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Xenobiotics

Q: Are there different types or variations of Xenobiotics?

Yes, Xenobiotics can take many forms and originate from various sources. They include medications, environmental pollutants, and other foreign compounds that are not naturally produced or expected to be present within the body. Each type of Xenobiotic may have unique ADME characteristics and effects on living systems, requiring careful study and consideration.

Xenobiotics are chemical substances found within an organism that are not naturally produced or expected to be present within the body.
These include medications, environmental pollutants, and other foreign compounds.
Researchers studying xenobiotics aim to understand their absorption, distribution, metabolism, and excretion within living systems in order to assess their safety and efficacy.
PubCompare.ai's AI-powered platform can help optimize xenobiotic research by identifying the most reliable protocols from literature, preprints, and patents.
Leveraging advanced comparisons, reserachers can pinpoint the best products and procedures to enhance reproducibility and accuracy in their xenobiotics studies.

Most cited protocols related to «Xenobiotics»

Comprehensive Molecular Catalog Compilation

We loaded catalogs from over 320 commercial vendors and 130 annotated catalogs. Some sources such as HMDB and DrugBank were loaded as several distinct catalogs in ZINC allowing us to leverage the curation of metabolite origin such as plant metabolites in HMDB or drug status such as investigational drugs in DrugBank. All catalogs in ZINC are categorized by their biogenic and bioactivity status, if any.⁹⁵ Only descriptions that characterized the entire catalog contents were applied. For instance, the “Approved” subset of DrugBank was categorized as “World Drugs” since it contains over 100 drugs approved in other countries but not by FDA, and the “Endogenous” subset of HMDB was categorized as having a biogenic type of “endogenous human metabolite”. Molecules inherit biogenic and bioactive properties from the catalogs they are found in. These values are computed and stored, and are accessible in the interface as molecular features. There are four biogenic catalog levels: 1) Endogenous human metabolites, i.e. compounds that are synthesized in man. Interestingly, this may include compounds produced by our bacterial flora; 2) Metabolites of any species, i.e. small molecules that are involved in metabolism, development and reproduction, but not metabolites of xenobiotics; 3) Biogenic compounds, often called natural products; 4) Unknown biogenic status. Likewise, ZINC supports seven levels of bioactivity annotation as follows. 1) FDA approved; 2) World drugs; 3) Investigational, compounds reported to be used in clinical trials; 4) In Man, which including nutraceuticals, for instance; 5) In vivo, which includes DrugBank experimental compounds that have been in animals; 6) In cells, which includes compounds reported active in cell based assays; 7) In vitro, compounds active or assumed active at 10 μM or better in a direct binding assay. All other compounds are marked as having unknown biological activity. The categories are ordered to be progressively inclusive within each series, thus all FDA approved drugs are also world drugs and all compounds active in cells are also active in vitro. We annotate as building blocks those catalogs of compounds available in preparative quantities, typically 250 mg or more. Commercial vendors are categorized by the speed and cost of compound acquisition, allowing the best purchasability of every compound to be computed based on its current catalog membership. Catalog categorizations are refined continually by purchasing experience in our lab and reports from colleagues, as follows:⁹⁵ 1) In stock, delivery in under two weeks, 95% typical acquisition success rate; 2) Procurement agent, in stock, delivery in 2 weeks, 95% typical acquisition success rate; 3) Make-on-demand, delivery typically within 8 to 10 weeks, 70% typical acquisition success rate; 4) Boutique, where the cost may be high, but still likely cheaper than making it yourself, 70% typical acquisition success rate.

Sterling T, & Irwin J.J. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337.

Publication 2015

Anabolism Animals Bacteria Biological Assay Biopharmaceuticals Cells Inclusion Bodies Investigational New Drugs Metabolism Natural Products Nutraceuticals Obstetric Delivery Pharmaceutical Preparations Plants Reproduction Xenobiotics Zinc

Genome-Wide Metabolite Ratio Analysis

A second discovery step was carried out by testing genome-wide associations on all 98,346 (=444*443/2) pairwise ratios of two metabolite concentrations present in both cohorts after QC steps, following the principle described previously ^{11 (link)}. Xenobiotic metabolites were excluded due to the low average call rates per individual. Due to the high number of traits and computation time and costs, for metabolite ratios genome-wide analyses were carried out in the TwinsUK study alone, followed by replication of significant hits (Bonferroni corrected p<5.08×10⁻¹³=5×10⁻⁸/98,346) in KORA F4. SNPs with low MAF (<1%) and info (<0.4) were removed from analysis, as in the association analysis on single metabolites. From the analysis in TwinsUK, a total of 430 loci survived Bonferroni correction and only the top association for each locus (i.e. the metabolite ratio and the SNP pair with the lowest p-value) was carried forward for replication in KORA. Both discovery and replication results were meta-analysed using the inverse variance model, and the combined result was filtered again on Bonferroni adjusted p-value of p<5.08×10⁻¹³ and heterogeneity (p≥0.001). Further notes on the interpretation and reporting of ratios are provided in the Supplementary Note.

