Chronic liver diseases pose a substantial health burden worldwide, with approximately two million deaths each year. Branched-chain amino acids (BCAAs)—valine, leucine, and isoleucine—are a group of essential amino acids that are essential for human health. Despite the necessity of a dietary intake of BCAA, emerging data indicate the undeniable correlation between elevated circulating BCAA levels and chronic liver diseases, including non-alcoholic fatty liver diseases (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC). Moreover, circulatory BCAAs were positively associated with a higher cholesterol level, liver fat content, and insulin resistance (IR). However, BCAA supplementation was found to provide positive outcomes in cirrhosis and HCC patients. This review will attempt to address the contradictory claims found in the literature, with a special focus on BCAAs’ distribution, key signaling pathways, and the modulation of gut microbiota. This should provide a better understanding of BCAAs’ possible contribution to liver health. In recent years, chronic liver diseases (CLDs) have become more common worldwide. It is estimated that over eight million people are currently suffering from CLDs . CLDs arAmino acids (AA) are an essential nutrient for human health. The building block of proteins are amino acids, which are separated into two main categories: essential amino acids and non-essential amino acids. Essential amino acids are amino acids that cannot be synthesized by humans, and thus have to be supplied from an exogenous diet, while non-essential amino acids are amino acids that can be synthesized in the body . One groThe processes of BCAAs’ synthesis and metabolism have been extensively reviewed ,20. In tIt is known that, upon oral intake of BCAAs, they circulate in the bloodstream, by-passing the first-pass metabolism in the liver due to the low activity of branched-chain amino transferases . BrancheIt is important to note that muscle tissues are not gluconeogenic; thus, if these BCAAs  cannot be fully utilized by the muscles, they must be removed . The transformation of BCKAs by branched-chain α-keto acid dehydrogenase (BCKDH) is the next step in BCAA catabolism, and this enzyme is also known to be the rate-limiting step in the BCAAs’ catabolism pathway. Notably, its levels are very low in skeletal muscles, and are the highest in the liver and the heart . Thus, uBCAAs can trigger different types of signaling , dependiOn the other hand, BCAA consumption during rest or exercise recovery triggers a pathway that redirects the BCAAs’ metabolism into protein synthesis or restoring and building up glycogen storage in muscles/liver. The increase in insulin triggers the activation of insulin receptor substrate (IRS1) and leads to the activation of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, which in turn activates mTOR complex 1 (mTORC1) via the phosphorylation of TSC1/2, while the increase in the amino acid pool directly triggers the mTORC1 pathway. This pathway plays the important role of maintaining cell proliferation, cell cycle, angiogenesis, apoptosis, and metabolism , and is BCAAs and their metabolites were found to be able to attenuate PI3K/Akt signaling on other tissues, such as the liver, and this was thought to be the main mechanism by which BCAAs bring about beneficial health outcomes beyond nutrition ,32. The In a hepatic tumor cell model, BCAA supplementation has been found to inhibit cell proliferation via decreasing the activity of the PI3K/Akt pathway . FurtherIn contrast to the documented beneficial effect of BCAA supplementation in cell culture models, higher BCAA circulatory levels were found in NAFLD patients ,48,49. TA large-scale clinical study on NAFLD subjects without T2D provided insight into the synergistic effect of NAFLD and the elevated BCAA levels on the development of type 2 diabetes. The total plasma BCAAs were positively correlated with a high fatty liver index (FLI), which was calculated from the levels of blood triglycerides, blood gamma-glutamyl-transferase, BMI, and waist circumference. In the 7.3-year follow-up analysis, nearly 20% of patients with elevated FLI were found to develop T2D. This elevation was suggested to be linked with the impaired hepatic mitochondrial function and increased mitochondrial lipid β-oxidation in NAFLD . AlthougPatients with both NAFLD and obesity have a higher BCAA circulatory level than non-obese NAFLD patients. In an Italian cohort with non-obese NAFLD patients, obese NAFLD patients, and healthy subjects, the rise in BCAA levels was more profound in the obese NAFLD group when compared to the healthy control, while valine and isoleucine levels were only significantly higher in the obese NAFLD group . These fSer307 [The elevated BCAAs levels were also found to contribute to IR. IR was also found to be positively correlated with the rise in BCAA levels in NAFLD and fibrosis patients ,58. The Ser307 . HoweverSer307 .The rise in plasma BCAA levels in NAFLD patients was also found to be sex-dependent. Male subjects were found to have significantly higher BCAA levels than female subjects. Plasma BCAA levels in female subjects were correlated with NAFLD and fibrosis stages, while the opposite result was found in male subjects. Leucine and valine were inversely correlated with NAFLD stages in males. Nevertheless, without considering the gender differences, leucine and isoleucine were significantly associated with NAFLD stages .Since circulatory BCAA levels were consistently found to be significantly increased in liver diseases, the possibility of using circulating BCAAs’ concentration as a diagnostic tool was suggested. A study on obese children found a high area under the curve (AUC), 0.92 , for using BCAA to discriminate between severe steatosis and a healthy obese subject, while an AUC of 0.82 (95% CI 0.67–0.97) could be used for the discrimination of any steatosis . The elePlasma BCAA levels were found to be significantly increased and have been identified as a biomarker of progression to HCC . A low BThe rise in BCAAs was not limited to their systemic level. A recent study found an increase in tissue BCAA level in HCC patients with severe fibrosis and cirrhosis. In 52 paired HCC tumor and nontumor tissues, BCAAs were found to be elevated in HCC tissue when compared with adjacent non-tumoral tissues . The samIn livers of HCC patients, and animal models, including high-fat diet-induced obesity and HCC tumor models, BCKDH activity and expression were found to be downregulated, and BCKDH kinase (BCKDK), the enzyme responsible for suppressing the activity of BCKDH, was found to be upregulated . The conDespite the association between elevated blood BCAA levels and negative conditions in liver diseases, the consumption of BCAA supplements was previously linked to a beneficial outcome in various liver diseases, especially during advanced fibrosis or cirrhosis, and especially hepatic encephalopathy. BCAA supplementation is recommended to cirrhotic patients according to the guidelines of the American Association for the Study of Liver Diseases (AASLD) and the European Association for the Study of the Liver (EASL) . Table 1In three separate studies, the supplementation of BCAAs in the diet of patients with advanced liver cirrhosis resulted in a significant improvement in major cirrhosis-related events, including improvements in Child–Pugh (CP) score, MELD score, and/or a significantly higher number of patients with event-free survival ,71,72. TA DEN-injected rat liver injury model showed that BCAAs significantly lowered dysplastic nodules. Although BCAAs could not prevent progression to malignant tumors, the supplementation prevented liver neoplasm lesions . This efOver the course of NAFLD/NASH progression, cirrhosis may also develop; therefore, BCAA supplementation has also been increasingly investigated to treat these diseases, and/or prevent them from progressing to cirrhosis. Although there is a lack of human studies utilizing BCAAs to treat NAFLD or NASH patients, some animal studies have pointed to a potential positive outcome of its utilization, although the results are controversial and not conclusive. In a choline-deficient, high-fat diet-induced NASH mice model, BCAA lowered serum ALT levels and hepatic triglyceride, while the liver histology showed that the lipid droplet area and fatty acid synthase (FAS) were lowered . SimilarThe full mechanism of how BCAAs prevent further deterioration in chronic liver diseases remains largely unclear. An explanation for this may be that the supplementation of BCAAs could elevate its catabolism via directly affecting the levels of its catabolizing enzyme. In particular, the increase in PPAR-α expression by BCAAs, through AMPK and an increase in serum-free fatty acid levels, could prevent the increase in BCKDK activity, preventing the suppression of BCKDH activity in catabolizing BCKAs ,83. The In recent decades, mounting evidence has unveiled the crucial role of gut microbiota in metabolism. Our gut is home to a large amount of gut microbiota, from fungi and archaea to bacteria. Recent evidence has confirmed the crucial contribution of gut microbial dysbiosis to NAFLD pathogenesis, scrutinizing the importance of homeostasis in the gut–liver axis. Shotgun sequencing results from feces of metabolic-associated NAFLD patients displayed an elevated abundance of ethanol-producing bacteria and a decreased abundance of butyrate-producing bacteria ,92. In oRuminococcus flavefaciens. Iwao et al. showed that cellulose was necessary for the beneficial effect of BCAAs, which highlighted the involvement of gut microbiota [Bifidobacterium species in healthy mice [Bifidobacterium strains to high-fat-fed mice showed an increase in GLP-1 secretion via the increase in short-chain fatty acids (SCFA) levels, particularly acetate [Several rodent studies have provided evidence on how gut microbiota regulate BCAA levels and subsequently contribute to liver disease. A study on BCAA supplementation to rats fed a high-fat diet showed that BCAAs increased the beneficial gut microbiota crobiota . BCAA suthy mice . The ora acetate . The inc acetate .Faecalibacterium prausnitzii levels to be inversely correlated with serum BCAA levels, insulin levels, and HOMA-IR. Faecalibacterium prausnitzii contributed to the majority of bacterial BCAA transporters’ gene count [Faecalibacterium prausnitzii is one of the most common gut microbe species in healthy adults, accounting for more than 5% of the total population, and their abundance was decreased in steatosis patients [F. prausnitzii to mice fed a high-fat diet improves parameters related to hepatic health, including AST, ALT serum levels, improved glucose tolerance, and insulin sensitivity, and decreased steatosis in the liver [A limited number of studies investigated the correlation between gut microbiota composition and circulating BCAA levels in patients with liver diseases. A recent study on healthy adolescence found fecal ne count . Faecalipatients ,98. The he liver .Bacteroides vulgatus and Bacteroides dorei, were reported to improve BCAAs’ catabolism in brown adipose tissue and improve systemic glucose tolerance and insulin sensitivity in high-fat diet-induced obesity mice [B. vulgatus abundance is positively correlated with insulin resistance in NAFLD patients. The team found that in 277 non-diabetic insulin-resistance patients, Prevotella copri and Bacteroides vulgatus were identified as the main species promoting insulin resistance by driving bacterial BCAAs’ synthesis [B. vulgatus and Eubacterium rectale [B. vulgatus also confer the same beneficial effects on improving BCAA catabolism if supplemented in NAFLD patients. Nonetheless, Pedersen et al. only reported the adverse effect of gavaging P. copri, in which glucose intolerance and serum BCAA levels were elevated in high-fat diet-fed mice. Prevotella copri enrichment was also found to be specifically enriched in advanced fibrosis in a study involving 39 NAFLD patients with fibrosis [In relation to BCAAs’ metabolism, other members of the gut microbiota, ity mice . These sity mice . Howeverynthesis . Similar rectale . Thus, ifibrosis .Ruminococcus flavefaciens and/or Bifidobacterium species, vs. the contrasting involvement of the gut microbiota Bacteroides vulgatus and Prevotella copri in driving bacterial BCAAs’ synthesis in NAFLD patients (In summary, recent studies proposed the possibility of utilizing BCAAs as a non-invasive marker for liver disease. While higher circulatory levels of BCAAs were found in NAFLD, NASH, cirrhosis, and HCC patients, the supplementation of BCAAs was found to be beneficial in liver diseases. The contradictory role of BCAAs could be due to the varied gut microbiota composition, in which the supplementation of BCAAs increased beneficial gut microbiota: patients .The current understanding and knowledge of the beneficial effects of BCAA supplementation in liver diseases remains inconclusive and is mainly derived from cell culture and animal studies, which cannot fully translate the etiology of human liver diseases and inter-individual variability. In terms of human studies, the majority were conducted without diet standardization, i.e., with a defined caloric intake and protein consumption. This is a crucial point to consider, since previous animal studies showed that diet is one of the crucial factors in chronic liver diseases, due to its relationship with the gut microbiome, especially in cases of NAFLD and NASH. Furthermore, limited research has been conducted to show how valine, leucine, or isoleucine, as compounds on their own, influence the outcome of liver diseases. Even though BCAAs share similar metabolic pathways and functions, it is unclear whether their beneficial effects rely on the combination of or an individual BCAA, as each BCAA has different metabolic effects. Additionally, prior research generally primarily focused on the clinical outcome of BCAAs’ administration, with few studies examining the correlation between BCAAs and gut microbiota in patients with liver disease and the mechanism of action. In recent years, the contribution of the gut–liver axis to the outlook of metabolic diseases has been extensively studied, but whether the gut microbiota could be the key regulator of the rise in BCAA levels is an area for future investigation.


Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions. The ability of insect herbivores to cope with plant specialized metabolites (PSMs) is an important driver of ecosystem functioning . In part2+, production of reactive oxygen species (ROS), and activity of mitogen-activated protein kinase (MAPK) , thereby increasing their solubility and excretion rate  that could be applied to plant-herbivore interactions and increase our knowledge about PSM detoxification and sequestration.This review focuses on insect resistance to PSMs and provides a guide of current and emerging technologies that can be employed to characterize PSM metabolism, sequestration, and active excretion in insects. In particular, this review highlights how to depict PSM Absorption, Distribution, Metabolism, and Excretion , resulting in diets of different complexity. While diets based on host plant material may match the insects’ dietary requirements most closely and likely contain important chemical feeding stimuli, they will also contain PSMs and/or enzymes that metabolize PSMs. Using purified diet, e.g., produced from essential and controlled ingredients only, avoids the presence of PSMs and ‘undesirable’ degrading enzymes and reduces the diet batch-to-batch variability. On the other hand, artificial diet composition can also affect activity of insect digestive enzymes. For example, the activity of glucose oxidase is ten times higher in caterpillars of the beet armyworm, Spodoptera exigua, when raised on a wheat germ-based artificial diet compared to when raised on a host plant . Limited solubility of many lipophilic PSM in water-based diets may require dissolving PSM in organic solvents which need to be included into PSM-free control diets as well to correct for toxic effects of the solvent , radiation mutagenesis , insertional mutagenesis ), RNA interference (RNAi), and Clustered Regularly Interspaced Palindromic Repeats-CRISPR-associated protein 9-based (CRISPR/Cas9) genome editing technology. An elegant review on the latest progress in plant transformation was recently published , the RIRARGE II ), or theRARGE II ). Maize RARGE II . Mutants mutants . Additio mutants , tomato  mutants , rice  may also be created if naturally occurring PSM mutants are available . PSM mutin planta within specific storage structures, thus spiking of PSMs into artificial diets or onto leaf discs may not result in normal activity. Furthermore, molecular manipulation of host plant PSM profiles may have pleiotropic effects, either directly due to the transformation procedure, or due to the multiple roles of PSMs in planta. It has been recognized that some PSMs are involved in primary plant processes such as signaling, nutrition, and growth  requires avoiding contamination by gut contents which otherwise could result in false positive results. Starvation of insects prior to analyses should reduce the amount of plant chemicals in the insect gut but may be an insufficient approach given that insects can accumulate and retain plant toxins in the gut [as recently described for cardenolides in monarch butterfly caterpillars (3H) cardenolides, followed by feeding on toxin-free sunflower seeds for 3 days still contained the radioactive label, which was substantially reduced after 10 days of feeding on sunflower seeds  by ice crystals during freezing. For dissecting, insects should be anesthetized by chilling on ice which may work differentially well for different insect species. For example, caterpillars from warmer regions such as the monarch butterfly will remain motionless for a longer period of time compared to caterpillars from temperate regions (as observed for selected species of geometrid caterpillars). Using a chilled dissection stage such as Petri dishes filled with crushed ice would be a solution to overcome this problem. As an alternative to chilling, insects can be anaesthetized with CO2. During dissection, care needs to be taken not to puncture the intestine to prevent plant compounds from spilling over into other body compartments. Therefore, it is advisable to leave the head and anus attached to the gut before taking it out to remove leakage of PSM containing gut contents into other body tissues whenever possible. Lepidopteran caterpillars frozen and stored at −80°C in tightly sealed containers that are dissected immediately after thawing display very well-preserved tissue morphology, so that dissection of insects after storing might represent a valid alternative to dissecting live specimens. For chemical analyses of diffusible PSM within tissues, it may be advisable to dissect specimens without immersion in a buffer to prevent a washout of analytes. Depending on the research questions to be addressed, especially organs involved in detoxification such as the fat body or the Malpighian tubules might be of special interest. For the analyses of macromolecules such as enzymes or RNA, dissecting under a physiological buffer such as PBS  will greatly facilitate the recovery of fragile organ systems. Besides tissue disruption, freezing and thawing will also increase degradation of labile plant compounds, especially secondary active glucosides easily hydrolyzed by enzymes. One recent approach to overcome both analyte dislocation and degradation is to dissect insect specimens after freeze drying. This method has been used successfully for dananine caterpillars . Targeted metabolomics thus is a useful tool for PSM budget analyses and can provide information on the efficiency of detoxification/sequestration, and even on the use and efficiency of specific metabolic pathways by different insect species. If major discrepancies appear between PSMs and their known metabolic products in such an analysis, an untargeted metabolomic approach may become necessary, ideally combined with feeding assays using labeled compounds  have been well-established , and aree.g., isothiocyanates, e.g., alkaloids, Volatile and non-thermosensitive compounds are best analyzed by GC-MS, either by directly adsorbing volatiles to a matrix before thermal desorption  metabolomic experiments, detailed and accurate reporting of sample preparation, extraction, and analysis is crucial to ensure repeatability of results, and minimum reporting standards for metabolomics experiments should be used .e.g., Detoxification and sequestration of PSMs by insects commonly involve diverse, and often unpredictable, metabolic modifications and conversions of the plant compounds by insect enzymes. An untargeted metabolomics approach can therefore serve as an important first step to determine the metabolic fate of PSMs after insect consumption. For example, specialist herbivores of Brassicaceae plants have developed many unique strategies that allow them to cope with the toxic glucosinolate PSMs of their host plant, including conjugation of the compounds with glutathione or various amino acids, desulfation of the compound, or nitrile formation upon compound breakdown . Novel pIn essence, untargeted metabolomics compares two or more sample types that were exposed to different treatments and aims to identify compounds whose abundance correlates significantly with these treatments. As the insect-derived metabolites of PSMs are not always known and sometimes difficult to predict, untargeted metabolomic methods aim to capture the full set of polar and semi-polar metabolites of low molecular weight  in a biological sample . WhereasData analysis is the key aspect that distinguishes untargeted from targeted metabolomics. Whereas targeted metabolomics focuses on a small number of known compounds, a single untargeted metabolomics experiment will typically generate >10,000 mass features, thus effective methods for data normalization, deconvolution (grouping of mass features belonging to the same compound), filtering, and identification of compounds of interests are essential . To idene.g., NIST/EPA/NIH). In contrast, ionization and fragmentation patterns of LC-MS are much more instrument-specific, requiring additional steps for compound identification. First, the exact mass of a compound as determined by HRMS can provide one or several possible molecular formulae. As the structure of the precursor PSM is generally known, a putative structure of a metabolite may sometimes be inferred from this formula alone. In addition, fragmentation of the unknown compound by tandem mass spectrometry (MS/MS or MS2) can provide further confirmation on a putative structure. Comparison of exact masses and molecular fragmentation patterns to public databases can sometimes result in successful identification   , METLIN,s (KEGG) , althougs (KEGG) , which ie.g., 13C, 15N, 2H (deuterium), or 17O) or unstable radioisotopes . As these labeled PSMs are then metabolized by insects, identification of conversion products is facilitated by the presence of the labeled atoms. In the field of plant herbivore interactions, several studies with labeled PSMs have been done, for example with glucosinolates, alkaloids, monoterpenoids, and cyanogenic glucosides , followed by purification of the PSMs of interest. Finally, labeled PSMs can even be produced from detached leaves or root cell cultures by feeding them labeled precursor molecules prior to stimulation of PSM production , followed again by purification of the labeled PSMs ), as not all resulting metabolites/fragments of a partially labeled molecule will carry the label  with its ion intensity. Next, the angle between two vectors is calculated, resulting in a cosine-score to specify and express the similarity between two MS2 spectra. Subsequently, the matrix of spectral similarities is visually organized as a molecular networking graph with each node representing a MS2 spectrum, and edges between the nodes displaying spectral similarity above the user-defined similarity score threshold. In the last step, spectral library annotations can be propagated throughout the generated molecular networks, thereby identifying molecular families  and facilitating the process of structural elucidation for unknown chemical compounds. Therefore, molecular networking allows for comprehensive metabolomic characterization to provide unprecedent insight into complex insect-plant interactions, such as detecting pathway- and PSM-specific natural variation in different native plant populations , UDP-glucosyltransferases (UGTs), and ATP-binding cassette (ABC) transporters, belong to multigene families with more than 100 members in some insects  or long read sequencing technologies . The number of sequenced reads depends, in addition to the availability of resources, on the sequencing method, the expected transcript abundance, and the complexity of the starting sample . Sequenced reads are then mapped to the predicted gene set using available tools. RNA-Seq studies usually reveal hundreds of differentially regulated genes even within a single tissue, showing that insect molecular responses to PSMs are complex . From this comprehensive list of genes, candidates can be filtered by gene expression analyses, comparing for instance overall levels of gene expression, expression in different insect tissues, or in different developmental stages. Alternatively, candidate genes are selected based on their inducibility in response to ingestion of specific PSMs compared to a control treatment without PSMs. However, genes involved in metabolization and sequestration could also be expressed constitutively, especially in specialist herbivores, which are continuously exposed to specific PSMs. The