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.

The increase in pandemics caused by RNA viruses of zoonotic origin highlights the urgent need for broad-spectrum antivirals against novel and re-emerging RNA viruses. Broad-spectrum antivirals could be deployed as first-line interventions during an outbreak while virus-specific drugs and vaccines are developed and rolled out. Viruses depend on the host’s protein synthesis machinery for replication. Several natural compounds that target the cellular DEAD-box RNA helicase eIF4A, a key component of the eukaryotic translation initiation complex eIF4F, have emerged as potential broad-spectrum antivirals. Rocaglates, a group of flavaglines of plant origin that clamp mRNAs with highly structured 5′ untranslated regions (5′UTRs) onto the surface of eIF4A through specific stacking interactions, exhibit the largest selectivity and potential therapeutic indices among all known eIF4A inhibitors. Their unique mechanism of action limits the inhibitory effect of rocaglates to the translation of eIF4A-dependent viral mRNAs and a minor fraction of host mRNAs exhibiting stable RNA secondary structures and/or polypurine sequence stretches in their 5′UTRs, resulting in minimal potential toxic side effects. Maintaining a favorable safety profile while inducing efficient inhibition of a broad spectrum of RNA viruses makes rocaglates into primary candidates for further development as pan-antiviral therapeutics.

Inhibition of eukaryotic initiation factor 4A has been proposed as a strategy to fight pathogens. Rocaglates exhibit the highest specificities among eIF4A inhibitors, but their anti-pathogenic potential has not been comprehensively assessed across eukaryotes. In silico analysis of the substitution patterns of six eIF4A1 aa residues critical to rocaglate binding, uncovered 35 variants. Molecular docking of eIF4A:RNA:rocaglate complexes, and in vitro thermal shift assays with select recombinantly expressed eIF4A variants, revealed that sensitivity correlated with low inferred binding energies and high melting temperature shifts. In vitro testing with silvestrol validated predicted resistance in Caenorhabditiselegans and Leishmaniaamazonensis and predicted sensitivity in Aedes sp., Schistosomamansoni, Trypanosomabrucei, Plasmodiumfalciparum, and Toxoplasmagondii. Our analysis further revealed the possibility of targeting important insect, plant, animal, and human pathogens with rocaglates. Finally, our findings might help design novel synthetic rocaglate derivatives or alternative eIF4A inhibitors to fight pathogens.

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.

Recently, and in a tour de force effort, Yap et al . performed a comprehensive association analysis of factors such as demographics, psychometrics, diet, stool metagenomics, stool consistency, and genome-wide SNP genotypes with autism in 247 Australian children. Surprisingly, the authors suggest their data show a strong correlation between diet and autism spectrum disorder (ASD) but only negligible, if any, ASD-specific microbiome signals. While the first conclusion comes as no surprise, we were rather puzzled by the second conclusion given the growing evidence of strong associations between the microbiome and ASD phenotype and the wide consensus on a close connection between diet and microbiome composition and function. The causal model proposed by Yap et al . seemed to imply that diet and the microbiome were two independent variables. A careful review of the approach used by the authors confirmed our suspicions that the statistical models were mis-specified, i.e. they had a questionable biological assumption—the independence of diet and microbiome—embedded in them. We have run side-by-side simulations of the causal linear model proposed by Yap et al . and of an analogous model in which diet and the microbiome are treated as co-dependent variables. We show how the Yap et al . model can preemptively exclude any potential host-microbe interactions if the diet-microbiome independence assumption is violated. We believe large-scale efforts such as the one described by Yap et al . are essential to advance our understanding of the potential role of the microbiome in ASD and other diseases. But these are highly complex systems to analyze and thus ensuring that the statistical methods used are accurate is essential to avoid drawing any potentially misleading conclusions due to subtle causal assumptions propagated by the statistical models themselves.

Selective inhibition of eukaryotic initiation factor 4A (eIF4A), an RNA helicase, has been proposed as a strategy to fight pathogens. Plant-derived rocaglates exhibit some of the highest specificities among eIF4A inhibitors. Sensitivity to rocaglates is determined by key amino acid (aa) residues mediating reversible clamping of the eIF4A:RNA complex. To date, no comprehensive assessment of eIF4A sensitivity to rocaglates across the eukaryotic tree of life has been performed to determine their anti-pathogenic potential. We performed an in silico analysis of the substitution patterns of six aa residues in eIF4A1 critical to rocaglate binding (human positions 158, 159, 163, 192, 195, 199), uncovering 35 pattern variants among 365 eIF4As sequenced to date. In silico molecular docking analysis of the eIF4A:RNA:rocaglate complexes of the 35 variants, modeled in a human eIF4A environment, and in vitro thermal shift assays with recombinantly expressed human eIF4A mutants, representing select natural and artificial variants, revealed that sensitivity to a natural or one of two synthetic rocaglates—silvestrol, CR-1-31-B, or zotatifin—was associated with lower inferred binding energies and higher melting temperature shifts. Helicase activities were comparable across variants and independent of sensitivity to rocaglates. In vitro testing with silvestrol validated predicted resistance based on position 163 substitutions in Caenorhabditis elegans and Leishmania amazonensis and predicted sensitivity in Aedes sp. , Schistosoma mansoni , Trypanosoma brucei , Plasmodium falciparum , and Toxoplasma gondii . Our analysis shows resistance to rocaglates emerging in disparate eukaryotic clades pointing to resistance being a selective neutral trait except in rocaglate-producing Aglaia plants and their fungal parasite Ophiocordyceps . The analysis further revealed the possibility of targeting important insect, plant, animal, and human pathogens including Galleria mellonella , Ustilago maydis , Babesia ovata , and Cryptosporidium sp. , with rocaglates. Finally, combined docking and thermal shift analyses might help design novel synthetic rocaglate derivatives or alternative eIF4A inhibitors to fight pathogens. Author Summary: In the ongoing search for novel ways to fight non-viral and non-bacterial pathogens, targeting translation—the universal process of protein synthesis—to inhibit growth and cell proliferation has emerged as an attractive strategy. Here, we focused on the potential of rocaglates, a group of plant-derived compounds, to inhibit an early step in translation mediated by a RNA helicase called eIF4A. We performed a comprehensive analysis of eIF4A sequence variants to determine their potential sensitivities to rocaglates, especially in pathogens of prokaryotic, fungal, or animal origin. We complemented this in silico analysis with enzyme-based in vitro and whole pathogen in vivo experiments to confirm the sensitivity or resistance to rocaglates of specific variants of eIF4A. Our analysis provides the first comprehensive picture of rocaglate sensitivity among pathogens and establishes targeting important insect, plant, animal, and human pathogens such as wax moth larvae, a major parasite of honey bees, corn smut, a widely distributed fungal disease, Babesia , a livestock parasite that causes anemia and babesiois, and Cryptosporidium , the causative organism of cryptosporidiosis in humans, with rocaglates as a viable anti-pathogen strategy.