Shin S.Y., Fauman E.B., Petersen A.K., Krumsiek J., Santos R., Huang J., Arnold M., Erte I., Forgetta V., Yang T.P., Walter K., Menni C., Chen L., Vasquez L., Valdes A.M., Hyde C.L., Wang V., Ziemek D., Roberts P., Xi L., Grundberg E., Waldenberger M., Richards J.B., Mohney R.P., Milburn M.V., John S.L., Trimmer J., Theis F.J., Overington J.P., Suhre K., Brosnan M.J., Gieger C., Kastenmüller G., Spector T.D, & Soranzo N. (2014). An atlas of genetic influences on human blood metabolites. Nature genetics, 46(6), 543-550.

Publication 2014

DNA Replication Genetic Heterogeneity Genome Xenobiotics

Genome-Wide Metabolite Ratio Analysis

Publication 2014

DNA Replication Genetic Heterogeneity Genome Xenobiotics

Comprehensive Toxicology Database Protocol

The data presented in the table and the corresponding annotations (Additional file 1) have been developed over the past 20 years and longer. A previous compilation [4 (link)] has been completely revised and updated. In addition, more than 170 substances, especially drugs that have been introduced to the market since then, as well as illegal drugs, which became known to cause intoxications, were added. All data were carefully referenced and more than 200 new references were included. Moreover, the annotations providing details were completely revised and more than 100 annotations were added (see Additional file 1).
Reviews, text books, compilations of other authors (mainly [5 -31 ]) and, most importantly, original publications concerning individual drugs and case reports have been used to set up and keep the database updated (see Additional file 1). Experience gained over more than 25 years from working in the clinical and forensic toxicological field contributed to the data presented (see Additional file 1).
The substances were selected by clinical and toxicological aspects, by frequency of prescribing or (mis-)use and other matters in the area of internal intensive care medicine as well as in clinical and forensic toxicology.
There is an increase in determining antibiotic, antiretroviral and antimycotic concentrations using analytical and chemical methods and there are special cases which are closely monitored, although therapeutic concentrations depend on the susceptibility of the microorganisms and tissue concentrations are often more reliable.
The following clinical categories were used for grouping analytical data:
Therapeutic: blood-plasma concentrations (in general, trough at steady state) observed following therapeutically effective doses; no or only minimal side effects (drugs); "normal": concentrations associated with no or only minimal toxic effects (other xenobiotics).
Toxic: blood-plasma concentrations which produce toxicity/clinically relevant side effects/symptoms.
Comatose-fatal: blood-plasma (comatose) concentrations and whole blood (fatal) concentrations reported to have caused coma and death, respectively. Whether published data for deaths refer to levels measured ante-mortem or post-mortem (femoral or heart blood) is, however, often unknown.
As no patient interventions were performed and this compilation does not contain data on human experimental research performed by the authors, ethical review board approval is waived and informed consent does not apply.

Schulz M., Iwersen-Bergmann S., Andresen H, & Schmoldt A. (2012). Therapeutic and toxic blood concentrations of nearly 1,000 drugs and other xenobiotics. Critical Care, 16(4), R136.

Publication 2012

Antibiotics Autopsy BLOOD Comatose Ethical Review Femur Heart Homo sapiens Illicit Drugs Intensive Care Patients Pharmaceutical Preparations Plasma Susceptibility, Disease Therapeutics Tissues Xenobiotics

