A machine learning approach for understanding the metabolomics response of children with autism spectrum disorder to medical cannabis treatment.

Autism spectrum disorder (ASD) is a neurodevelopmental condition impacting behavior, communication, social interaction and learning abilities. Medical cannabis (MC) treatment can reduce clinical symptoms in individuals with ASD. Cannabis-responsive biomarkers are metabolites found in saliva that change in response to MC treatment. Previously we showed levels of these biomarkers in children with ASD successfully treated with MC shift towards the physiological levels detected in typically developing (TD) children, and potentially can quantify the impact. Here, we tested for the first time the capabilities of machine learning techniques applied to our dynamic, high-resolution and rich feature dataset of cannabis-responsive biomarkers from a limited number of children with ASD before and after MC treatment and a TD group to identify: (1) biomarkers distinguishing ASD and TD groups; (2) non-cannabinoid plant molecules with synergistic effects; and (3) biomarkers associated with specific cannabinoids. We found: (1) lysophosphatidylethanolamine can distinguish between ASD and TD groups; (2) novel phytochemicals contribute to the therapeutic effects of MC treatment by inhibition of acetylcholinesterase; and (3) THC- and CBD-associated cannabis-responsive biomarkers are two distinct groups, while CBG is associated with some biomarkers from both groups.

The Potential of Salivary Lipid-Based Cannabis-Responsive Biomarkers to Evaluate Medical Cannabis Treatment in Children with Autism Spectrum Disorder.

Introduction: Autism spectrum disorder (ASD) is a group of heterogeneous neurodevelopmental conditions affecting social communication and social interaction. Medical cannabis (MC) treatment shows promising results as an approach to reduce behavioral difficulties, as determined mainly by subjective observations. We have recently shown the potential of cannabis-responsive biomarkers detected in saliva of children with ASD to objectively quantify the impact of successful MC treatment using a metabolomics approach. Since the pathology of ASD is associated with abnormal lipid metabolism, we used lipidomics on the same samples to (1) expand the repertoire of cannabis-responsive biomarkers and (2) provide preliminary insight into the role of MC on lipid metabolism. Materials and Methods: Saliva samples collected from children with ASD (n=15) treated with MC (both before and at the time of maximal impact of treatment) and an age-matched group of typically developing (TD) children (n=9) were subjected to untargeted lipidomics. The study was observational. Each child from the ASD group was receiving a unique individualized MC treatment regimen using off-the-shelf products as permitted by California law under physician supervision for at least 1 year. Doses of tetrahydrocannabinol (THC) ranged from 0.05 to 50 mg and cannabidiol (CBD) from 7.5 to 200 mg per treatment. The ASD group was evaluated for signs of improvement using parental brief Likert scale surveys. Results: Twenty-two potential lipid-based cannabis-responsive biomarkers exhibiting a shift toward the TD physiological levels in children with ASD after MC treatment were identified. Members from all five lipid subclasses known to be present in saliva were characterized. Preliminary lipid association network analysis suggests involvement of two subnetworks previously linked to (1) inflammation and/or redox regulation and (2) oxidative stress. The significant changes in sphingomyelin in this study and in N-acetyl-aspartate (NAA) previously detected in the metabolomics analysis of the same saliva samples may indicate a role of MC in neuron function. Conclusions: Our findings suggest that lipid metabolites in saliva can potentially serve as cannabis-responsive biomarkers and objectively quantify the impact of MC treatment, and indicate a possible mechanism of action for MC. This preliminary study requires further investigation with a larger population and appropriate clinical trial monitoring.

Cannabis-Responsive Biomarkers: A Pharmacometabolomics-Based Application to Evaluate the Impact of Medical Cannabis Treatment on Children with Autism Spectrum Disorder.