most comprehensive method to analyze gene expression is by RNA sequencing (RNA-Seq). RNA is extracted from dissected tissues or whole insects with several replicates (≥3) per treatment or tissue. Comparable amounts of RNA from each sample are used to prepare a sequencing library and sequenced using short read  but differ with respect to the trait under study. After gene annotation, phylogenetic analyses are performed to analyze the diversification pattern using nucleotide or amino acid sequence alignments. The resulting phylogenetic trees will reveal genes that are conserved and those that have duplicated in a lineage-specific manner, which might be linked to the emergence of an evolutionarily novel function in PSM detoxification and sequestration . Two main approaches can be undertaken to identify proteins that interact with PSMs.Proteomic analyses can be a powerful tool to discover enzymes that are involved in PSM metabolization. One particular advantage of proteomic analyses is the fact that they allow to elucidate new pathways that may not be highlighted during phylogenetic and/or transcriptomic studies  using for instance fast liquid protein chromatography (FPLC). The obtained protein fractions are then screened for activity by carrying out enzyme assays (SeeActivity-guided protein fractionation coupled to mass spectrometry aims to purify and identify the protein(s) responsible for PSM metabolization in a complex mixture . In briessays See. Active ssays See.e.g., PSM) and proteins in vivo. The proteins identified as interacting with the tested ligand should further be examined to disentangle whether the interaction is due to targeting of proteins, metabolization and/or sequestration machinery, and untargeted effects  can be used for the identification of direct physical and undirect functional correlations between proteins and to score Protein-Protein Interactions . The STRGene co-expression networks identify groups of genes with similar expression patterns across tissues and treatments, with the underlying assumption that genes involved in the same biological processes, such as insect response to a particular host plant, will be co-regulated under the same environmental or developmental conditions . In co-eSpodoptera frugiperda, the fall-armyworm, inclusion of both the rice-feeding strain and the maize-feeding strain would likely increase variation in detoxification gene expression because maize produces benzoxazinoids while rice does not  or the Integration of co-expression networks with multi-omics datasets allows for a more complete model of a biological process and helps identify the top candidate genes for functional characterization. The simplest strategy for integration of metabolomics or microbiome data is to map these data onto an existing co-expression network generated using one of the previously discussed strategies. For example, if a PSM modification is identified in an untargeted metabolomics experiment, the correlation between the abundance of the modified metabolite and gene expression could be mapped onto the network to highlight modules and genes whose expression correlated with this modification across tissues and treatments. Other emerging tools use more sophisticated algorithms to integrate multi-omics data into a single network . For exaEscherichia coli), yeast , and insect cells  derived from Drosophila melanogaster embryos). Xenopus laevis oocytes are used for expression and biochemical characterization of membrane transporters and other membrane proteins.To date, there is a strong discrepancy regarding the number of studies investigating the effects of PSMs on gene expression and those validating the function of individual genes using heterologously expressed proteins . The genE. coli expression system has many advantages and is usually preferred if the protein does not require posttranslational modifications , cofactors required for enzyme activity, and an appropriate buffer. Background controls include assays with boiled protein, without substrate, or without protein. Assay products may be analyzed by chemical analytical methods or other methods developed for specific enzyme classes  is synthesized and injected into oocytes using a microinjection set-up. As negative control, oocytes are injected for example with water. A detailed protocol for these steps is provided in  into the buffer to detect import activity, or by injecting the substrate into the oocyte to detect export activity  can help to design dsRNAs for specific gene knock-down (RNA interference (RNAi), a conserved post transcriptional gene silencing mechanism, represents the most commonly used method for studying the function of genes in insects. In RNAi experiments, the nucleotide sequence of the candidate gene is used as a template to synthesize double stranded RNA (dsRNA), which is injected into the insect body or administered orally, for example via an artificial diet or using plants expressing dsRNAs. The exogenous dsRNA is taken up into cells primarily by endocytosis and processed into short (19–21 base pairs) interfering RNAs (siRNAs) that trigger the siRNA pathway, resulting in the degradation of the target messenger RNA (mRNA) through sequence complementarity . As negaock-down .The impact of the dsRNA treatment on gene expression is evaluated by quantitative reverse transcription PCR (qRT-PCR) experiments, comparing the transcript abundance of the candidate gene normalized to a reference gene between the different treatments. To determine the specificity of RNAi, the expression level of genes with high sequence similarity to the target gene should be analyzed as well. Important considerations for the design of qRT-PCR studies are summarized in the MIQE guidelines  . Finallye.g., regarding the amount and length of the dsRNA, the dsRNA delivery mechanism, and time point of application  that interacts with the Cas9 nuclease. The Cas9-gRNA complex is also referred to as ribonucleoprotein (RNP). DNA recognition and cleavage by Cas9 requires the presence of the protospacer adjacent motif (PAM) sequence of NGG nucleotides directly downstream of the target sequence. Several web-based tools are available for crRNA design and off-target prediction , 3) crosses of genome-edited adults and screening for transmission of target mutations to G1, 4) set-up of mutant lines. The timing and method of egg injection are critical parameters that determine the efficiency of genome editing. Recent publications by Blattella germanica, and over 50% in the red flour beetle, Tribolium castaneum (The basic workflow to obtain genome-edited insect lines comprises 1) microinjection of the RNP into freshly laid eggs, 2) screening of adults for target mutations  imaging.The distribution of PSMs and metabolization products within an herbivore body can be crucial to identifying the molecular and biochemical mechanisms of detoxification and sequestration, but also to tackling their possible effects on the insect biology. Localizing PSMs can be achieved through the metabolomic analysis of individual tissues, mass spectrometry imaging, immunohistochemistry, in vitro. Spheroids and organoids have been developed for drug discovery and personalized medicine to encapsulate the complexity and heterogeneity of responses to drugs in complex genetic and molecular environments . Given the limited knowledge available of insect organogenesis mechanisms, preparing insect-derived organoids may be still challenging. Yet, developing spheroids and/or organoids from insect cells would be a corner stone in advancing the fields of plant-herbivore interactions and pest management  into a heat map image displaying the relative distribution of the selected m/z values for the analyzed region of interest   MSI represents one of the most advanced techniques for bioanalytical research. In the first step, the biological sample, usually a histological cross-section of organs or whole-body section, is carefully embedded in a matrix of UV-absorbing low molecular weight molecules that extracts and crystalizes the analytes of interest. In this context, the MALDI matrix has to be carefully selected depending on the analytes of interest . Next, tinterest . Recent tissues) .D. plexippus) and the common crow (Euploea core) at the low-micrometer scale  laser pulses to ablate material from the sample surface. Next, an electrospray mist is pneumatically directed towards the ablation cloud causing ionization of the analytes of interest, which are then transmitted into the mass spectrometer. Both methods have been applied in pharmaceutical and natural products research, especially when mapping small lipophilic molecules, including plant metabolites in roots and alkaloids in whole body sections of alkaloid-sequestering poison frogs .Alternatively, Desorption Electrospray ionization (DESI) and Laser Ablation Electro Spray ionization (LAESI) are ambient ionization techniques for MSI, which require minimal to no sample preparation at all. DESI utilizes ionization principles of electrospray (ESI) by pneumatically directing an electrically charged mist to the sample surface. Subsequently, analytes of interest are desorbed via splashed droplets, ionized and transported into the mass spectrometer for determining their on frogs . Howeveri.e., hapten-carrier conjugate). The production of a hapten-carrier conjugate might not be completely specific, because it can elicit non-specific immune response due to the protein carrier, but production of antibodies targeting PSM has been done and it could be an interesting method to have available  of RNA provides information on the spatial distribution of gene transcripts in their native cellular environment. The most commonly used RNA probes are indirect immunochemical methods, where the probe is labeled with a reporter molecule that will be bound to antibodies that, in turn, are conjugated to alkaline phosphatase, peroxidase, fluorescein, rhodamine, or colloidal gold. Commercially pre-labeled probes allow investigating the distribution of multiple transcripts by using different fluorophores that emit specific detectable fluorescent signals (Plutella xylostella)  .FISH has also been used to detect and investigate the functions and distribution of symbiotic bacteria in insects , it became possible to use PET in research with small animals and plants. The main advantage is that PET imaging is non-invasive and does not require animal sacrifice (Positron Emission Tomography (PET) is a promising avenue for the study of PSM distribution in herbivores. PET measures acrifice . For exaThis review emphasizes cutting-edge techniques, from the field and beyond, that empower researchers to study herbivore resistance strategies to PSMs. These cutting-edge methods enable scientists to characterize the processes that underlie PSM metabolization, diversion (incl. sequestration), or rapid elimination of metabolized products. The unprecedented technological progresses allow researchers to increase the resolution of our understanding of the processes from whole organisms to single cells. While tolerance strategies were not under the scope of this work, several techniques described presently can be used to assess insensitivity, exclusion, and direct excretion. Further microbiological studies can be conducted to assess the contribution of associated organisms (microbes from the digestive system) to PSM resistance in insects . Finally