Comprehensive Curation of Gene Expression Data

Data for the Atlas are selected from ArrayExpress Archive and selection is based on various criteria outlined earlier. As currently we are using only microarray data, our first consideration is whether sufficient array annotation is given to enable us to map the array design elements to existing gene identifiers. We use two routes for this mapping: we preferentially map array probe sequences to Ensembl genomes (15 (link)) or we attempt to map the design element annotation identifiers to gene annotation in UniProt database (16 (link)). Where re-annotation fails, experiments that are performed on such arrays cannot be included in the Atlas. The array re-annotation pipeline will be released as a software package, described and published separately (Sarkans et al., in preparation).
Experiments in ArrayExpress Archive that are performed on well-annotated arrays, which have high MIAME scores (2 ,17 (link)), where the EF/EFV annotation and sufficient replication criteria (as well as some other technical criteria not described here), and where normalized data are present, are annotated as ‘suitable for Atlas’. When all basic criteria are satisfied, experiment selection for the Atlas is motivated by the quality of annotation, use of standard platforms and large sample sizes, without any preference for any biological conditions. Recently, we started to produce themed Atlas data releases, e.g. species oriented or addressing a specific research domain, or by curating user-requested studies. Experiments selected for Atlas are then exported from the Archive. The submitter's; normalized data are used, hence we do not perform any renormalization. Prior to loading into the Atlas, annotations are harmonized, experimental descriptions checked for consistency and non-standard terms are standardized. Maps to EFO are added where the term required is present in the ontology. If terms are not in EFO, we examine source ontologies and provide a term name, definition and maps to external ontologies. The term is then placed in the EFO hierarchy that is optimized for the Atlas visualization.
Once data are loaded, statistical computations, as described in the previous section, are performed and for each new experiment, for each EF and EFV, for each gene the P-value is computed.
Currently, the Atlas contains data from nine species. Table 1 shows the number of assays and the number of studies (experiments) included from each. The experiments included in the Atlas together have more than 40 different EFs, covering over 4500 different EFVs. The distribution of the number of assays for the most frequently studied (at least 50 experiments for each factor) EFs and EFVs are given in Table 2.

Table 1.

Number of studies and assays for each species in the Atlas

Species	Assays	Studies
Homo sapiens	13 703	410
Mus musculus	7539	373
Rattus norvegicus	4858	133
Arabidopsis thaliana	1607	88
Saccharomyces cerevisiae	813	43
Drosophila melanogaster	790	40
Schizosaccharomyces pombe	458	19
Danio rerio	214	13
Caenorhabditis elegans	166	5
Total	30 148	1124

Table 2.

Most frequently used EFs and the number of EFVs and studies for each factor

EFs	EFVs	Studies
Genotype	389	211
Compound treatment	425	196
Disease state	214	137
Organism part	267	98
Cell type	164	61
Growth condition	122	61
Strain or line	227	51

The method used in Gene Expression Atlas analytics allows us to examine trends in differential gene expression across all Atlas data. Figure 5A shows the distribution of proportions of differentially expressed genes across all experiments. There are approximately 400 experiments (from over 1000) with fewer than 10% of all genes showing differential expression; the mean proportion of genes differentially expressed in an experiment, according to our FDR criteria, is 25%. Further, when we examine the number of differentially expressed genes per factor (Figure 5B), we observe that the numbers are highest in the factors ‘observation’, ‘histology’, ‘cell line’, ‘generation’ and ‘organism part’. It appears that, broadly, across species, transcriptional activity is strongly driven by its context: by tissue (‘histology’, ‘organism part’ and, by extension, ‘cell line’), followed by developmental stage and then cell type, while the main extrinsic drivers of transcriptional activity such as xenobiotic responses (‘compound treatment’) and disease states contribute to differential expression to a smaller extent. We can also observe that the number of differentially expressed genes is largely independent of the number of EFVs (the median factor value count is around 3 EFVs).
Figure 5.

Distributions of differentially expressed genes over (A) experiments and (B) EFs. Error bars in (B) mark the 25% and 75% quantiles in the differentially expressed gene count for each EF.

Kapushesky M., Emam I., Holloway E., Kurnosov P., Zorin A., Malone J., Rustici G., Williams E., Parkinson H, & Brazma A. (2009). Gene Expression Atlas at the European Bioinformatics Institute. Nucleic Acids Research, 38(Database issue), D690-D698.

Publication 2009

Biological Assay Biopharmaceuticals Cell Lines Cells DNA Replication efavirenz Gene Components Gene Expression Gene Expression Profiling Genes Genome Microarray Analysis Microtubule-Associated Proteins Patient Discharge Tissues Transcription, Genetic Xenobiotics

Most recents protocols related to «Xenobiotics»

Selectivity of FAAH Inhibitors Against Serine Hydrolases

Not available on PMC !

