor to fair’ agreement [Data analysis was completed using SPSS Statistics version 26. Descriptive statistics for all continuous variables were reported as mean (standard deviation) or median (inter-quartile range), depending on the normality of distribution. Categorical variables were reported as counts and percentages. Subsequent to descriptive reporting, all analyses used the GLIM variable according to the first method of assessment, whereby malnutrition was diagnosed by the presence of at least one phenotypic and one etiologic criterion being present. Construct (discriminant) validity was assessed using Chi-square tests for categorical variables and independent t-tests or Mann-Whitney U tests for continuous variables, dependent on the normality of distributions. A p-value of greement .Binomial logistic regression models were used to determine the predictive validity of the GLIM criteria malnutrition diagnosis for unplanned hospital admissions up to 30 days post initial data collection. Potential co-variates to be considered in the models included: sex, age (continuous variable), social support , treatment setting (inpatient versus ambulatory), presence of metastatic disease and treatment location . Where variables were highly intercorrelated (multi-collinearity) one variable was removed from the model. Singularity was also assessed between the metastatic disease and malnutrition variables given metastatic disease is a criterion which forms part of the GLIM score. The malnutrition variable was retained in all models given it was the independent variable of interest. Findings are presented as odds ratios . The recommended sample size for regression analyses is 50 + 8m (where ‘m’ represents the number of independent variables). Therefore, the outlined analyses required a complete case sample of 106 patients . Ethics approval was obtained from the Human Research Ethics Committee at Peter MacCallum Cancer Centre, Melbourne, Australia (HREC/16/PMCC/149.) with site specific approval obtained at each of the participating sites. This study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines .There were a total of 188 HNC participants in the combined 2016 and 2018 datasets. Of these, 177 participants had the required data to be able to determine the presence of malnutrition according to the first method of scoring and 184 when the metastatic disease (inflammation proxy) criterion was removed  are reported in Participant characteristics for the complete dataset  and using only the presence of reduced food intake as the etiologic criterion, was 20.7% (38/184). There were also an additional 18 participants who had at least one phenotypic criterion present but were not diagnosed as malnourished as neither etiologic criterion were present. When assessing subgroups by treatment modality, the prevalence of malnutrition using GLIM criteria was 24.1% (19/79) for surgery, 19.8% (17/86) for chemotherapy, 17.9% (26/145) for radiotherapy, and 0% (0/3) for immunotherapy.Inter-rater reliability for malnutrition diagnosis using the GLIM criteria was classified as ‘excellent’ for the overall malnutrition prevalence (GLIM criteria), as well as for each individual criterion .p = 0.031.)There was no significant association found between the presence of malnutrition according to the GLIM criteria and sex, age, metastatic disease, social supports, BMI , treatment setting or treatment location . Malnutrn = 21 participants) within the 30 day period from the date of the point prevalence data collection. There was no evidence of multi-collinearity. The full regression model was non-significant, χ2  = 14.66, p = 0.066, indicating that it was not able to predict unplanned hospital admissions. As a whole, the model explained between 10.0% (Cox and Snell R square) and 18.6% (Nagelkerke R square) of the variance in unplanned hospital admissions indicating that other factors not measured in this study contribute to unplanned hospital admissions. The only co-variates which retained significance in the model were age (p = 0.034) and the presence of metastatic disease (p = 0.011). For each additional year of age participants were 0.95  times less likely to have an unplanned hospital admission. Participants with metastatic disease were 5.0  times more likely to have an unplanned hospital admission than participants without metastatic disease.A diagnosis of malnutrition using the GLIM criteria was not significantly associated with unplanned hospital admissions (Using the 2-step approach to diagnosing malnutrition according to the GLIM criteria, this study found that, of the 42% of HNC participants who were at risk of malnutrition, almost one-quarter were malnourished. An excellent level of inter-rater agreement was demonstrated, indicating that the criteria can be reliably applied by experienced clinicians to determine the presence of