Effective training and retraining may be key to good quality paediatric cardiopulmonary resuscitation (pCPR). PCPR skills decay within months after training, making the current retraining intervals ineffective. Establishing an effective retraining strategy is fundamental to improve quality of performance and potentially enhance patient outcomes.To investigate the intervals and strategies of formal paediatric resuscitation retraining provided to healthcare professionals, and the associated outcomes including patient outcomes, quality of performance, retention of knowledge and skills and rescuer’s confidence.This review was drafted and reported using the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews (PRISMA-ScR). PubMed, Medline, Cochrane, Embase, CINAHL Complete, ERIC and Web of Science were searched and studies addressing the PICOST question were selected.The results indicate complex data due to significant heterogeneity among study findings in relation to study design, retraining strategies, outcome measures and length of intervention. Out of 4706 studies identified, 21 were included with most of them opting for monthly or more frequent retraining sessions. The length of intervention ranged from 2-minutes up to 3.5 hours, with most studies selecting shorter durations (<1h). All studies pointed to the importance of regular retraining sessions for acquisition and retention of pCPR skills.Brief and frequent pCPR retraining may result in more successful skill retention and consequent higher-quality performance. There is no strong evidence regarding the ideal retraining schedule however, with as little as two minutes of refresher training every month, there is the potential to increase pCPR performance and retain the skills for longer. Survival from paediatric cardiac arrest is dependent on medical interventions including high quality paediatric cardiopulmonary resuscitation (pCPR).6Effective training and retraining may be key to pCPR quality. Previous studies demonstrated that learners acquire CPR knowledge and skills irrespective of the method it is deliveredThis scoping review aimed to provide a contemporary synthesis of the literature exploring intervals and strategies of formal paediatric resuscitation training/retraining provided to healthcare professionals, and the associated outcomes including patient-level outcomes, quality of pCPR performance, retention of knowledge and skills, and rescuer’s confidence. Since interventions such as “low dose, high frequency”In order to achieve the above stated aim, a scoping review was the preferred method. It enables to determine the scope of evidence available, provide an overview on key aspects underpinning the research area and gaps in literature to be identified.This scoping review protocol was drafted and reported using the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews (PRISMA-ScR).The research question was based on PICOST  and defined as: “What can be learned from the literature regarding strategies and intervals of paediatric CPR retraining provided to healthcare professionals in relation to patient outcomes; good quality performance; better retention of knowledge and/or skills; and/or improved rescuer’s confidence?”P - healthcare professionals including doctors, nurses, EMS providers, Allied Health Professionals or any other healthcare professional working in any geographic location and any setting  undergoing formal pCPR retraining.I - any form of formal pCPR retraining.C - different retraining or refresher intervals.O – patient outcome; ability to deliver effective pCPR – simulated or real; knowledge and skill improvement; retention of knowledge and skills; and rescuer’s confidence.S - Primary studies  including randomised controlled trials (RCTs), non-randomised controlled trials, interrupted time series, controlled before-and-after studies, observational and cohort studies were included in order to consider different aspects of measuring outcomes.T - studies published between January 2005 and March 2022 .All studies addressing the PICOST question were eligible, including paediatric manikin and/or simulation; paediatric basic life support (BLS) retraining; paediatric advanced life support (PALS); European paediatric advanced life support (EPALS); advanced paediatric life support (APLS); paediatric immediate life support (PILS); pre-hospital paediatric life support (PHPLS); and advanced trauma life support (ATLS). Studies based on neonatal CPR training/ retraining; adult CPR training/retraining; healthcare students; unpublished studies and studies in a language other than English were excluded from this review.The following databases were searched by three researchers : PubMed/Medline; Cochrane; Excerpta Medica Database (Embase); Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete and Web of Science. A pre-defined search strategy was used combining Boolean operators ‘AND’ and ‘OR’ with medical search headings and subheadings (e.g. MESH) when applicable. The search terms  were draAll articles initially identified were sent to the web-based bibliographic manager (EndNote Desktop X9) where duplicate references were removed. To increase consistency, two reviewers (DA and LT) screened the identified sources for relevance by evaluating the titles and abstracts according to the proposed eligibility criteria. Disagreement was resolved by consensus, moderated by a third reviewer (TP). If during abstract screening suitability could not be determined, further evaluation of the full text was performed, at which point, those studies that did not fit the eligibility criteria were excluded.Data from included full text sources were extracted and organised in an Excel spreadsheet using a “descriptive-analytical” method within a pre-set frameworkThe initial search resulted in 6272 studies. Of those, 1566 were duplicates, resulting in 4706 titles post-deduplication. After reading titles and abstracts, 134 were selected for full text review. Of those, one could not be retrieved and 112 were discarded for not fully fulfilling the inclusion criteria, leaving 21 studies included in the analysis, as seen in Study characteristics and interventions are summarised in The geographical areas consisted of Thailand,48Different training strategies were observed, with the majority of studies using PALS or BLS as initial training models. Most of the interventions (14) were team-based trainingOutcome measures have also differed between studies and comprised of patient outcomes;Methods to assess outcome measures included hospital record for cardiac arrest survival rates;The studies selected for this review used different timeframes for the first reinforcement session after initial pCPR training. Two studies performed the interventions straight after training47The number of refresher interventions throughout the study periods also varied considerably, with most studies doing monthly refresher sessions.50The length of intervention in each retraining session varied considerably, ranging from 2-minutes up to 3.5 hours. For easier identification and analysis, the studies were grouped as short duration (<1h)Studies investigating interventions using short durations per session were the majority (17 studies). The length of intervention ranged between two to 40 minutes and the outcome measures included patient outcome;Patient outcome was analysed in one studyFour studies used longer lengths of interventions when retraining participants, with two of them lasting 1 h,This research has broadly and systematically identified and analysed studies relevant to retraining schedules of paediatric resuscitation skills for healthcare professionals. The International Liaison Committee on Resuscitation (ILCOR) has stated that regular pCPR skills updates are important however, the ideal retraining interval has yet to be established as evidence is limited in both quantity and quality.In this review exploring retraining schedules of paediatric resuscitation skills for healthcare professionals, although the inclusion and exclusion criteria were well defined, the findings reveal complex data with studies that do not fit precisely into the categories. Despite internal quality assurance and transparency in reporting, identifying the time schedule and length of interventions was not simple, due to differing study methods and interventions. Additionally, the lack of clarity in some studies regarding the length of retraining interventions, made it more difficult to analyse the results.The included studies demonstrated that the initial acquisition of pCPR skills is similar, irrespectively of the training model used. Different strategies were observed, including the traditional instructor-based training;The best training and retraining designs should be tailored to specific learning objectives, learner type and needs, or context of learning. There are recommendations related to the use of deliberate practice, mastery learning, booster training, in situ education, real-time feedback and other strategies for training and retraining.In this review, all studies pointed to the importance of regular retraining sessions to the retention of pCPR skills. Although there is no consensus on the optimal interval, most of the studies opted for refresher sessions on a monthly basisAlthough regular updates are beneficial to retention of skills, frequent retraining sessions can be associated with high dropout rates.The use of feedback devices during retraining was observed in many studies.This review has demonstrated that brief and frequent pCPR retraining using simulation and additional tools such as real-time feedback devices can potentially develop skills, knowledge and confidence in pCPR performance. It is also noted that increasing the frequency of retraining sessions may result in a more successful skill retention and consequent higher-quality performance. Despite this, there is no strong evidence regarding the ideal retraining schedule. It is suggested that a more nuanced approach to pCPR retraining, based on specific learning objectives, context and learners’ needs and/or performance is recommended in an attempt to maximise skill retention and improve pCPR performance.This study has some limitations. First, potential biases were not systematically addressed like in a systematic review. Second, the heterogeneity among study design, retraining strategies, outcome measures and length of intervention, may impact the interpretation and synthesis of the results. Third, most studies were performed in a simulated, controlled environment, making it difficult to extrapolate the results to real-life CPR performance. Fourth, despite improvement in pCPR quality and retention of the skills, the results cannot be directly associated with patient-level outcomes. Fifth, this review only includes studies after 2005, therefore, it is not known if other important evidence exists prior to 2005. Finally, despite exhaustive attempts to locate every relevant resource, one study was identified but could not be retrieved.Brief and frequent pCPR retraining may result in a more appropriate skill retention and consequent high-quality performance. There is no strong evidence regarding the ideal retraining schedule however, it was demonstrated that, with as little as two minutes of refresher training every month, there is the potential to increase pCPR performance and retain the skills for longer.The authors declared that this research has not received any grant from any funding agency in the public, commercial or not-for-profit sectors.The authors declare that there are no conflicts of interest.