Example 39

Generally, pharmacophores for FAAH inhibitors, urea and non-urea based, interact by either carbamoylating or forming transition-state mimics with the catalytic serine residue. However, since a large number of hydrolases utilize a similar catalytic serine residue, many FAAH inhibitors have suffered from poor selectivity. Therefore, the potency of t-TUCB, A-14 and A-21 on several other serine hydrolases was tested. Included in this panel were carboxylesterases, hydrolases involved in xenobiotic detoxification, and paraoxonases and esterases involved in the regulation of arterosclerosis. As is shown in Table 5 below, none of these serine hydrolases were inhibited by t-TUCB, A-14, or A-21.

TABLE 5

Selectivity of A-14 and A-15 against other serine hydrolases.

IC₅₀(nM)

Enzyme1728A-14A-21

FAAH14024120

sEH0.832

MAGL>10,000>10,000>10,000

hCE1>10,000>10,000>10,000

hCE2>10,000>10,000>10,000

PON1>10,000>10,000>10,000

PON2>10,000>10,000>10,000

PON3>10,000>10,000>10,000

AADAC>10,000>10,0005,400

US11873288B2. Inhibitors for soluble epoxide hydrolase (sEH) and fatty acid amide hydrolase (FAAH) (2024-01-16). The Regents of the University of California [US]. Inventors: Bruce Hammock [US], Sean Kodani [US].

Patent 2024

Aryldialkylphosphatase Carboxylic Ester Hydrolases Catalysis Enzymes Esterases Genetic Selection Hydrolase inhibitors Metabolic Detoxication, Drug PON1 protein, human PON2 protein, human Serine Urea Xenobiotics

RNA-seq Analysis of Bipolar Disorder and Schizophrenia

We used the Illumina RiboZero TruSeq Stranded Total RNA Library Prep Kit (Illumina) to construct the RNA-seq library and used the Illumina NovaSeq6000 platform for sequencing in the 100 nt, paired-end configuration, as described previously [20 (link), 21 (link)]. We obtained an average of 60 million reads for each sample. The reads were trimmed with Cutadapt and aligned to the reference genome (hg38 UCSC assembly) to analyze gene expression using TopHat v2.0.14 and Bowtie v2.10 with default parameters and RefSeq annotation (genome-build GRCh38.p9) [22 (link)]. We used Cufflinks v2.2.1 to analyze distribution of alignments and quantile normalized FPKM (fragments per kilobase of exon model per million reads mapped) values [23 (link), 24 (link)]. We utilized Cuffdiff v2.2.1 to perform differential expression testing. We did not consider sex differences for this analysis. The false discovery rate (FDR) was 0.05. The raw data analysis is included in Supplementary file 1, in sheet 3 titled significant genes. Tables 1, 2 and 3 show q values represent FDR-adjusted p-value of the test statistic. RT-PCR was used to validate a number of key relevant genes.Table 1