malnutrition in HNC.The previously described study using GLIM criteria in HNC by Einarsson et al. reported a malnutrition prevalence of between 0.5 and 32.4% depending on the treatment time point and the combination of GLIM criteria used . Our resOur reported GLIM malnutrition prevalence is relatively low compared to previous studies that have used other criteria to diagnose malnutrition in HNC patients. Many of the studies that reported prevalence rates higher than 40% analysed patient data from more than 10 years ago ,4,5, andAs recommended by the 2-step approach to diagnosing malnutrition using the GLIM criteria, all participants in our study who were not at risk of malnutrition according to the MST were classified as well nourished. This may, however, have led to under-reporting of the malnutrition prevalence. A recent study conducted in a geriatric cohort reported that approximately 15% of participants who were diagnosed as malnourished using the GLIM criteria were not detected by the MST . The MSTTo our knowledge, this is the first study in HNC patients to investigate the inter-rater reliability of applying the GLIM criteria. There was excellent agreement between assessors when applying each of the individual GLIM criterion, as well as the overall GLIM criteria. This finding is important in both the clinical and research settings, as it suggests that using multiple experienced assessors will produce reliable results, if provided with clear, standardised criteria. This can enable health services to compare results across the organisation where multiple clinicians are applying the criteria, as well as within multi-site research studies.A GLIM malnutrition diagnosis in our study was found to only be significantly associated with BMI. Previous studies conducted in oncology patients using criteria other than GLIM have shown malnutrition to be significantly associated with older age ,7,16,38,The GLIM Consortium states that for admitted patients, 30 or 60 day readmission are relevant outcomes for use in predictive validity . Again, This study is only the second study to investigate the prevalence of malnutrition using GLIM criteria, and its predictive validity, in a cohort of HNC patients. The use of data that are commonly collected as part of standard clinical care to apply the GLIM criteria is a strength of this study. However, the use of metastatic disease to indicate inflammation rather than an objective measure is a limitation. This may have led to under-reporting of the presence of this etiologic criterion and therefore malnutrition prevalence. The cut offs used to apply the GLIM criteria differ slightly to those stated in the GLIM criteria, which may also have led to under-reporting. In some participants, anthropometry was self-reported. However, this has been shown to be reliable ,42. AddiThis is the first study to demonstrate excellent inter-rater reliability applying predetermined GLIM criteria to diagnose malnutrition in HNC patients, and one of the first to demonstrate that one in five HNC patients undergoing treatment are malnourished according to the GLIM criteria. Further large, prospective cohort studies are required in HNC patients that use an objective measure to determine inflammation, assess muscle stores for all patients, and apply the GLIM criteria to the whole cohort, as well as to determine the validity of the GLIM criteria to predict patient outcomes, including hospital readmission and one-year mortality.


The quantification of total ATP utilization is currently underexplored, as it requires simultaneous saturation of inorganic phosphate (Pi) and PCr. This is challenging, as currently available saturation pulses reduce the already‐low γ‐ATP signal present.Phosphorus saturation‐transfer experiments can quantify metabolic fluxes noninvasively. Typically, the forward flux through the creatine kinase reaction is investigated by observing the decrease in phosphocreatine (PCr) after saturation of Pi to enable determination of total ATP utilization. The pulses were evaluated in Bloch equation simulations, compared with a conventional hard‐cosine DANTE saturation sequence, before being applied to perfused rat hearts at 11.7 T.Using a hybrid optimal‐control and Shinnar‐Le Roux method, a quasi‐adiabatic RF pulse was designed for the dual saturation of PCr and B1, producing equivalent saturation with a 53% reduction in delivered pulse power and a 33‐fold reduction in spillover at the minimum effective B1. This enabled the complete quantification of the synthesis and degradation fluxes for ATP in 30‐45 minutes in the perfused rat heart. While the net synthesis flux  was not significantly different from degradation flux  and both measures are consistent with prior work, nonlinear error analysis highlights uncertainties in the Pi‐to‐ATP measurement that may explain a trend suggesting a possible imbalance.The quasi‐adiabatic pulse was insensitive to a >2.5‐fold variation in This work demonstrates a novel quasi‐adiabatic dual‐saturation RF pulse with significantly improved performance that can be used to measure ATP turnover in the heart in vivo. Together these coupled reactions determine how much ATP is synthesized and utilized in the heart. This reaction scheme (1) has been extensively studied in the heart using phosphorus magnetic resonance spectroscopy (31P‐MRS) saturation transfer approaches. Such methods permit the quantification of the rate constants for the CK reaction itself and the net rate of ATP hydrolysis and synthesis as a whole.