Cell therapy has transformed oncology and drug development, yet better model systems are needed to recapitulate the tumor immune microenvironment (TIME). Microphysiological systems (MPS) can comprehensively model the human TIME, including immune cells, endothelial cells, fibroblasts, matrix, and cytokines. This review discusses current barriers to developing cell therapies for solid tumors from the perspective of MPS model design approaches. Overcoming current limitations in model systems and advancing MPS engineering will facilitate oncology drug development.Cell therapies, including adoptive immune cell therapies and genetically engineered chimeric antigen receptor (CAR) T or NK cells, have shown promise in treating hematologic malignancies. Yet, immune cell infiltration and expansion has proven challenging in solid tumors due to immune cell exclusion and exhaustion and the presence of vascular barriers. Testing next-generation immune therapies remains challenging in animals, motivating sophisticated ex vivo models of human tumor biology and prognostic assays to predict treatment response in real-time while comprehensively recapitulating the human tumor immune microenvironment (TIME). This review examines current strategies for testing cell-based cancer immunotherapies using ex vivo microphysiological systems and microfluidic technologies. Insights into the multicellular interactions of the TIME will identify novel therapeutic strategies to help patients whose tumors are refractory or resistant to current immunotherapies. Altogether, these microphysiological systems (MPS) have the capability to predict therapeutic vulnerabilities and biological barriers while studying immune cell infiltration and killing in a more physiologically relevant context, thereby providing important insights into fundamental biologic mechanisms to expand our understanding of and treatments for currently incurable malignancies. Genetically modified immune cells, such as autologous or allogeneic CAR T and NK cells, represent a paradigm shift in oncology and cancer therapeutics . CAR T cAs previously reviewed , resultsThere is hope that refined CAR engineering can improve transduced T and NK cell homing and efficacy , and manOne challenge in developing cell therapies remains the lack of suitable model systems ,14. In vAdvanced preclinical models such as MPS have the potential to transform the drug development pipeline by predicting the outcome of clinical trials better than current animal models and traditional two-dimensional (2D) culture . PreclinMPS platforms contain living cells that have been organized into compartmentalized single or multiple channels. The main goal of MPS modeling is to reproduce minimal functional units that recapitulate tissue- and organ-level functions in a more physiologically relevant model. Fundamental aspects of the technology are: (i) the selection of appropriate cell types, able to undergo self-organization into organ- or tissue-appropriate complex architectures and (ii) the design of appropriate extracellular matrix (ECM) composition that enables cell polarization and development through complex morphogenic steps, converging into precise anatomical structures. MPS use different approaches to generate 3D multicellular architecture in vitro. Broadly speaking, these methods can be categorized as spherical organoid and droplet models, bioprinting, and microfluidic systems . Hence, Cell sources for MPS include fully differentiated primary cells and cell lines or stem cells, including both embryonic stem cells (ESC) and induced pluripotent stem cells (iPSC). ESC and iPSC have the capacity for spontaneous self-organization, reproducing pluripotent stem cell arrangement during embryogenesis to form in vitro “organoids” ,25,26,27Either cell line spheroids or organotypic spheroids are suspended in hydrogel and injected and subsequently grown in one compartment of a microfluidic device. In yet another approach, cancer cells can be suspended within fluid drops to allow the mixing of multiple cell types in small volumes of fluid instead of encapsulation in hydrogel. For example, both lymphocytes and tumor spheroids can be combined inside hanging drops, such as a study which used this technique to assess the cytotoxicity of CAR T cells recognizing the human epidermal growth factor receptor 2 (HER2) in breast cancer ,39. Oil Bioprinting can also be used to generate 3D cancer models, with more controllable architecture than organoid models, but generally lacking the dense stromal structure and heterogeneity of a patient tumor . Many biMicrofluidic devices for MPS containing adjacent chambers and channels for cell/hydrogel or fluidic compartments can be fabricated from a variety of materials, but often rely on high-precision soft lithographic methods with polydimethylsiloxane (PDMS), a transparent, inert, and biocompatible polymeric substrate. Soft lithography or other methods can be used to create features at the micron scale in configurations that typically enable live microscopy, addition and sampling of fluids, and controllable gradients across devices ,50. MicrThe wide variety of approaches possible for generating MPS allows researchers to use simple systems to facilitate 3D in vitro oncology studies or to design custom platforms that enable unique tissue-on-a-chip configurations or new quantification methods and opportunities for novel hypothesis testing. MPS studies in oncology and cell therapy have been accelerating, likely due to several advantages they possess over traditional 2D cell culture and in vivo studies using small animal models.MPS offer several advantages, especially for cell therapy studies in oncology. These multicellular, 3D culture platforms can be designed to resemble the complex architecture of living tissue more closely, and multiple cell types can be combined in culture systems accessible to high-resolution imaging, fluid sampling, and other quantification techniques for monitoring interactions between cells. Furthermore, many of these models also incorporate extracellular matrix, which has been observed to influence cell phenotype, immune cell migration, and cytotoxicity efficiency. Finally, these models can make use of cells from any organism, making it possible to generate fully human, 3D, in vitro systems closely mimicking tissue architecture and overcoming the disadvantages of small animal models for immune-oncology studies . Though it is common in MPS oncology studies to use a combination of cancer cells and immune cells in 3D, they vary widely in how the cells are organized or introduced. The simplest models are tumor spheroids, either from established cell lines or patient-derived sources, with a suspension of immune cells added. These models typically place tumor spheroids in microwells to image the infiltration and cytotoxicity of lymphocytes ,58,59,60Cytotoxicity efficiency can be reduced by nutrient deficiencies and metabolic changes that occur in larger 3D systems. For example, Ayuso et al. used a rectangular microfluidic system that was supplied with cell culture media from only one side to form a gradient across the width of the device . They fo2. Another study tracked the speed of interactions between T cells under flow and immobilized beads coated with MHC to infer the affinity of the receptor-ligand interactions [Microfluidic systems are also ideal for mimicking flow conditions in and around the tumor, serving several useful purposes including the delivery of immune cells that are suspended in the flowing medium and allowed to contact and adhere to either the gel surface or other cells in order to understand lymphocyte adhesion under shear stress. Rosa et al. cultured a monolayer of dendritic cells and designed a microfluidic device to direct streams of CD4+ and CD8+ cells across it at defined shear stress levels . T cellsractions . A differactions . They foMicrophysiological platforms that support the culture of multiple cell types can, therefore, also be used to investigate the roles of additional cells making up the stroma of the tumor microenvironment, such as ECs (vascular or lymphatic), fibroblasts, and other immune cell types such as antigen-presenting cells and myeloid-derived suppressor cells. CAFs can exert immunosuppressive functions in multiple ways, primarily through remodeling of the extracellular matrix and secretion of paracrine factors that inhibit lymphocyte function. CAF are often combined directly with tumor spheroids or embedded in the gel surrounding them ,75,76,77In these tissue models where cells are embedded in hydrogels, not only are lymphocytes required to navigate through the gel to reach their targets, but they are also subjected to the mechanical properties of the substrate, such as density and stiffness. Lymphocytes exert their killing functions through direct contact with target cells, and as such are exquisitely sensitive to mechanical stimuli and use these signals to guide their migration and activation ,73. WhilECs also play an important role in the lymphocyte infiltration of solid tumors, since NK and T cells traveling through circulation must bind to the endothelium and extravasate before migrating toward tumors. While some models incorporate an endothelial monolayer to enforce the binding/extravasation step of lymphocyte homing to tumors ,73,74, oTumors are complex ‘ecosystems’ comprised of many different cell types and noncellular features, and it has been demonstrated that the complexity and diversity of the TIME influences patient response to immunotherapy. The tumor stroma, including ECs, pericytes, CAFs, etc., has a critical role in tumor initiation, progression, metastasis, and therapy resistance ,81. In mModeling vascularization is key to cell therapy testing for a variety of reasons. First, vascularized models incorporate the angiocrine functions of ECs which interact with cancer cells to shape the TIME . Second,In vitro models of blood vessels are typically generated by two distinct bioengineering approaches: via endothelial-lined patterned channels or by self-assembled networks ,92,94,99The patterned microchannel method consists of the 3D culture of cells on the walls of micropatterned chips or gel surfaces. Using this method, the channel design determines the size and architecture of the newly formed vessels. To develop hollow-lumen 3D macrovessels, ECs adhere to microfluidic channel walls or cylindrical casts, precoated with cell adhesive materials, such as fibronectin, Matrigel, or collagen ,93,94,95Alternatively, microvascular networks can be created by leveraging a vasculogenesis-like process that occurs when ECs are embedded in hydrogel in suitable conditions ,102,103.To provide effective function, it is important that the vascular networks be perfusable, as can be confirmed by introducing fluorescent tracers such as dextran or beads . Self-asThe key to success in developing any advanced preclinical model is to focus on mimicking organ-level physiology or pathophysiology observed in vivo. Validation depends on the demonstration that the model can effectively recapitulate behaviors observed in vivo ,106. OveMPS using microfluidic technology have some of the characteristics of other advanced models (3D bioprinting and organoids) , such asMany extracted immune cells have short life spans ex vivo, for example as little as 1 day for neutrophils, and on the order of a few weeks for lymphocytes. Since MPS