Gene set enrichment analysis

	Size	ES	NES	NOM p-val	FDR q-val	FWER p-val	Rank at max	Leading edge
Reactome
Reactome_Influenza_Infection	154	− 0.59132	− 2.80241	0	0	0	10,503	tags = 73%, list = 27%, signal = 99%
Reactome_Mitochondrial_Translation	93	− 0.60753	− 2.62088	0	0	0	13,391	tags = 86%, list = 34%, signal = 131%
Reactome_Respiratory_Electron_Transport	89	− 0.60034	− 2.61611	0	0	0	12,856	tags = 84%, list = 33%, signal = 125%
Reactome_Infectious_Disease	371	− 0.49008	− 2.60519	0	0	0	13,579	tags = 68%, list = 35%, signal = 103%
Reactome_Cell_Cycle_Checkpoints	282	− 0.51048	− 2.60441	0	0	0	15,024	tags = 74%, list = 39%, signal = 120%
Reactome_Naplus_Cl_Dependent_Neurotransmitter_Transporters	19	0.515474	1.541765	0.038388	0.66119	1	1466	tags = 32%, list = 4%, signal = 33%
Reactome_Role_Of_Phospholipids_In_Phagocytosis	33	0.452627	1.529319	0.032136	0.623584	1	4477	tags = 24%, list = 11%, signal = 27%
Reactome_Plasma_Lipoprotein_Assembly	19	0.504823	1.504998	0.043222	0.652722	1	2605	tags = 32%, list = 7%, signal = 34%
Reactome_Xenobiotics	24	0.466743	1.485898	0.049904	0.666401	1	8655	tags = 50%, list = 22%, signal = 64%
Reactome_Long_Term_Potentiation	23	0.474369	1.470371	0.046693	0.608513	1	8104	tags = 48%, list = 21%, signal = 60%
Hallmark
Hallmark_Myogenesis	199	− 0.55399	− 2.73616	0	0	0	5986	tags = 43%, list = 15%, signal = 51%
Hallmark_E2F_Targets	198	− 0.51894	− 2.56449	0	0	0	13,107	tags = 65%, list = 34%, signal = 98%
Hallmark_Oxidative_Phosphorylation	185	− 0.52698	− 2.53241	0	0	0	13,844	tags = 71%, list = 36%, signal = 109%
Hallmark_Unfolded_Protein_Response	111	− 0.43549	− 1.96044	0	0	0	14,339	tags = 65%, list = 37%, signal = 102%
Hallmark_Glycolysis	198	− 0.38222	− 1.88766	0	0	0	13,694	tags = 57%, list = 35%, signal = 87%
Hallmark_Pancreas_Beta_Cells	40	0.268541	0.960266	0.515539	1	1	2039	tags = 10%, list = 5%, signal = 11%
Hallmark_Spermatogenesis	132	0.17472	0.771267	0.937394	1	1	9809	tags = 27%, list = 25%, signal = 36%
Hallmark_Xenobiotic_Metabolism	200	0.164059	0.76698	0.98087	0.93739	1	5051	tags = 12%, list = 13%, signal = 13%
KEGG
KEGG_Ribosome	86	− 0.66314	− 2.80034	0	0	0	10,890	tags = 93%, list = 28%, signal = 129%
KEGG_Parkinsons_Disease	99	− 0.50716	− 2.26377	0	0	0	13,694	tags = 75%, list = 35%, signal = 115%
KEGG_Oxidative_Phosphorylation	100	− 0.51326	− 2.26148	0	0	0	13,763	tags = 71%, list = 35%, signal = 109%
KEGG_Small_Cell_Lung_Cancer	84	− 0.50382	− 2.18709	0	0	0	8917	tags = 50%, list = 23%, signal = 65%
KEGG_P53_Signaling_Pathway	67	− 0.52866	− 2.14187	0	9.17E−04	0.003	12,063	tags = 64%, list = 31%, signal = 93%
KEGG_Taste_Transduction	51	0.457469	1.688169	0.001692	0.234029	0.361	7615	tags = 49%, list = 20%, signal = 61%
KEGG_Linoleic_Acid_Metabolism	28	0.510943	1.677292	0.011858	0.129439	0.388	8626	tags = 43%, list = 22%, signal = 55%
KEGG_Type_I_Diabetes_Mellitus	42	0.433637	1.558308	0.012797	0.188571	0.766	8960	tags = 43%, list = 23%, signal = 56%
KEGG_Asthma	29	0.486376	1.629667	0.013619	0.132661	0.538	8960	tags = 52%, list = 23%, signal = 67%
KEGG_Retinol_Metabolism	64	0.358277	1.385493	0.043956	0.410179	0.994	7681	tags = 28%, list = 20%, signal = 35%

Gene set enrichment analysis (GSEA) data for DEGs using three different databases (Hallmark, KEGG, Reactome) tabulated according to normalized enrichment score (NES) and False discovery rate (FDR) q-value. Positive correlation indicates relative association with gene expression in bipolar disorder and a negative correlation indicates relative association with gene expression in schizophrenia