γ‐ATP, for a period of time, and observing the subsequent depletion in signal of the other exchange partner, for example, PCr. If metabolite concentrations are known or measured, the total metabolic flux through any given part of the system can be calculated.The heart is a metabolic omnivore with the greatest energy requirement, per gram of tissue, of any organ in the body, in order to power contraction and maintain cellular membrane potentials. This requirement is met by sophisticated metabolic machinery, culminating in a high rate of adenosine triphosphate (ATP) hydrolysis and regeneration that is mediated through the “energy buffer” and subcellular transportation system that shuttles phosphocreatine (PCr), a moiety that forms the primary energy reserve of the heart,kf, kf′ and kr and kr′, it is the forward CK flux reaction kf that has been extensively studied in the heart, in part owing to the poor SNR of cardiac 31P‐MRS wherein PCr, having the highest SNR, provides the most accessible readout in the saturation transfer experiment in clinical settings. This remains highly biologically relevant because metabolic dysregulation plays a key role in common heart diseases,kf has additionally been associated with cardiac metabolic health, with reductions in kf reported in conditions ranging from obesityPi signal in the presence of overlapping signals from diphosphog lycerate compounds originating in the blood pool for the forward saturation transfer experiment; and the typically low signal‐to‐noise ratio (SNR) of Pi achievable in healthy tissue in clinically feasible scan times. We therefore wish to design a scheme to permit the role of the total reverse synthetic flux, (kr+kr′) to be revealed in humans and animals, in order to reveal further biological insight.However, while the use of saturation transfer experiments can provide quantitative measurements of Pi for varying durations, and the determination of the relative decrease in γ‐ATP that results. The determination of the total forward and reverse flux of ATP are obtained by fitting the acquired signals to solutions of the Bloch‐McConnell equations.γ‐ATP is typically worse than that of PCr, the dual‐saturation experiment has the potential to report on the biologically key energetic process: the net turnover of the whole cellular ATP pool, beyond that involved in the CK flux reaction alone.The reverse experiment is much more complex, as it requires the simultaneous saturation of PCr and kf and the forward CK reaction is technically easier to perform than the kf′/kr′ experiment requiring only narrow single‐band saturation pulses with a sharp stop‐band. Narrow bandwidth pulses can be made longer and tailored to better minimize Gibbs ringing than the larger bandwidth shorter pulses that are required to saturate both PCr and Pi simultaneously. Furthermore, the latter are typically associated with higher peak B1 fields, increased power requirements for the non‐proton RF amplifier and higher rates of RF‐specific absorption in the body .Additionally, M0PCR with μM0PCR,31P‐MRS surface coils, B1 is not uniform. It is therefore desirable to design adiabaticity into the RF pulse to provide tolerance to B1 inhomogeneity to aid its application.Even so, the length of the saturation pulses used in the conventional CK flux experiment is finite and their amplitude and center frequency difficult to adjust. It is common to have incomplete or excessive saturation and to accidentally spillover into neighboring resonances partially saturating them. Traditionally, a control experiment is performed, wherein the saturation frequency is shifted by the same frequency offset to the other side of the resonance of interest. While this typically leads to a first‐order correction for unwanted loss, replacing B1. The novelty of this design approach is that it combines the accuracy of computationally challenging optimal‐control methods which excel at constrained problems, with the speed and analytic underpinning of the SLR approach. Specifically, we present a highly uniform and temporally long dual‐band saturation pulse that simultaneously saturates PCr and Pi, and eliminates the need to obtain control data within each