require few cells, microfluidic culture can conserve immune cells, as seen in applications with patient-derived organotypic tumor spheroids (PDOTS) and murine MDOTS . These pMPS also offer the possibility of improving personalized medicine by more faithfully recapitulating patient-specific, organ-level pathophysiology and responses to therapies . Indeed,MPS systems offer the alternative of generating fully human 3D systems that can be used to culture either established cell lines or patient-derived tissues. Patient-derived organotypic tumor spheroids (PDOTS) have been embedded in microfluidic devices to enable research groups to track immune cell migration towards spheroids, monitor cell death with live/dead stains, and to perform immunostaining to identify different cellular populations within the samples, which can come from biopsies or surgical resections ,35,36,37One of the obstacles to immune cell homing is the vasculature barrier facing CAR T or NK cells injected intravenously. Only a small portion of these cells can extravasate through the vasculature barrier and enter the TIME to kill tumor cells. Hence, strategies to improve the extravasation and migration of CAR T cells are critical for improving cell therapy efficacy. We previously showed that the stimulator of interferon genes (STING) plays a crucial role in antitumor immune responses and restoring or activating cGAS-STING signaling in both tumor cells and ECs promotes immune cell recruitment via the CXCR3-CXCL10 receptor-chemokine axis and vascular activation . As discDespite innovations in terms of complexity, in vitro devices are still unable to fully recapitulate biological in vivo interactions within an organism. With recent advances in fluidically coupled multiple microfluidic systems to connect microphysiological systems and recreate a human body-on-chip, we can begin to consider the possibility of creating multi-MPS models to mimic the systemic interactions between organs . One oppOne of the disadvantages of microfluidic platforms is that they are difficult to maintain for long-term culture (months), and experimental protocols involving microfluidic devices require experienced operators. Microfluidic devices, compared to multiwell plates, could be considered as low-throughput screening tools . One possibility will be to design more complex platforms containing an increased number of devices. Additionally, automation will help increase throughput . A key iInclusion of additional cells, such as lung epithelial cells for the lung models and lymphatic vessels and fibroblasts to generate lymph nodes, will create a more faithful recapitulation of the TIME ,128. HowMPS have shown promising results and value, but the field is in its infancy in terms of preclinical application and standard inclusion in drug discovery pipelines. MPS are not yet robust enough to support routine and widespread adoption, but could eventually be incorporated into existing screens, traditional workflows, and platforms. In support of their adoption, MPS were used to test >100 compounds for which clinical data were available . It willIn contrast to hematologic malignancies, the major limitation to cell therapies for solid tumors is the necessity for lymphocytes to infiltrate tumor tissue and remain active in the presence of immunosuppressive signals supplied by the TIME. The barriers to effective lymphocyte homing are imposed by the tumor architecture, including vascular barriers to extravasation, cytokine gradients to guide migration, and mechanical barriers imposed by the extracellular matrix. Once lymphocytes reach the tumor tissue, they often encounter a highly immunosuppressive environment generated by the tumor and stroma, such as metabolic conditions, paracrine signaling, and checkpoint inhibition. Together, this complex TIME results in the exclusion or exhaustion of cell therapies without a clear indication of which feature is the major limitation. Therefore, MPS offer an ideal approach to identify the mechanisms underlying these barriers, allowing researchers to design novel strategies for overcoming them. The many advantages of 3D, multicellular, human MPS allow them to replicate cancer pathophysiology more fully than traditional cell culture methods. With regard to long-term impact, refining and expanding the repertoire of MPS models in oncology will enable improvements in the testing of emerging cell therapies and facilitate the design of new drugs to overcome the barriers of the TIME. Furthermore, patient derived MPS enable stratification and identification of individual responses to therapy. These numerous advantages will result in an increasing number of approaches to MPS tailored for cell therapy studies in the future, leading to continued improvements in patient outcomes.


This necessitatesindividual researchers to develop their own analytical pipelines,resulting in a lack of standardization across the field. To facilitateprotease research, here we present Protease Activity Analysis (PAA),a toolkit for the preprocessing, visualization, machine learning analysis,and querying of protease activity data sets. PAA leverages a Python-basedobject-oriented implementation that provides a modular framework forstreamlined analysis across three major components. First, PAA providesa facile framework to query data sets of synthetic peptide substratesand their cleavage susceptibilities across a diverse set of proteases.To complement the database functionality, PAA also includes toolsfor the automated analysis and visualization of user-input enzyme–substrateactivity measurements generated through in vitro screensagainst synthetic peptide substrates. Finally, PAA supports a setof modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-basedsensors. Overall, PAA offers the protease community a breadth of computationaltools to streamline research, taking a step toward standardizing dataanalysis across the field and in chemical biology and biochemistryat large.Analyzing the activityof proteases and their substrates is criticalto defining the biological functions of these enzymes and to designingnew diagnostics and therapeutics that target protease dysregulationin disease. While a wide range of databases and algorithms have beencreated to better predict protease cleavage sites, there is a dearthof computational tools to automate analysis of  To this end, molecular tools suchas activity-based probes (ABPs), short synthetic peptide substrates,and noninvasive enzyme activity sensors have been developed to measureprotease activities in vitro, i.e., of recombinantproteases or enzymes present in biospecimens,6 as well as in vivo, i.e., withinthe disease microenvironment.11 Beyond their use as a discovery tool, sensors that quantify proteaseactivity are being applied directly for early detection and monitoringof disease,918 biological imaging,18 and drug discovery.20 Furthermore, proteolytic cleavage of peptide linkers is being usedto trigger disease-specific activation of engineered activity-baseddiagnostics11 and therapeutics,24 all of which inherently rely on assessments of protease activityfor their design and optimization. To support the development of thesenew activity-based tools and to advance the study of protease biologyat large, a wide range of databases and algorithms have been createdto better identify protease substrates and cleavage sites, providinga clear demonstration of how protease research can benefit from computationaltools.27Proteases play essentialroles in diverse biological processesranging from development to differentiation, and dysregulated proteaseactivity is a driver of a variety of pathological conditions includingcancer, neurodegeneration, and infectious diseases.30 despite the fact that synthetic activity-based sensorsand large-scale libraries of synthetic peptides are now standard toolsfor measuring and quantifying protease activity in vivo and in vitro. Furthermore, the development of theseexperimental and molecular methods has not been accompanied by scalable,modular data analytic workflows. The creation of computational packagessimilar to what exists for genomics  could enablethe standardized analysis of data generated from both in vitro and in vivo protease-based experiments. This wouldgreatly benefit researchers by accelerating experimental workflows,informing diagnostic and therapeutic design, and facilitating biologicalinsight into protease dysregulation in disease.Rapidly identifying, designing, and characterizingnew peptidesubstrates and activity-based sensors remains a major bottleneck towardadvancing these applications. This is due to the promiscuous natureof protease activity, the combinatorial number of synthetically accessiblesubstrates, and the dearth of methods to automate protease activityanalysis and substrate design. Current protease databases and analytictools also focus exclusively on endogenous substrates and cleavagesites,in vitro proteaseactivity screens and in vivo protease-activated nanosensors.The package is accompanied by step-by-step tutorials that detail thefunctionalities provided by PAA in an open-source repository . PAA’s Python-based implementation provides a modular frameworkthat is easy to interface with other software packages and can bereadily integrated into broader data analytic workflows.To address theseneeds, we present Protease Activity Analysis (PAA),a toolkit that addresses the need for computational methods to acceleratedata analysis of enzyme activity data in biochemistry, chemical biology,and bioengineering 1. PAA coin vivo nanosensors.Across all three workflows, PAA provides preprocessing, visualization,machine learning, and search functionalities.PAA provides scalable and modular analysis capabilities for datasets of enzyme activity measurements. Specifically, PAA supports threecore analytic workflows 1: (1) anSubstrateDatabase data structurethat provides a facile framework for curating and querying data setsof enzyme–substrate activity, which often take the form offluorometric assays of proteolytic cleavage of synthetic, fluorogenicpeptide substrates. These assays measure the kinetics of enzyme activityover time and can be used to assess both the efficiency of an enzymefor a particular substrate, by quantifying the initial rate of thereaction, as well as the specificity of a substrate for a protease,by comparing the substrate’s cleavage against other proteasesscreened.Identifying and characterizingwhich substrates are robustly andspecifically cleaved by proteases of interest, such as those thatare overexpressed in a specific cancer, is critical to discovery andengineering efforts that seek to understand and exploit protease activity.Indeed, the rapid rise and promise of engineered conditionally activateddiagnostics and therapeutics, which almost universally incorporatea protease-cleavable peptide linker as the “trigger”for disease-specific activation, has motivated the need for toolsand methods that identify synthetic peptide substrates that are maximallycleaved in diseased tissues and/or by target proteases. To this end,in PAA we present a 31 However, users canalso import their own data into PAA for individual use, instantiating SubstrateDatabase data structures that can be readily queriedand analyzed.To demonstrate these capabilities, we have createda publicly available database that incorporates data generated byour group from six independent recombinant protease screens againstfluorogenic