Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects. Small drug molecules usually bind to unintended off-targets, leading to unexpected drug responses such as side effects or drug repositioning opportunities. Thus, identifying unintended drug-target interactions (DTI) is particularly required for understanding complicated drug actions. It remains expensive nowadays to experimentally determine DTI, so various computational methods are developed. In this study, we initiatively demonstrated that target binding is directly correlated with drug induced genomic expression profiles in Connectivity Map (CMap). By improving data quality of CMap, we illustrated three important facts: (1) Drugs binding to common targets show higher gene-expression similarity than random compounds, indicating that upstream ligand binding could be characterized by downstream gene-expression change. (2) It is found that some targets are better characterized by CMap than others. To guarantee efficiency of DTI discovery, prediction models should be specifically built for those well characterized targets. (3) It is broadly observed in the predicted DTI that ligands for the same target may collectively interact with common off-target. This observation is consistent with published experimental evidence and can help illustrate the mechanisms of unexplained drug reactions. Based on CMap, our work established an efficient pipeline of identifying potential DTI. By extending the success in CMap to other genomic data sources, we believe more DTI would be discovered.

Previous studies have shown that gut microbiota can affect human immune system in many ways. Our aim was to investigate quantitative differences in fecal bacterial compositions of childhood acute lymphoblastic leukemia (ALL) patients compared to those of healthy children, so as to identify individual bacterial species that are related to the etiology of ALL. We recruited 81 subjects, including 58 patients with ALL and 23 healthy controls. Fecal samples were collected and examined by 16S rRNA quantitative arrays and bioinformatics analysis. Both Principal Coordinates Analysis (PCoA) and Non-metric Multidimensional scaling (NMDS) demonstrated that the microbial composition of ALL patients deviated from the tight cluster of healthy controls. Multiple bacterial species exhibited significant changes (e.g., Roseburia faecis, Edwardsiella tarda, and Fusobacterium naviforme) in the ALL samples. Some of the differentially abundant taxa were correlated with the level of interleukin-10. The ALL cases could be efficiently distinguished from healthy controls by the random forest model based on differential species (area under ROC curve = 0.843). Taken together, the composition of gut microbiota differed from healthy controls to pediatric ALL patients. Our study identified a series of ALL-related species in the gut microbiota, providing a new direction for future studies aiming to understand the host-gut microbiota interplay in ALL pathogenesis.

Background. As a transitional state between normal aging and Alzheimers disease (AD), mild cognitive impairment (MCI) is characterized by a worse cognitive decline than that of natural aging. The association between AD and gut microbiota has been reported in a number of studies; however, microbial research regarding MCI remains limited. Methods. This study examined 48 participants, of whom 22 were MCI cases and 26 were normal control cases. Fecal samples were collected for 16S ribosomal RNA (rRNA) quantitative arrays and bioinformatics analysis. Results. A principal coordinates analysis (PCoA) and nonmetric multidimensional scaling (NMDS) both demonstrated that the microbial composition of participants with MCI deviated from that of healthy control participants. Multiple bacterial species were significantly increased (e.g., Staphylococcus intermedius) or decreased (e.g., Bacteroides salyersiae) in samples from the MCI group. Conclusion. The composition of gut microbiota differed between normal control and MCI cases. This is the first study to identify a signature series of species in the gut microbiota of individuals with MCI. The results provide a new direction for the future development of an early diagnosis and probiotic regimen.

T cell receptor (TCR)-mediated immune functions are closely related to autoimmune diseases, such as systemic lupus erythematosus (SLE). However, technical challenges used to limit the accurate profiling of TCR diversity in SLE and the characteristics of SLE patients remain largely unknown. In this study, we collected peripheral blood samples from 10 SLE patients with lupus nephritis (LN) who were confirmed by renal biopsy, as well as 10 healthy controls. The TCR repertoire of each sample was assessed by high-throughput sequencing to examine the distinction between SLE subjects and healthy controls. Our results showed statistically significant differences in TCR diversity and usage of TRBV/TRBJ genes between the two groups. A set of signature V–J combinations enabled efficient identification of SLE cases, yielding an area under the curve (AUC) of 0.89 (95% CI: 0.74–1.00). Taken together, our results revealed the potential correlation between the TCR repertoire and SLE status, which may facilitate the development of novel immune biomarkers.

Background: Fetal growth restriction (FGR) in utero leads to failure of fetus to reach the genetically normal growth potential. Currently available means of treating FGR are limited. And it remains unknown how pregnant women who give birth to FGR fetus differ in gut microbiota composition from normal pregnant women.Methods: In this case-control study, fecal samples were obtained from maternal rectum in the operation room by an obstetrician under strict aseptic conditions. We compared gut microbiota of 14 pregnant women with FGR and 18 normal controls by performing 16S rDNA amplicon sequencing.Results: We identified significant differences in β-diversity between the FGR and control groups (P < 0.05). At genus level,Bacteroides,FaecalibacteriumandLachnospirawere highly abundant in the FGR subjects, which are significantly enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to glycometabolism.Conclusion: These findings demonstrated that the distinct composition of the gut microbiota between FGR and normal pregnant women could contribute to an improved understanding of the prevention and treatment of FGR.

Aristolochic Acid (AA), a natural component of Aristolochia plants that is found in a variety of herbal remedies and health supplements, is classified as a Group 1 carcinogen by the International Agency for Research on Cancer. Given that microRNAs (miRNAs) are involved in cancer initiation and progression and their role remains unknown in AA-induced carcinogenesis, we examined genome-wide AA-induced dysregulation of miRNAs as well as the regulation of miRNAs on their target gene expression in rat kidney. We treated rats with 10 mg/kg AA and vehicle control for 12 weeks and eight kidney samples (4 for the treatment and 4 for the control) were used for examining miRNA and mRNA expression by deep sequencing, and protein expression by proteomics. AA treatment resulted in significant differential expression of miRNAs, mRNAs and proteins as measured by both principal component analysis (PCA) and hierarchical clustering analysis (HCA). Specially, 63 miRNAs (adjusted p value < 0.05 and fold change > 1.5), 6,794 mRNAs (adjusted p value < 0.05 and fold change > 2.0), and 800 proteins (fold change > 2.0) were significantly altered by AA treatment. The expression of 6 selected miRNAs was validated by quantitative real-time PCR analysis. Ingenuity Pathways Analysis (IPA) showed that cancer is the top network and disease associated with those dysregulated miRNAs. To further investigate the influence of miRNAs on kidney mRNA and protein expression, we combined proteomic and transcriptomic data in conjunction with miRNA target selection as confirmed and reported in miRTarBase. In addition to translational repression and transcriptional destabilization, we also found that miRNAs and their target genes were expressed in the same direction at levels of transcription (169) or translation (227). Furthermore, we identified that up-regulation of 13 oncogenic miRNAs was associated with translational activation of 45 out of 54 cancer-related targets. Our findings suggest that dysregulated miRNA expression plays an important role in AA-induced carcinogenesis in rat kidney, and that the integrated approach of multiple profiling provides a new insight into a post-transcriptional regulation of miRNAs on their target repression and activation in a genome-wide scale.