transfer experiment. While other adiabatic saturation pulses have been proposed in the context of 31P‐MRS for applications including the CK‐flux measurement,13C imaging,In this work, we demonstrate a hybrid optimal‐control RF pulse design scheme, with alterations to the alpha and beta polynomials after Shinnar‐Le Roux (SLR) transformation that optimize the passband and stop‐band homogeneity, while achieving low sensitivity to nonuniformity in the RF excitation field, We demonstrate the new pulse design scheme in a saturation transfer experiment on the Langendorff retrograde perfused rat heart at 11.7 T, to measure the energetic status of the heart as a whole. This setup is of particular value in drug studies on disease models, where the workload of the heart, oxygenation status, and pressure afterload can be independently controlled rapidly, repeatably, and with or without pharmacological alteration. It additionally does not contain blood, and thus intracellular phosphate is more readily observable.22.1B1 pose a set of unique challenges to the design of multi‐frequency saturation RF pulses. In comparison to low flip‐angle RF pulse design, the problem of designing efficient saturation pulses is complicated by the fact that the RF is not purely the Fourier transform of the desired excitation profile. A number of methods have been proposed to function in this regime. Most famous is the SLR approach,The extreme requirements of uniform saturation, low off‐resonance spillover, and a relative degree of insensitivity to A(z) and B(z) where z=e‐iω, which may be reversibly transformed to find an RF pulse.A and B polynomials and their equivalent frequency representation via a z‐transformation, which is related to the discrete Fourier transformation. The latter, in turn can be related to frequency‐domain Bloch equation simulations via a transformation that exploits the Cayley‐Klein representation of rotations.The SLR algorithm reduces the problem of pulse design to that of finding two polynomials, B1 homogeneity, it has been widely adopted and remains a “gold standard” for RF pulse design. It has been shown to be possible to expand the SLR design framework to generate adiabatic pulses, that is, pulses that are insensitive to a range of B1 values above a certain threshold. This is desirable, both for the heart at ultrahigh fields and for in vivo studies employing surface coils.Although SLR neglects relaxation terms and, conventionally, considerations relating to B(ω) frequency domain, modulating the linear phase generated by a filter designed with the specified single‐band pass/stop‐band requirements. The A(ω) polynomial is designed under a least‐phase constraint, to yield a minimum‐energy (least SAR) pulse with a quadratic phase variation. Not all quadratic phase RF pulses are adiabatic, but those with a resulting frequency ramp that is slow enough to satisfy the adiabatic condition will be so. It remains uncertain how to directly specify multiple frequencies for excitation or saturation within this framework, as it requires root‐flipping procedures to minimize the peak amplitude of a fixed duration pulse, or equivalently, reducing its duration with a fixed peak amplitude. This process involves replacing the selected roots of its A(z) and/or B(z) polynomials with the inverses of their complex conjugates. The best pattern of flipped roots produces the most uniform distribution of RF energy in time, imitating a quadratic phase pulse.B(ω) domain.Briefly,z—the Bloch equations in the rotating frame can be written as u(t)=(ux(t),uy(t)) describes the normalized, nondimensional RF pulse and the linearized Bloch rotation matrix A and longitudinal relaxation vector b are given by Gz(t) an applied slice‐selection gradient.An alternative framework for designing RF pulses in MR based on optimal control considers the Bloch equations without exchange, and minimizes an integrated metric for the difference between their current and the desired solution. Typically this is performed with a cost term based on the square of the RF pulse amplitude incorporated as an SAR penalty. Under these conditions, the design problem falls into the analytical framework of linear quadratic control. Assuming one spatial degree of freedom—a slice selection gradient along a slice axis u(t) of a prespecified duration Tu that minimizes a chosen cost function after it is played (at t>Tu for time T=Tu+ϵ where ε ≪ 1) to generate a numerically determined solution M as compared to a desired predefined solution, Md(z), within a finite computational domain . Including the quadratic cost function to accommodate the practical limitations on RF amplifier power and SAR limitations, the one‐dimensional pulse design problem is Within the framework of optimal control, we seek an RF pulse λ effectively acts as a regularization term that relates the competing goals