peptide substrates. The database consists of 150 uniquesynthetic peptide substrates and their cleavage susceptibilities acrossa set of 77 distinct recombinant proteases spanning the metallo, serine,cysteine, and aspartic catalytic classes. The substrates publishedas part of PAA were identified based on the literature and designedto query the activity of disease-associated proteases, including thosein cancer, infection, and thrombosis. As such, there is an over-representationof metallo- and serine-sensitive substrates in the data set, whichis open-sourced as a part of PAA 2B.13,31 https://github.com/apsoleimany/protease_activity_analysis/tree/master/tutorials. This step-by-step guide showcases how to load and query the protease-substratedatabase that is published with this work. Briefly, to instantiatea SubstrateDatabase, the user inputs raw data matricesof activity measurements  for screens to be included in the database; a file that mapssubstrate names or labels to their corresponding sequences; as wellas an optional file that maps amino acids to different colors basedon properties of interest , lending strength to the power ofPAA to help optimize protease activity experiments using insilico methods. Similarly, given a substrate as the userquery, PAA can identify proteases that have been shown to robustlyand/or specifically cleave that substrate. Note that, for the publicdata set published as part of PAA, substrate names correspond to namesassigned by our group for specific sequences. These names map to existingnomenclature from previously published works for easy reference.31A guide to the core analytic and visualization functions relatedto the database can be found at dentity)2A. The SSubstrateDatabase can retrievethe top-k substrates most similar to the query sequence,as quantified by different sequence similarity metrics. PAA offerstwo different sequence similarity metrics: the Levenshtein distancesimilarity ratio and the partial ratio. The former is strictly basedon the Levenshtein distance that can be computed as the minimum numberof single-character edits required to change one amino acid sequence into another. The partialratio metric works similarly but instead takes the shortest sequenceand compares it with all substrings of the same length. This partialratio is particularly useful when two substrates contain the sameamino acid cleavage motif  but are flankedby different spacers at the N- or C-terminus , as they will still be assigned highsimilarity estimates. After calculating the similarity between theuser’s input sequence, PAA returns the k mostsimilar sequences and the values of the similarity metrics . For instance, the function will map thehuman protease “MMP9” to its mouseortholog “Mmp9” while alerting theuser that human “GZMH” does not havea mouse ortholog.35 Furthermore, whilePAA features data from our group published as a resource and examplefor the SubstrateDatabase data structure, users canuse PAA as a local package to analyze their own data sets, which willkeep all data private to the user. Then, users can implement PAA’sfunctionalities and modular methods to analyze their private or internallygenerated data sets, as well as other data sets of interest.All recombinant proteases included in the database areof humanorigin. However, because overlapping cleavage sites have been identifiedbetween protease orthologs,in vitro enzyme activity assays are vitalto characterizing protease activities and their dysregulation in disease.36 Despite the broad prevalenceof these assays throughout the protease biology and chemical biologycommunities, as well as the consideration that such assays will onlybecome larger in size as high-throughput screening becomes more common,data analysis remains, to the best of our knowledge, largely manual.There is a dearth of computational tools to automate the analysisand visualization of enzyme activity data sets. PAA introduces a wayto represent, store, and analyze these data sets automatically throughthe KineticDataset data object, which contains asuite of functions for rapidly preprocessing, visualizing, and analyzingthese data  generated directly by measurement instruments . Raw files consist of matrices of activity measurementsfor each sample to be analyzed .The class automatically generates key output activity measurements,including initial rates (intensity/min–1) and foldchanges at user-defined time points across substrates , demonstrating that PAA extendsto data sets from different experimental setups and research groups.A step-by-step tutorial of thecore data input, processing, visualization,and analytic functions can be found at trates13 3. We shoKineticDataset and the visualizationfunctionality provided by PAA streamline the aggregation, visualization, and analysis of in vitro activity measurements. In particular, these analysesfacilitate the assessment of cleavage efficiency and specificity aswell as the identification of differential and overlapping activitysignatures among different enzymes or tissue types.Together, 37 To this end, our group has developed activity-based nanosensors,probes that detect the activity of aberrant proteases within the bodyand generate exogenous urinary reporters that reflect the degree ofproteolytic cleavage encountered in vivo.42 These nanosensors consist of an inert scaffold whosesurface is decorated with peptide substrates, designed to be cleavedby proteases dysregulated in the disease state of interest. Each substrateis marked with a mass-encoded peptide barcode, which is released uponinteraction of the nanosensor with target proteases and then concentratedin the urine. Upon collection of urine, the relative concentrationsof each reporter are quantified using mass spectrometry. Multiplesensors can be multiplexed simultaneously by barcoding each uniquepeptide substrate with a different mass-encoded peptide reporter.31 This multiplexing generates n × k matrices of urinary reportermeasurements, where n is the number of samples and k is the number of sensors/reporters. The reporters serveas input features, and thusPAA automates data analysis of these inputmatrices and enables training of downstream machine learning classifiers.Because proteases play critical functionalroles in a variety of disease processes, recent years have seen theemergence of new classes of activity-based diagnostics that are engineeredto measure the activity of endogenous enzymes at the site of diseaseand to generate an output signal that can be read out externally.SyneosDataset for analysisand machine learning on these mass-barcoded reporter measurements.Our framework provides a variety of capabilities directly tied tobiological, diagnostic, and analytic questions of interest. Thesecapabilities include differential enrichment analysis of reportersbetween conditions ; unsupervised dimensionality reduction;binary and multiclass classification; feature, sample, and data specificationfor all analyses; recursive feature elimination; and associated visualizations.To demonstrate these capabilities, we created a step-by-step guide,published as an implementation tutorial, that details input data requirements,analytic functions, and visualization options. This guide recapitulatesthe findings of previously published work demonstrating the noninvasivedetection of localized lung cancer in mice using a 14-plex activity-basednanosensor panel.13 For the original study,the authors analyzed the in vivo data using uniquescripts created specifically for their analysis. In our demonstration,we show how the same raw data can be parsed, analyzed, and visualizedusing the modular functions available in PAA  and of urinaryreporter measurements from activity-based nanosensors (for the latter).PAAadvances computational methods to accelerate data analysisin biochemistry, chemical biology, and bioengineering. PAA representsa toolkit for users to automate the analysis of protease activitymeasurements generated Figure S2. Additional analytic functions for protease–substratescreening data, such as modeling time to cleavage saturation, predictionof competitive interactions between pairs of peptides,43 deconvolution of signals from mixtures of proteases,44 and identification of optimal substrate setsusing principles from information theory,32 will expand PAA’s abilities to automate optimal enzyme substrateselection and design. Future work could extend PAA’s machinelearning capabilities to include neural network models for classificationanalysis,45 methods for assessment of distributionshift and data set bias,48 as well as approaches for quantificationof predictive confidence.54However, the modular methods and concepts presented in PAA readilyextend to other data sets, particularly in terms of the tools foranalysis of substrate screening data, as shown in 44 Being implemented and released as a Python package, PAA can be furtherdeveloped and integrated into larger data analytic workflows. We envisionthat PAA will accelerate analysis workflows for biologists, biochemists,and engineers interested in understanding and leveraging proteaseactivity to better understand, detect, and treat disease.Not only does PAA contain valuable analysis tools, but italsoincludes a publicly available database of 150 unique synthetic peptidesand their cleavage susceptibilities across a set of 77 distinct recombinantproteases, together with an interface to query this database for proteases,substrates, or sequences of interest. PAA’s database can bereadily expanded through publication and addition of new proteasedata, for example through high-throughput screening efforts that expandits coverage to additional enzymes. Users can use PAA as a local packageto upload and analyze their own data sets, keeping all data privateand leveraging PAA’s functionalities to query and analyzetheir data. PAA's modular database functionality and public datasetcould be of great interest in the context of nomination of protease-cleavablepeptide linkers, for example for protease-activated diagnostics andtherapeutics. By focusing on synthetic substrates that directly measureprotease activity and providing modular data science functionalitiesthrough an accessible software package, PAA’s database andanalytic capabilities directly complement existing tools for assessingprotease cleavage patterns.NumPy, SciPy, Matplotlib, pandas, seaborn, and scikit-learn. The Python-based implementationallows for flexible use, easy interfacing to machine learning anddata analytic packages, and object-oriented programming. PAA'sopen-sourcecode is available at https://github.com/apsoleimany/protease_activity_analysis and is published under the MIT license. PAA is organized and builtas a package for ease of use and to facilitate developer integration.PAA’s core relies on in vitro screens of recombinantproteases and tissue lysates against synthetic peptide substrates;and (3) analysis and machine learning classification of urinary reportersignatures from in vivo activity-based nanosensors.The data sets used in these demonstrations were generated by our researchgroup and are published together with PAA.The demonstrations described in this work are stored as Jupyternotebooks available in the PAA repository. These include: (1) queryingand analysis of protease–substrate databases; (2) analysisand aggregation of All code was writtenin the Python programming language. The PAApackage is compatible with Mac OS, Windows, and Linux operating systems.