Table 2

Mitochondrial genes upregulated in schizophrenia compared to bipolar disorder

Gene	Log2(fold_change)	P_value	Q_value
B2M	inf	0.00005	0.0168915
SFTPB	5.47445	0.00005	0.0168915
P2RX3	4.09903	0.00015	0.041525
PKHD1L1	4.00139	0.00005	0.0168915
THSD7B	3.9018	0.00005	0.0168915
EBF2	3.73444	0.00005	0.0168915
ACTA1	3.71003	0.00005	0.0168915
SHOX2	3.54632	0.00005	0.0168915
BCL6B	3.40011	0.00005	0.0168915
MUC5B	3.27599	0.00005	0.0168915
CD5	3.27526	0.00005	0.0168915
CASQ2	3.27468	0.00005	0.0168915
MYBPC2	3.20693	0.00005	0.0168915
NPTX2	3.17192	0.0001	0.0297493
NHLH1	3.15736	0.00005	0.0168915
MYBPH	3.05216	0.00005	0.0168915
ASB4	3.01621	0.0001	0.0297493
MUC12	2.87732	0.00005	0.0168915
SHD	2.83455	0.00005	0.0168915
TRIM55	2.73832	0.00005	0.0168915
ACTC1	2.72941	0.00005	0.0168915
MYH3	2.66443	0.00005	0.0168915
EBF1	2.6039	0.00005	0.0168915
CHRND	2.60343	0.00005	0.0168915
APLN	2.54672	0.00005	0.0168915
GRID2	2.50442	0.0002	0.0480289
MYL4	2.42199	0.0001	0.0297493
KRT1	2.38926	0.0002	0.0480289
TNNT2	2.26791	0.00005	0.0168915
NEFM	2.25293	0.00005	0.0168915
AFAP1L1	2.2312	0.00005	0.0168915
EYA1	2.10989	0.0002	0.0480289
RBM24	2.05014	0.0002	0.0480289
RYR1	2.00653	0.00005	0.0168915
C7	1.92159	0.00005	0.0168915
COL19A1	1.87283	0.0001	0.0297493
DUOX2	1.86384	0.0001	0.0297493
SERPINA3	1.79983	0.00005	0.0168915
TNNC1	1.798	0.0002	0.0480289
NTRK2	1.73311	0.00005	0.0168915
CAPN6	1.60497	0.00005	0.0168915
NEUROD1	1.53609	0.00005	0.0168915
MCAM	1.52598	0.00005	0.0168915
SPOCK2	1.43396	0.00005	0.0168915
ARHGAP29	1.29965	0.00005	0.0168915
CDR1	1.0788	0.0001	0.0297493
PEG10	1.07848	0.0002	0.0480289
FREM2	1.00581	0.0002	0.0480289

List of mitochondria-associated genes in the MitoCarta 2.0 database that were upregulated in SCZ organoids vis-à-vis BPI organoids and in BPI organoids vis-à-vis SCZ organoids

Table 3

Mitochondrial genes upregulated in bipolar disorder compared to schizophrenia

Gene	Log2(fold_change)	P_value	Q_value
CCL25	4.42404	0.00005	0.0168915
GIP	4.04306	5.00E−05	0.0168915
HLA-DRB1	3.66592	0.00005	0.0168915
OPRK1	3.58192	0.00005	0.0168915
CX3CR1	3.33744	0.00005	0.0168915
ASAH2	3.32305	0.00005	0.0168915
LCT	3.24615	0.00005	0.0168915
APOC3	3.05208	0.00005	0.0168915
SLC2A2	2.78622	0.0001	0.0297493
FOLH1	2.75393	0.00005	0.0168915
GATA4	2.37677	0.0002	0.0480289
ANXA13	2.13002	0.00005	0.0168915
COL2A1	1.98566	0.00005	0.0168915
SI	1.92321	0.00005	0.0168915
PIGR	1.76004	0.00005	0.0168915
SLC5A1	1.70496	0.0001	0.0297493
APOB	1.69844	0.00005	0.0168915
SULT2A1	1.69654	0.0002	0.0480289
MTTP	1.66128	0.00005	0.0168915
MALRD1	1.6595	0.00005	0.0168915
OLFM4	1.61724	0.00005	0.0168915
GSTA1	1.3839	0.00005	0.0168915
OAT	1.24558	0.00015	0.041525
FOS	1.2401	0.00005	0.0168915
PRLR	1.21405	0.00015	0.041525
XDH	1.21051	0.00005	0.0168915
MME	1.07592	0.00005	0.0168915

Kathuria A., Lopez-Lengowski K., McPhie D., Cohen B.M, & Karmacharya R. (2023). Disease-specific differences in gene expression, mitochondrial function and mitochondria-endoplasmic reticulum interactions in iPSC-derived cerebral organoids and cortical neurons in schizophrenia and bipolar disorder. Discover Mental Health, 3(1), 8.

Publication 2023

All-Trans-Retinol Bipolar Disorder cDNA Library Cell Cycle Cells Diabetes Mellitus, Insulin-Dependent DNA, Mitochondrial E2F2 protein, human Electrons Exons Gene Expression Genes Genes, vif Genome Infection Influenza Linoleic Acid Lipoproteins Lung Mitochondria Neurotransmitters Organoids Pancreas Phospholipids Plasma Proteins Respiratory Rate Reverse Transcriptase Polymerase Chain Reaction RNA-Seq Schizophrenia Taste Xenobiotics

Mechanistic Insights into TCDD Toxicity

The test compound analyzed in this study was 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (LGC Standards, Wesel, Germany), a persistent organic pollutant and endocrine disruptor compound, and the corresponding highest concentration tested was 25 nM. The rationale for the selection of the test compound was to include a well-known xenobiotic of high relevance for human and environmental health. Therefore, we included TCDD and its concentration from a previous study for in vitro exposures to the human hepatic cell line HepaRG. Concentration selection was based on the translation of external intakes into internal doses in hepatic cells. The external intake estimates from plasma monitored levels or environmental, accidental, and occupational conditions of TCDD were associated with target tissue (liver cells) dosimetry using a generic physiologically based biokinetic model. The highest serum levels observed correspond to an intake of up to 15 ng/kg_bw/d which corresponded to an internal dose in the liver cells of up to 25 nM TCDD [20 (link)].