of pulse fidelity and SAR reduction. This minimization problem is then typically approached via numerical methods. However, optimal‐control algorithms remain far more computationally expensive than the matrix approach used by SLR transformation, and convergence is slow with most naïve gradient‐descent or quasi‐Gauss Newton approaches.Here B1 variation. While examples of successful applications exist,While Equation (4) is modifiable to problems beyond one dimension, the extension of this method into ≥2 dimensions becomes challenging in practice.Pi saturation in the present application. Furthermore, an ability to excite PCr and Pi in antiphase analogous to a CAIPIRINHA‐based simultaneous multislice excitation,For the one‐dimensional RF pulse design problem, Aigner et al33.1Pi saturation. The new pulses are given a degree of adiabaticity by the imposition of a quadratic phase in the A, B polynomial domain. The general approach can be summarized asThis work combines the SLR and optimal‐control approaches described above to provide dual‐band saturation with minimal ripple for simultaneous PCr/T1 and T2 values are included explicitly in this framework for each metabolite, as the optimal‐control framework explicitly includes relaxation in pulse design. The result is a nonadiabatic pulse u0(t).Highly optimized optimal‐control pulses are developed subject to hardware constraints, using the analytically enhanced optimal‐control framework.A and B polynomials for each pulse, that is (A(z),B(z))=SLR(u0(t)).Obtain the resulting b of the B polynomial, which would be equal to the linear phase profile of the pulse traditionally generated via filter‐design methods in the conventional SLR approach, say F(b)=Ffilter(ω).Obtain the discrete Fourier transform of the coefficients Ffilter(ω)↦Ffilter(ω)×eikω2 where k is a constant that determines the rate of quadratic phase cycling across, described in detail elsewhere.Impose a quadratic phase in the frequency domain: B polynomial coefficients: b′=F‐1Ffilter(ω)×eikω2.Inverse the process to obtain an updated set of u(t)=SLR‐1.Obtain the optimized waveform with a degree of adiabaticity imposed, B polynomial does not affect the spectral bandwidth(s) of the pulse, but increases its overall duration and decreases the effective peak B1 value.This approach is computationally quick, and produces a far simpler optimization problem than using a full optimal‐control framework for the design of adiabatic pulses with multiple excitation bands. The imposition of quadratic phase on the 31P saturation transfer experiment at 202 MHz. This was a dual‐band excitation with a 950 Hz gap between saturation bands, a 150 Hz FWHM of each pole. The pulse was 25 ms in length comprised of 2500 points, and k=4.1×10‐6, with a nominal B1 of 1.3 μT. The optimal‐control algorithm was initiated with a hard‐cosine pulse and a target magnetization consisting of two slabs filtered with a 2.5 Hz transition width convolved with a Gaussian kernel. The optimal‐control methods were otherwise as described previously,|Mxy|M0 at 0 Hz, of 10‐7. The simulation was allowed to converge before the manual imposition of quadratic phase, which took about 4 hours on a MacBookPro15,3 laptop computer (2.9 GHz Intel Core i9 processor). The resulting pulse is shown in Figure 90∘ pulse with a subsequent gradient crusher . Temperature was monitored and maintained within specimens using a separate platinum 100Ω ‘PT100’ 4‐wire resistive thermal device, and using PID  control through the heater.All experiments were performed on a vertical‐bore 11.7 T spectrometer (Magnex Scientific/Varian DDR2) at a constant The new saturation pulse was initially tested on a 100 mM phenylphosphoric acid (PPA) phantom. The PPA has a single peak ∼10 times larger than the PCr peak seen in vivo, and saturation efficiency was assessed by iteratively offsetting the transmitter voltage and frequency in 41 steps between  ±2 kHz.KH2PO4 to prevent the slow loss of intracellular phosphate. The heart was then weighed, and rapidly cannulated via the aorta prior to Langendorff perfusion with Krebs‐Henseleit buffer at 37.2∘C and 100 mm Hg, delivered in a warmed umbilical. A balloon containing 100 mM PPA was introduced into the left ventricle (LV), and inflated to achieve an end‐diastolic pressure of 3‐5 mm Hg. The heart was then placed in the warmed NMR tube and homeothermic MR coil within the magnet. All animal investigations conformed to Home Office Guidance on the Operation of the Animals (Scientific Procedures) Act of 1986 (ASPA), to institutional guidelines, and were separately approved by the University of Oxford Animal Ethics Review Committee. All compounds were obtained from Sigma Aldrich .For experiments on the perfused heart, male Wistar rats (∼250 g) were sacrificed via i.p. pentobarbitone anesthetic overdose (0.7 ml of 200 mg/ml). Hearts were rapidly