Dietary information was collected via food frequency questionnaires (FFQs) and used to calculate the empirical dietary inflammatory pattern (EDIP), Alternative Healthy Eating Index (AHEI), Western and Prudent dietary patterns, and the alternative Mediterranean Diet (aMED) score. Primary analyses examined associations with continuous autism-related traits as measured by the Social Responsiveness Scale (SRS), and secondary analyses with autism spectrum disorder (ASD) diagnosis. We used crude and multivariable quantile regression fixed at the 50th percentile to examine associations between quartiles of dietary patterns and SRS scores, and logistic regression to examine associations with ASD diagnosis. There was suggestion of a positive association with the Western diet  in EARLI, though the association was attenuated with adjustment for total energy intake, and no clear associations were observed with other dietary patterns and ASD diagnosis or SRS scores. Further work is needed to better understand the role of maternal dietary patterns in ASD and related outcomes.We examined the relationship between maternal intake of established dietary patterns and child autism-related outcomes in two prospective cohorts in the United States. Participants were drawn from the Early Autism Risk Longitudinal Investigation (EARLI,  Autism spectrum disorder (ASD) is a neurodevelopmental condition defined by social communication deficits and restricted repetitive behaviors, which present along a heterogeneous spectrum . ASD hasFocusing on individual foods and nutrients, however, may obscure the relation between diet and ASD. Dietary patterns have the advantage of capturing overall dietary behavior and interactions between individual foods and nutrients. Moreover, dietary patterns are often good predictors of key biological mechanisms implicated in ASD ,26,27, iIn order to address gaps in our understanding of the relationship between comprehensively considered maternal diet and ASD, we used data from two prospective cohorts, the Early Autism Risk Longitudinal Investigation (EARLI) and the Nurses’ Health Study II (NHSII). In addition to allowing for prospective investigation of maternal diet, these two populations were chosen in order to enable unique cross-cohort comparisons. EARLI is a relatively small, family-based multi-site US study that capitalized on the increased ASD recurrence rate in siblings of those with ASD in order to enable rigorous, detailed prospective data collections to learn more about ASD etiology. In contrast, the NHSII is a large, population-based US nationwide study that has prospectively followed registered nurses for decades to learn about a range of health outcomes. This multi-cohort comparative design thus allowed us to address novel comparisons in the study maternal diet and ASD, including potential differences due to background likelihood of ASD, which has been suggested to modify relationships with other risk factors for ASD .n = 8 pairs) in order to avoid repeat diet information for the same pregnancy. Following these exclusions, 154 participants from EARLI were included in the present analyses . The average follow-up rate exceeds 90%. Participants were eligible for this current study if they had index births between 1991 and 2007, in order to allow for prospective reporting of diet and to include participants from the nested case–control study of ASD that collected the outcome measure used here. The details of this nested case–control study have been previously described [The Nurses’ Health Study II (NHSII) is a large ongoing longitudinal study of 116,429 female registered nurses from the United States aged 25–42 years beginning in 1989 . Bienniaescribed . BrieflyMothers/caregivers in EARLI gave written informed consent to study procedures, which were reviewed and approved by local site IRBs. In NHSII, return of questionnaires constitutes implied consent, and the study protocol of the parent NHSII was approved by the Institutional Review Boards of the Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health.Dietary information in both studies was collected via comparable food frequency questionnaires (FFQs), both enabling calculation of dietary patterns. EARLI used a modified version of the National Cancer Institute’s Dietary History Questionnaire (DHQ). Modifications were made to assess diet during pregnancy and to capture additional information on toxicant exposures . Dietary information was collected at about the 20th and 36th week of pregnancy, covering diet during weeks 1–20 and 21–36 of gestation, respectively. Approximately 77.5% of participants had dietary information available at both time points, and prior work has indicated strong correlation between reported foods across these times [In the NHSII, dietary intake was collected in 1991 and every 4 years thereafter, using a previously validated semiquantitative 131-item FFQ ,38. PartDietary patterns, including the empirical dietary inflammatory pattern (EDIP), the Alternative healthy Eating Index (AHEI-2010), the Alternate Healthy Eating Index Modified for Pregnancy (AHEI-P), the Western and Prudent dietary patterns, and the Alternative Mediterranean Diet Score (aMED), were derived in each cohort according to standard approaches ,41,42,43The EDIP was developed using reduced rank regression to summarize the overall inflammatory potential of diet based on intake of food groups shown to predict plasma concentration of three inflammatory markers: interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor α receptor 2 (TNFRα2) ,39. The In both studies, ASD-related traits were measured using the Social Responsiveness Scale (SRS), a widely used and validated measure of ASD-related traits ,47. The Information on autism diagnosis was also available in both studies. In EARLI, autism diagnosis was defined according to clinical assessment and scores on the Autism Diagnostic Observation Schedule (ADOS) ,49,50. Ia priori associations with outcomes and potential or known associations with maternal diet, included maternal age (continuous), child sex , maternal pre-pregnancy BMI (continuous), household income , physical activity (METs/week), child year of birth (in NHSII given the larger distribution), maternal race , maternal prenatal smoking , prenatal vitamin use , parity (continuous), and total energy intake (continuous). Final fully adjusted models did not include those variables with minimal impact on estimates .Data from both cohorts was analyzed in parallel using a common analytical plan. We used descriptive statistics to examine dietary intake in both cohorts and to determine associations with covariates, overall and by quartiles, of dietary patterns. In order to assess associations between maternal dietary patterns and child SRS scores, we used crude and multivariable adjusted quantile regression. Quantile regression allows for assessment of the relationship between exposure and outcome across quantiles of the outcome and also has the benefit over linear regression of being more robust to non-normally distributed outcomes . Our priWe also ran a series of secondary and sensitivity analyses. To compare to associations with ASD diagnosis, logistic regression was used to examine the association of each dietary pattern with ASD diagnosis. Primary analyses were also conducted using SRS T-scores rather than raw scores, in order to facilitate clinical interpretation. To examine whether the association between maternal diet and offspring SRS score differed by severity of autism traits, we fit multivariable quantile regression models fixed at other quantiles . Modification by child’s sex was examined for primary associations (dietary patterns and SRS scores) using stratified models. In addition, because the NHSII did not explicitly measure dietary intake during pregnancy, but rather captured intake overlapping across pregnancy and lactation periods, we stratified analyses for this cohort by the derived timing of dietary assessment. Associations were examined separately in these subgroups (pregnancy and lactation). All statistical analyses were conducted using SAS 9.4.Sociodemographic characteristics of the study participants overall and by cohort are shown in In analyses examining the associations between maternal dietary patterns during pregnancy and child SRS scores in EARLI, overall, strong associations were not observed . EstimatIn NHSII, estimates across quartiles of the EDIP, AHEI, Prudent, and Mediterranean dietary patterns were generally null . There wn values) for both the AHEI-2010 and the aMED, but with wide CIs. In NHSII, there was a non-significant increased odds of ASD with higher EDIP , and mainly null estimates for other dietary patterns.In analyses examining associations with ASD diagnosis, results were generally consistent with those of SRS scores, suggesting no strong associations with these maternal dietary patterns in both cohorts , NHSII. Across additional secondary analyses, results were broadly consistent with primary findings. Findings were consistent with primary analyses when using T-scores . In bothIn this study drawing from two prospective longitudinal cohorts of more than 800 pregnant women in the United States, we did not find strong evidence that maternal dietary patterns were associated with ASD-related traits or diagnosis. This is the first study to examine relationships between a series of established dietary patterns in and surrounding pregnancy and child ASD-related outcomes. Our cross-cohort comparative design also enabled consideration of the role of background familial likelihood of ASD in these relationships. While strong associations were not identified in either population, there were some suggestions of associations in the familial cohort that were not evident in the general population cohort. Given the known importance of prenatal and early life nutrition to child health and development, continued work should address how these dietary patterns and other dietary factors may influence ASD and related outcomes in other ways than assessed here.Few prior studies have addressed the role of prenatal dietary patterns and ASD-related outcomes. A 2018 case–control study in China, including 708 participants, 354 of whom had a confirmed autism diagnosis, examined maternal dietary patterns during and prior to pregnancy in relationship to ASD case status . This stIn our study, we observed some suggestions of a link between unhealthy diet according to the Western diet and increases in ASD-related outcomes. This relationship was stronger in EARLI, and when not adjusting for total energy intake. The Western dietary pattern had the strongest correlation with total energy intake, and variability in total energy intake was also highest in the highest quartile of dietary patterns, which may have contributed to the less precise estimates in these energy-adjusted models. It is also possible that total consumption may be a causal intermediate of the relationship between diet and neurodevelopment, or may have joint effects with other covariates, such as BMI or income. We did not have sufficient sample size in EARLI, where Western pattern associations were primarily seen, to adequately address potential interactions. Prior work has suggested a positive relationship between maternal obesity and gestational weight gain and ASD, and thus weight, increased caloric intake, and unhealthy intake may together influence these outcomes. There was also a non-significant elevation in odds of ASD diagnosis with higher EDIP in the NHSII. Both the EDIP and the Western dietary patterns include pro-inflammatory food groups such as processed and red meat, and high adherence to both dietary patterns is consistent with a pro-inflammatory diet ,41. Pro-There are several potential explanations for the lack of strong associations in our work. One may be due to these being relatively healthy cohorts. There was a relatively high degree of supplement use in these cohorts. It is possible that in less healthy, more nutrient-deficient populations, the association between dietary intake and ASD-related outcomes may be stronger. It is also possible that associations with prenatal diet are tied to more specific phenotypic aspects or subgroups than examined here. Some prior work examining maternal dietary factors, for example, has suggested stronger associations between maternal polyunsaturated fatty acid levels and ASD with co-occurring intellectual disability ,11. DietOur study has several strengths, including the ability to examine multiple established dietary patterns in association with both ASD-related traits and diagnosis in both an increased familial likelihood (EARLI) and general population cohort (NHSII). Additional strengths include clinical assessments in EARLI, the use of established FFQs ,38, and Dietary patterns represent useful tools for understanding and communicating how usual intake of foods relates to outcomes. Patterns more closely assess diet as it is experienced, and also help capture combined effects of dietary factors. Though we did not observe strong associations between maternal dietary patterns and ASD-related outcomes in the two cohorts studied here, future work should consider following up on suggestive findings. This includes addressing potential associations with more pro-inflammatory or unhealthy diets and examining the role of diet in potential critical windows of neurodevelopment. In addition, extensions of this work addressing how dietary patterns may modify associations with other risk factors that share the same pathways are needed.