Lizano-Fallas V., Carrasco del Amor A, & Cristobal S. (2023). Prediction of Molecular Initiating Events for Adverse Outcome Pathways Using High-Throughput Identification of Chemical Targets. Toxics, 11(2), 189.

Publication 2023

Accidents Endocrine Disruptors Generic Drugs Hepatocyte Homo sapiens Occupational Diseases Persistent Organic Pollutants Plasma Radiometry Serum Tetrachlorodibenzodioxin Tissues Xenobiotics

Untargeted Serum Metabolomics in PLCO and Jiangsu

In the PLCO, untargeted serum metabolomics data were generated by using the Metabolon Inc. platform consisting of ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS). The details of the procedures have been described elsewhere [17 (link)]. In brief, protein precipitation with methanol was performed to extract a broad coverage of metabolites in the serum. The extracts for UHPLC-MS/MS were analyzed on a Waters ACQUITY UPLC (Waters, Milford, MA, USA), and the extracts for GC-MS were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole MS (Thermo Finnegan, San Jose, CA, USA). Each batch contained up to 30 samples, including blinded quality-control samples of pooled serum at a level of 10%. Matched cases and controls were consecutively arranged in a counterbalanced order within each batch. In addition, a standard was spiked every six samples for quality control. The metabolites were identified by comparison to a chemical reference library generated from 2500 standards. A total of 447 named metabolites were identified, out of which 278 metabolites were measured in >80% of the participants and included in the analysis. These metabolites included amino acids, lipids, peptides, carbohydrates, cofactors and vitamins, xenobiotics, nucleotides, and energy.
In the Jiangsu cohort, untargeted metabolites in the plasma were measured using UHPLC-MS/MS at Metabolon, as described in detail elsewhere [16 (link),18 (link)]. Briefly, based on ACQUITY UPLC (Waters, Milford, MA, USA) and Q Exactive HF hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific, San Jose, CA, USA), four independent UHPLC-MS/MS methods were applied: two separate reverse-phase (RP)/UHPLC-MS/MS methods with positive-ion mode electrospray ionization (ESI), RP/UHPLC-MS/MS with negative-ion mode ESI, and hydrophilic interaction liquid chromatography (HILIC)/UHPLC-MS/MS with negative-ion mode ESI. The methods for quality control and metabolite identification were similar to those used in the PLCO.

Yang M., Zhu C., Du L., Huang J., Lu J., Yang J., Tong Y., Zhu M., Song C., Shen C., Dai J., Lu X., Xu Z., Li N., Ma H., Hu Z., Gu D., Jin G., Hang D, & Shen H. (2023). A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case–Control Studies. Metabolites, 13(2), 234.

Publication 2023

Amino Acids Carbohydrates Chromatography Chromatography, Reversed-Phase Liquid Gas Chromatography-Mass Spectrometry High-Performance Liquid Chromatographies Hybrids Lipids Methanol Nucleotides Peptides Plasma Proteins Serum Spectrometry Tandem Mass Spectrometry Vitamins Xenobiotics

C. elegans Transgenic Strain Protocols

The transgenic C. elegans strains BC20333 (sod-4::GFP) and BC20306 (cyp-34A9::GFP) have been previously described [37 (link)] and were kindly provided by David De Pomerai (University of Nottingham, UK). These integrated reporter strains express the green fluorescent protein (GFP) under the control of promoters that are responsive to either oxidative (sod-4) or xenobiotic (cyp-34A9) stress.
The C. elegans strains were grown and maintained at 20 °C on NGM (nematode growth medium) agar plates seeded with the E. coli strain OP50 or its streptomycin-resistant derivative OP50-1 using standard culturing methods [57 (link),58 (link)]. Before experimentation, the C. elegans populations were synchronized by bleaching. For liquid assays, well-fed day one adults were washed three times with K medium (53 mM NaCl, 32 mM KCl) to remove bacteria, after which aliquots of 800–900 animals in 295 µL of K medium were placed to wells of 96-well plates with optically transparent bottoms (Black IsoPlate-96, PerkinElmer, Waltham, MA, USA). Then the animals were exposed in triplicates to increasing concentrations of exposure agents. For airborne exposures, two-compartment petri dishes (Greiner Bio-One, Thermo Fisher Scientific, Waltham, MA, USA) were used, in which the C. elegans nematodes were grown on one side and the microbes or materials to be analysed on the other side, both on their own optimal growth media.