excised and arrested in ice‐cold Krebs‐Henseleit buffer, containing 1.2 mM 3.331P spectrum based Ernst‐angle frequency adjustment; (d) acquisition of a 5‐minute fully relaxed 31P spectrum; (e) single‐band and dual‐band saturation‐transfer experiments; and (f) acquisition of three 1‐minute fully relaxed spectra to obtain the volume of the internal PPA phantom to use as a concentration reference (see below) and for the fully relaxed metabolite signals at the end of the perfusion experiment. The entire protocol took 30‐45 minutes, including the preparation and insertion of the perfused heart into the MR system.The measurement protocol for assessing total myocardial energetics was comprised of: (a) the acquisition of localizers; (b) shimming; (c) a 90∘ flip angle, 10 kHz bandwidth, 2048 complex points) to enable absolute quantification based on the internal PPA phantom and volume, and the weight of the heart. We note that the presence of the phantom inside the LV ensures that the phantom signal is detected with substantially the same sensitivity as that of the myocardium. Trains of either single‐band or dual‐band saturation pulses of total duration  seconds, were applied, followed by all‐axes gradient crusher pulses, the hard excitation pulse, and FID readout. After acquisition of fully relaxed, and selectively saturated spectra, absolute quantification (protocol step f) was performed by adding two known volumes of PPA  to the internal PPA phantom and obtaining two separate fully relaxed spectra. This process accounts for variability in heart sizes, and hence LV balloon volumes since the balloon is inflated to a diastolic pressure that ensures retrograde perfusion via the coronary sinus and arteries during diastole.The saturation pulse was developed as a “module” that preceded a simple hard‐pulse‐acquire spectroscopic readout. This was applied (protocol step e) under fully relaxed conditions  to obtain the volume of the 100 mM PPA present during the saturation transfer experiments (step e). Absolute metabolite concentrations were calculated using an intracellular volume fraction for healthy myocardium of 52% and tissue specific gravity of ∼1.05 g/ml.Spectra were fitted in the time domain via the OXSA implementation of the AMARES algorithmk=kf if Y is the amplitude of PCr, kf′ of Pi, or (kr+kr′) if it is γ‐ATP following dual saturation. For this, a global optimization algorithm was used with the linear constraint that τ−k ≥ 0 and all parameters ≥0.Saturation transfer data were fitted to integrated solutions of the Bloch‐McConnell equations via a bounded nonlinear least‐squares method, in the form 4B1 variation, with the optimized dual‐band pulse featuring a dramatic increase in immunity to variation compared to the conventional hard‐cosine pulses when used either for 90∘ excitation  has three linear but interleaved frequency ramps with constant offsets, characteristic of adiabatic pulses.Bloch equation simulations revealed that the designed dual‐band quasi‐adiabatic pulse demonstrates a far greater immunity to n Figure A,B or inPi with >99% saturation efficiency on both resonances, the pulse required a 70% higher peak B1 (0.32 μT vs 0.55 μT) but possessed a B1rms that was 50% lower: 0.0516 μT for the hard‐cosine vs 0.0275 μT. This corresponded to a lower integrated power deposition (P∝∫0Tu|B1(t)|2dt) over the duration of the longest DANTE saturation chain considered, that is, 4.58 seconds: the deposited power of the new pulse is just 53.4% of that for the hard‐cosine pulse. Similarly, at the frequency offset corresponding to γ‐ATP , the designed pulse featured a relative excitation of ∼10‐9 in comparison to 10‐3 for a conventional hard‐cosine pulse. This improvement in selectivity, by six orders of magnitude, translates into a dramatic reduction in the degree of spill‐over following saturation by a DANTE chain. At the frequency of γ‐ATP, a 10% erroneous saturation occurs if the hard‐cosine pulse is played at or above a peak B1 of 0.63 μT, whereas the designed novel pulse does not reach that point until peak B1 exceeds 4.2 μT. This therefore gives it a greatly expanded range in which B1 variation does not significantly affect the γ‐ATP signal; the saturation remains ≤1% until peak B1>1.5μT, yielding a ≥2.5‐fold effective immunity to B1 variation. When played at the minimum effective B1, spillover at γ‐ATP is therefore ∼0.03% compared to 1% for the hard‐cosine pulse, 33‐fold lower.Compared to an equivalent hard‐cosine DANTE chain saturating both PCr and The predicted frequency‐domain response was verified with the PPA phantom Figure A. A contn = 5 naïve Wistar rat hearts show that that no measurable saturation correction is needed when the pulse is applied off‐resonance, as illustrated in Figure Single‐band and dual‐band saturation experiments performed on 31P‐containing moieties absolutely in the heart, and