Introduction: Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions that impact behavior, communication, social interaction, and learning abilities. Treatment of ASD with medical cannabis (MC) shows promising results in reducing the severity of certain behavioral aspects. The goals of this observational study are to demonstrate the potential of metabolic biomarkers to (1) objectively determine the impact on metabolites of MC treatment and (2) suggest the metabolic pathways of children with ASD, who respond to MC treatment. Materials and Methods: The impact of effective physician-supervised MC treatment on children with ASD (n=15), compared with an age-matched group of typically developing (TD; n=9) children, was evaluated in an observational study design. Each child followed a unique MC regimen determined by their specific response over at least 1 year of treatment, which included the following: tetrahydrocannabinol-dominant MC (dosing range 0.05-50 mg per dose) in 40% of children and cannabidiol-dominant MC (dosing range 7.5-200 mg per dose) in 60% of children. Samples from the ASD group collected pre-MC treatment and at time of maximal impact, and from the TD group, were subjected to salivary metabolomics analysis. Ten minutes before saliva sampling, parents filled out behavioral rating surveys. Results: Sixty-five potential cannabis-responsive biomarkers exhibiting a shift toward the TD physiological levels were identified in children with ASD after MC treatment. For each biomarker, the physiological levels were determined based on the values detected in the TD group. A similar qualitative improvement trend in children with ASD treated with MC was also observed in the behavioral surveys. Twenty-three potential Cannabis-Responsive biomarkers exhibiting change toward TD mean were categorized as anti-inflammatory, bioenergy associated, neurotransmitters, amino acids, and endocannabinoids. The changes in the levels of the Cannabis-Responsive biomarkers N-acetylaspartic acid, spermine, and dehydroisoandrosterone 3-sulfate have been previously linked to behavioral symptoms commonly observed in individuals with ASD. Conclusions: Our results suggest Cannabis-Responsive biomarkers shift toward the TD mean after MC treatment and can potentially quantify benefit at the metabolic level. These changes appear to be similar to the trend described in behavior surveys. Larger trials are needed to confirm these preliminary findings.

Three-dimensional fibroblast cultures stimulate improved ventricular performance in chronically ischemic canine hearts.

The current study's purpose was to evaluate the safety and biological effect of a scaffold-based three-dimensional human dermal fibroblast culture (3DFC, also known as Anginera™) to treat chronically ischemic canine hearts. It was hypothesized that treatment with 3DFC would be safe and significantly improve ventricular performance and wall motion. In this study, chronic myocardial ischemia was induced in 40 animals through the surgical placement of an ameroid constrictor. Approximately 30 days after ameroid placement, animals were randomized into four test groups: (1) sham treatment, (2) one unit of acellular 3DFC, (3) one unit of viable 3DFC, and (4) three units of viable 3DFC. Animals were necropsied 30 or 90 days after treatment. Evaluation of the safety endpoint demonstrated the safety of 3DFC at all dosing levels and at both time points. Additionally, parameters of cardiac output, left ventricular ejection fraction, left ventricular end systolic volume index, and systolic wall thickening support the conclusions that 3DFC stimulates a positive biologic effect on ischemic canine hearts. Further, these data support the conclusion that treatment with viable 3DFC improves ventricular performance and ventricular wall motion in chronically ischemic canine hearts 30 days after treatment.

Theregen, inc.

Theregen, Inc. is a regenerative medicine company based in San Francisco, California, USA, that develops cell-based therapies for patients with cardiovascular and vascular disease. The company is conducting a Phase I human clinical safety trial at two US sites for its lead product, Anginera, a cell-based epicardial patch therapy. Anginera is designed to promote blood vessel growth in damaged cardiac tissue, stimulate wound healing and, potentially, to restore cardiac function in patients with heart failure.

A knowledge-based clustering algorithm driven by Gene Ontology.

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis.

SUMMARY: The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe sets. The GO Mining Tool provides visualization combining biological annotation with expression data, encompassing thousands of genes in one interactive view. AVAILABILITY: GO Mining Tool is freely available at http://www.affymetrix.com/analysis/query/go_analysis.affx

Gene structure-based splice variant deconvolution using a microarray platform.

MOTIVATION: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling. RESULTS: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.

GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs.

