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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.

Cristina Piras,1 Bruno Maria Pintus,2 Antonio Noto,1 Maurizio Evangelista,3 Vassilios Fanos,4 Mario Musu,2 Michele Mussap,4 Luigi Atzori,1 Salvatore Sardo,2 Gabriele Finco2 1Department of Biomedical Sciences, University of Cagliari, Monserrato, 09042, Italy; 2Department of Medical Sciences and Public Health, University of Cagliari, Monserrato, 09042, Italy; 3Department of Anesthesiology and Pain Medicine, Cattolica University, Rome, 00168, Italy; 4Department of Surgical Science, University of Cagliari, Monserrato, 09042, ItalyCorrespondence: Antonio Noto, Email antonionoto@hotmail.comAbstract: Chronic pain affects almost 20% of the European adult population and it significantly reduces patients’ quality of life. Chronic pain is considered a multidimensional experience determined by the interaction of several genetic and environmental factors. The effect of specific genetic contributions is often unclear, and the interpretation of the results from studies focused on genetic influences on pain has been complicated by the existence of multiple pain phenotypes. A step forward from genetics could be given by the application of metabolomics and microbiomics tools. Metabolomics is a powerful approach for hypothesis generation in biology, and it aims to analyze low molecular weight compounds, either metabolic intermediates or metabolic end-products, resulting from human or microbial metabolism. Microbiomics is a fast-growing field in which all the microbes are examined together, and as a result, its perturbation may indicate the development of chronic diseases. By applying these methodologies for the study of chronic pain, several differences have been identified. The alteration of the choline-PAF pathway is an intriguing finding recognized by several groups. In our opinion, metabolomics and microbiomics techniques will allow significant progress into the medical field. Patients may benefit from the possibility of being stratified and classified based on their metabolic a

The first aim of this study was to assess the diagnostic performance of presepsin (sCD14-ST) in postmortem serum from femoral blood compared to procalcitonin (PCT) to detect sepsis-related fatalities. The second aim was to compare sCD14-ST levels found in postmortem serum to the values in pericardial fluid to investigate the usefulness of the latter as an alternative biological fluid. Two study groups were formed, a sepsis-related fatalities group and a control group. Radiology (unenhanced CT scans and postmortem angiographies), autopsies, histology, neuropathology, and toxicology as well as other postmortem biochemistry investigations were performed in all cases. Microbiological investigations on right cardiac blood were carried out exclusively in septic cases. The results of this study indicated that postmortem serum PCT and sCD14-ST levels, individually considered, allowed septic cases to be identified. Even though increases in both PCT and sCD14-ST concentrations were observed in the control cases, coherent PCT and sCD14-ST results in cases with suspected sepsis allowed the diagnosis to be confirmed. Conversely, no relevant correlation was identified between postmortem serum and pericardial fluid sCD14-ST levels in either the septic or control groups

The first aim of this study was to assess the diagnostic performance of presepsin (sCD14-ST) in postmortem serum from femoral blood compared to procalcitonin (PCT) to detect sepsis-related fatalities. The second aim was to compare sCD14-ST levels found in postmortem serum to the values in pericardial fluid to investigate the usefulness of the latter as an alternative biological fluid. Two study groups were formed, a sepsis-related fatalities group and a control group. Radiology (unenhanced CT scans and postmortem angiographies), autopsies, histology, neuropathology, and toxicology as well as other postmortem biochemistry investigations were performed in all cases. Microbiological investigations on right cardiac blood were carried out exclusively in septic cases. The results of this study indicated that postmortem serum PCT and sCD14-ST levels, individually considered, allowed septic cases to be identified. Even though increases in both PCT and sCD14-ST concentrations were observed in the control cases, coherent PCT and sCD14-ST results in cases with suspected sepsis allowed the diagnosis to be confirmed. Conversely, no relevant correlation was identified between postmortem serum and pericardial fluid sCD14-ST levels in either the septic or control groups