the developed saturation pulse permits the quantification of reaction rates. Multiplying the reaction rates by the corresponding concentrations, yielded the total ATP synthesis and degradation fluxes for the heart in approximately 30‐45 minutes, a duration compatible with perfused heart experiments. Spectra are shown with a 20 Hz apodization in the form of exponential line‐broadening. While it is the case that 31P MRS is inherently insensitive, the ability of the AMARES algorithm to quantify both peak amplitudes and the Cramér‐Rao lower bound of uncertainty on those amplitudes permits a quantitative analysis of the limitations of the technique, as detailed in the Online Supporting Information. In our work, the mean ATP concentration was 7.31 ± 0.5 mM and the mean PCr concentration was 11.6 ± 0.7 mM =1.05±0.19s‐1. The corresponding synthesis and degradation fluxes for ATP were thus 4.24 ± 0.82 mM/s and 6.88 ± 1.82mM/s . Heart function, as quantified via developed pressure, did not decrease significantly throughout the duration of perfusion.As illustrated in Figure 5Pi and PCr with a ≥2.5‐fold immunity to B1 at ultrahigh field, and hence permit the determination of (kr+kr′). The constraint on B12 applied in the optimal‐control scheme used for designing pulses, reduces the SAR required to achieve practical pulses that can be used in animal models and could be adapted for human studies at 3 T and 7 T systems within the same design framework. Compared to an optimized single‐band saturation pulse that broadly saturates both PCr and Pi, the dual‐band pulse is significantly longer with a lower B1rms, has sharper transition bands, and offers minimized off‐resonance spill‐over irradiation. The use of dual‐band saturation permitted the rapid quantification of the total ATP degradation flux from a fully relaxed 31P spectrum, and one additional saturation transfer experiment. The increased temporal resolution potentially afforded by the omission of the control saturation data allows for dynamic experiments and extends the viability of the perfused heart model, wherein cardiac performance slowly deteriorates after a ∼2 hour window. We note that the designed quasi‐adiabatic multiple‐band saturation or excitation pulses could be incorporated into more widely used saturation schemes, such as FAST,31P MRS. It may also find wider applications in that require selective excitation, including simultaneous multislice imaging with a degree of immunity to B1 variation; the selective excitation of metabolites in hyperpolarized metabolic imaging; chemical exchange saturation transfer (CEST), and spatial tagging of multiple arteries or planes in the arterial spin labeling experiment.Here we have demonstrated the development of a quasi‐adiabatic dual‐band saturation pulse that is able to efficiently and simultaneously saturate both 31P MRS is both inherently a low SNR technique, and one subject to a number of potentially confounding factors. While the low SNR is both quantifiable and can be analytically understood , permitting the determination of intracellular Pi. It is also guaranteed to be free of spectral contamination from nearby skeletal muscle and liver, such as may occur in the in vivo situation depending upon the spatial response function of the localization sequence used. It additionally offers the potential to measure cardiac work, and thus quantifies both cardiac energy supply and utilization to support contractile function, although this was not demonstrated here. As a model system, the perfused heart does slowly deteriorate over time, which typically manifests as slight changes in pH (leading to a reported heterogeneous distribution in true intracellular pH),While powerful, noninvasive, and scientifically important, we note that kf: our 0.27±0.05s‐1 compares to 0.35±0.06s‐1 in the in vivo healthy control rat heart0.32±0.05s‐1 in the healthy human heart.(kr+kr′)=1.05±0.19s‐1 agree with a value of 1.15±0.19s‐1 measured by Spencer et al. using hard cosine saturation pulses.Pi peak is the single greatest source of uncertainty in the total estimated flux, and thus improving its quantification is of greatest importance. This could be achieved without increasing the total scan time either by increasing B0, or, given the comparatively small size of the perfused heart, the use of a cryocoil. Second, increasing the number of averages, and thus the total scan time would lead to greater spectral SNR, but potentially at the cost of the variability and stability of the perfused heart. Third, another alternative is to increase n, the number of experiments performed, and increase statistical power and average out the effect of the uncertainty in measurements.The results of the experimental studies conducted here nevertheless are in quantitative agreement with previously reported values of kf can be determined accurately