GPCR-GRAPA-LIB is a library of HMMs describing G protein coupled receptor families. These families are initially defined by class of receptor ligand, with divergent families divided into subfamilies using phylogenic analysis and knowledge of GPCR function. Protein sequences are applied to the models with the GRAPA curve-based selection criteria. RefSeq sequences for Homo sapiens, Drosophila melanogaster, and Caenorhabditis elegans have been annotated using this approach.

NetAffx: Affymetrix probesets and annotations.

NetAffx (http://www.affymetrix.com) details and annotates probesets on Affymetrix GeneChip microarrays. These annotations include (i) static information specific to the probeset composition; (ii) sequence annotations extracted from public databases; and (iii) protein sequence-level annotations derived from public domain programs, as well as libraries of hidden Markov models (HMMs) developed at Affymetrix. For each probeset, NetAffx lists the probe sequences, and the consensus sequence interrogated by the probes; for the larger chip sets, interactive maps display this sequence data in genomic context. Sequence annotations include Gene Ontology (GO) terms and depiction of GO graph relationships; predicted protein domains and motifs; orthologous sequences; links to relevant pathways; and links to public databases including UniGene, LocusLink, SWISS-PROT and OMIM.

Exploring alternative transcript structure in the human genome using blocks and InterPro.

Understanding how alternative splicing affects gene function is an important challenge facing modern-day molecular biology. Using homology-based, protein sequence analysis methods, it should be possible to investigate how transcript diversity impacts protein function. To test this, high-quality exon-intron structures were deduced for over 8000 human genes, including over 1300 (17 percent) that produce multiple transcript variants. A data mining technique (DiffMotif) was developed to identify genes in which transcript variation coincides with changes in conserved motifs between variants. Applying this method, we found that 30 percent of the multi-variant genes in our test set exhibited a differential profile of conserved InterPro and/or BLOCKS motifs across different mRNA variants. To investigate these, a visualization tool (ProtAnnot) that displays amino acid motifs in the context of genomic sequence was developed. Using this tool, genes revealed by the DiffMotif method were analyzed, and when possible, hypotheses regarding the potential role of alternative transcript structure in modulating gene function were developed. Examples of these, including: MEOX1, a homeobox-containing protein; AIRE, involved in auto-immune disease; PLAT, tissue type plasminogen activator; and CD79b, a component of the B-cell receptor complex, are presented. These results demonstrate that amino acid motif databases like BLOCKS and InterPro are useful tools for investigating how alternative transcript structure affects gene function.

Structure-based comparison of four eukaryotic genomes.

The field of comparative genomics allows us to elucidate the molecular mechanisms necessary for the machinery of an organism by contrasting its genome against those of other organisms. In this paper, we contrast the genome of homo sapiens against C. Elegans, Drosophila melanogaster, and S. cerevisiae to gain insights on what structural domains are present in each organism. Previous work has assessed this using sequence-based homology recognition systems such as Pfam [1] and Interpro [2]. Here, we pursue a structure-based assessment, analyzing genomes according to domains in the SCOP structural domain dictionary. Compared to other eukaryotic genomes, we observe additional domains in the human genome relating to signal transduction, immune response, transport, and certain enzymes. Compared to the metazoan genomes, the yeast genome shows an absence of domains relating to immune response, cell-cell interactions, and cell signaling.

Protein-based analysis of alternative splicing in the human genome.

Understanding the functional significance of alternative splicing and other mechanisms that generate RNA transcript diversity is an important challenge facing modern-day molecular biology. Using homology-based, protein sequence analysis methods, it should be possible to investigate how transcript diversity impacts protein structure and function. To test this, a data mining technique ("DiffHit") was developed to identify and catalog genes producing protein isoforms which exhibit distinct profiles of conserved protein motifs. We found that out of a test set of over 1,300 alternatively spliced genes with solved genomic structure, over 30% exhibited a differential profile of conserved InterPro and/or Blocks protein motifs across distinct isoforms. These results suggest that motif databases such as Blocks and InterPro are potentially useful tools for investigating how alternative transcript structure affects gene function.