Paavanen-Huhtala S., Kalichamy K., Pessi A.M., Häkkilä S., Saarto A., Tuomela M., Andersson M.A, & Koskinen P.J. (2023). Biomonitoring of Indoor Air Fungal or Chemical Toxins with Caenorhabditis elegans nematodes. Pathogens, 12(2), 161.

Publication 2023

Adult Agar Animals Animals, Transgenic Bacteria Biological Assay Caenorhabditis elegans Culture Media Culture Techniques Escherichia coli Green Fluorescent Proteins Hyperostosis, Diffuse Idiopathic Skeletal Nematoda Population Group Sodium Chloride Strains Streptomycin Xenobiotics

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The CH-223191 is a laboratory instrument designed for cell culture and related applications. It functions to precisely control and maintain the temperature, humidity, and gas composition of the environment surrounding cultured cells. The device specifications and technical details are available upon request.

What are the common challenges in studying Xenobiotics?

Studying Xenobiotics can be complex due to their diverse nature and varying effects within living systems. Some of the common challenges include accurately measuring absorption, distribution, metabolism, and excretion (ADME) of these foreign compounds, as well as assessing their safety and efficacy. Ensuring reproducibility and accuracy in xenobiotic research is crucial, as these compounds can have significant impacts on biological processes.

How can PubCompare.ai help optimize Xenobiotic research?

PubCompare.ai's AI-powered platform can assist in optimizing Xenobiotic research in several ways. First, it allows researchers to screen protocol literature more efficiently, saving time and effort. The platform's AI-driven analysis can also help identify critical insights, such as key differences in protocol effectiveness for specific Xenobiotic studies. This enables researchers to choose the most effective protocols, enhancing reproducibility and accuracy in their work.

Are there different types or variations of Xenobiotics?

What are some specific applications of Xenobiotic research?

Xenobiotic research has a wide range of applications, including: 1. Assessing the safety and efficacy of new drugs or environmental compounds. 2. Studying the impact of pollutants and toxins on human and animal health. 3. Optimizing the delivery and metabolism of therapeutic agents. 4. Understanding the mechanisms by which foreign substances are processed by the body. 5. Developing strategies to mitigate the harmful effects of Xenobiotics.

More about "Xenobiotics"

Xenobiotics, also known as foreign compounds or exogenous substances, are chemical entities that are not naturally produced or expected to be present within an organism's body.
These include a wide range of substances, such as medications, environmental pollutants, and other foreign compounds.
Researchers studying xenobiotics aim to understand their absorption, distribution, metabolism, and excretion (ADME) within living systems in order to assess their safety and efficacy.
One key aspect of xenobiotic research involves the use of various analytical techniques and assays.
For example, the FBS (Fetal Bovine Serum) assay is commonly used to assess the cytotoxicity of xenobiotics, while the GoldenGate assay can be employed to study genetic variations in response to xenobiotic exposure.
The Oragene DNA Kit, on the other hand, is a useful tool for collecting and preserving DNA samples from xenobiotic studies.
Transfection reagents, such as Lipofectamine 2000, are also widely used in xenobiotic research to introduce genetic material into cells, enabling the investigation of gene expression and signaling pathways affected by xenobiotic exposure.
The Luciferase Assay System and Dual-Glo Luciferase Assay System are commonly utilized to measure reporter gene activity, which can provide insights into the mechanisms of xenobiotic action.
Caffeine, a widely consumed xenobiotic, has also been the subject of extensive research, with studies exploring its effects on various physiological processes and potential interactions with other xenobiotics.
Additionally, researchers may employ tools like the MinElute PCR Purification Kit to purify DNA or RNA samples from xenobiotic studies, and compounds such as CH-223191 can be used as chemical probes to investigate the role of specific receptors in xenobiotic metabolism and signaling.
PubCompare.ai's AI-powered platform can greatly benefit xenobiotic research by helping researchers identify the most reliable protocols and procedures from the literature, preprints, and patents.
By leveraging advanced comparisons, researchers can pinpoint the best products and methods to enhance the reproducibility and accuracy of their xenobiotic studies, ultimately advancing our understanding of these foreign compounds and their impact on living systems.