owing to the larger size of the PCr peak, the determination of kf′ is much more challenging owing to the smaller size of the intracellular Pi peak, which can only readily be resolved in a cardiac setting in the perfused heart. The addition of phosphate buffer to the perfusate increases the size of this peak via the slow intracellular import of phosphate making it easier to measure, but not fundamentally changing the kinetics through ATP‐synthase. This slow import will, however, make the determination of total flux mildly dependent upon the time after perfusion at which it was measured, because it is necessary to multiply the concentration of inorganic phosphate by kf′ to calculate the total flux. Consequently, although our determined kf′=0.21±0.06s‐1 is not significantly different to that reported by Spencer et al. of 0.37±0.09s‐1, the combined effect of measurement uncertainty  on both the rate constant and absolute concentration may combine unfavorably.We note that despite these inherent sources of variability phosphorus spectroscopy has found a successful niche as a powerful research tool, where results from numerous patients or participants can be averaged. In this proof‐of‐principle work, we explicitly determine the contribution to the uncertainties in ATP synthesis and degradation fluxes by analysis of the nonlinear error propagation of both saturation transfer schemes. In particular, while σPCr etc denote the uncertainty on a reported value of [PCr] etc, estimated by its CRLB, with σkf etc for the rate constants determined by a curve‐fitting regime, then a Taylor expansion for error propagation for the synthesis (Synth) and degradation (Deg.) flux shows, given (kr+kr′) or kf directly. If it is assumed that the subject is physiologically stable, and that the MR‐visible ATP synthesis and degradation fluxes are equal, then we would expect to see less variability in the determined rate constants of interest, and the remaining one could be inferred from flux balance. This approach would rely upon measuring γ‐ATP accurately as a function of time, something that is routinely performed in human phosphorus spectroscopy, in addition to the kf conventional CK‐flux experiment. The difficulty in resolving inorganic phosphate thus is neatly sidestepped in the reverse experiment: as there is no need to resolve it in order to saturate it effectively, and effective saturation could be confirmed by the simultaneous depletion of phosphocreatine, which can easily be monitored.Analytically, if 6B1 insensitivity suitable for ultrahigh field NMR in experimental animals and the human heart at ≥7 T. The resultant pulse was exceptionally flat, with no experimentally detectable “spillover”. It was incorporated into a protocol to assess the high‐energy phosphate compounds and the ATP synthesis and degradation rates in the perfused rat heart at 11.7 T, providing measures in good agreement with the literature. We hope this technology will be useful in future studies intent on quantifying the total flux of ATP turnover in the heart, and in other areas of MR where quasi‐adiabatic multifrequency saturation is required.This work demonstrates a novel approach for the design and application of a quasi‐adiabatic dual‐frequency saturation pulse, suitable for measuring forward and reverse metabolic reaction rates. The design employs a hybrid optimal‐control and SLR transformation to achieve sharp frequency selection and a degree of i written for this purpose . The authors would welcome further collaboration requests for additional RF pulse design problems.All spectrally‐quantified data are available either in Table 1, or in the Online Supporting Information. Raw data can be obtained by emailing the corresponding author with a reasonable request. Temperature control was performed using a second‐hand Bruker BVT3000 variable‐temperature NMR unit and an open‐source re‐implementation of its control software running on a Raspberry PTABLE S1 Summary uncertainty values in derived concentrationsTABLE S2 Summary of parameters (Heart 1)TABLE S3 Raw metabolite MRS peak amplitudes and CRLBs, and determined metabolite concentration and Δ Concentration. (Heart 1)TABLE S4 Summary of parameters (Heart 2)TABLE S5 Raw metabolite MRS peak amplitudes and CRLBs, and determined metabolite concentration and ΔConcentration. (Heart 2)TABLE S6 Summary of parameters (Heart 3)TABLE S7 Raw metabolite MRS peak amplitudes and CRLBs, and determined metabolite concentration and ΔConcentration. (Heart 3)TABLE S8 Summary of parameters (Heart 4)TABLE S9 Raw metabolite MRS peak amplitudes and CRLBs, and determined metabolite concentration and ΔConcentration. (Heart 4)TABLE S10 Summary of parameters (Heart 5)TABLE S11 Raw metabolite MRS peak amplitudes and CRLBs, and determined metabolite concentration and ΔConcentration. (Heart 5)Click here for additional data file.