The Landscape of Genetic Content in the Gut and Oral Human Microbiome.

Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were "singletons," or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio.

Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that 'learns' molecular models from training data, opening the possibility of 'machine learning' in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

Clustered complementary amino acid pairing (CCAAP) for protein-protein interaction.

OBJECTIVES: Designing a polypeptide sequence to interact with a preselected target polypeptide sequence of a protein has long been of interest, yet remains an elusive goal. RESULTS: Here, we propose a novel concept named "Clustered Complementary Amino Acid Pairing (CCAAP)," which plays an essential role in protein-protein interaction (PPI). Complementary amino acid pairing (CAAP) is a pairing between two amino acids encoded by a codon and its reverse complementary codon. CAAP interactions largely agree with the physicochemical and stereochemical requirements for probable amino acid pairings. Interestingly, 82 PPI structure data revealed that clusters of CAAP interactions (CCAAP boxes) are predominantly found in all PPI sites. Analysis of all amino acid pairings in the CCAAP boxes unveiled amino acid-pairing preferences and patterns for PPI that allowed us to develop a new method for designing an oligopeptide sequence to bind to a chosen polypeptide sequence of any target protein. CONCLUSIONS: Discoveries in the present study provide proof of the CCAAP principle.

USP11 Enhances TGFß-Induced Epithelial-Mesenchymal Plasticity and Human Breast Cancer Metastasis.

Epithelial-mesenchymal transition (EMT) is a conserved cellular plasticity program that is reactivated in carcinoma cells and drives metastasis. Although EMT is well studied its regulatory mechanisms remain unclear. Therefore, to identify novel regulators of EMT, a data mining approach was taken using published microarray data and a group of deubiquitinases (DUB) were found to be upregulated in cells that have undergone EMT. Here, it is demonstrated that one DUB, ubiquitin-specific peptidase 11 (USP11), enhances TGFß-induced EMT and self-renewal in immortalized human mammary epithelial cells. Furthermore, modulating USP11 expression in human breast cancer cells altered the migratory capacity in vitro and metastasis in vivo Moreover, elevated USP11 expression in human breast cancer patient clinical specimens correlated with decreased survival. Mechanistically, modulating USP11 expression altered the stability of TGFß receptor type II (TGFBR2) and TGFß downstream signaling in human breast cancer cells. Together, these data suggest that deubiquitination of TGFBR2 by USP11 effectively spares TGFBR2 from proteasomal degradation to promote EMT and metastasis.Implications: USP11 regulates TGFß-induced epithelial-mesenchymal plasticity and human breast cancer metastasis and may be a potential therapeutic target for breast cancer. Mol Cancer Res; 16(7); 1172-84. ©2018 AACR.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules.

Voltage-dependent regulation of CaV2.2 channels by Gq-coupled receptor is facilitated by membrane-localized ß subunit.

G protein-coupled receptors (GPCRs) signal through molecular messengers, such as Gßγ, Ca(2+), and phosphatidylinositol 4,5-bisphosphate (PIP2), to modulate N-type voltage-gated Ca(2+) (CaV2.2) channels, playing a crucial role in regulating synaptic transmission. However, the cellular pathways through which GqPCRs inhibit CaV2.2 channel current are not completely understood. Here, we report that the location of CaV ß subunits is key to determining the voltage dependence of CaV2.2 channel modulation by GqPCRs. Application of the muscarinic agonist oxotremorine-M to tsA-201 cells expressing M1 receptors, together with CaV N-type α1B, α2δ1, and membrane-localized ß2a subunits, shifted the current-voltage relationship for CaV2.2 activation 5 mV to the right and slowed current activation. Muscarinic suppression of CaV2.2 activity was relieved by strong depolarizing prepulses. Moreover, when the C terminus of ß-adrenergic receptor kinase (which binds Gßγ) was coexpressed with N-type channels, inhibition of CaV2.2 current after M1 receptor activation was markedly reduced and delayed, whereas the delay between PIP2 hydrolysis and inhibition of CaV2.2 current was decreased. When the Gßγ-insensitive CaV2.2 α1C-1B chimera was expressed, voltage-dependent inhibition of calcium current was virtually abolished, suggesting that M1 receptors act through Gßγ to inhibit CaV2.2 channels bearing membrane-localized CaV ß2a subunits. Expression of cytosolic ß subunits such as ß2b and ß3, as well as the palmitoylation-negative mutant ß2a(C3,4S), reduced the voltage dependence of M1 muscarinic inhibition of CaV2.2 channels, whereas it increased inhibition mediated by PIP2 depletion. Together, our results indicate that, with membrane-localized CaV ß subunits, CaV2.2 channels are subject to Gßγ-mediated voltage-dependent inhibition, whereas cytosol-localized ß subunits confer more effective PIP2-mediated voltage-independent regulation. Thus, the voltage dependence of GqPCR regulation of calcium channels can be determined by the location of isotype-specific CaV ß subunits.

High polybrominated diphenyl ether levels in California house cats: house dust a primary source?

Polybrominated diphenyl ethers (PBDEs) are brominated flame retardants that act as endocrine disruptors, affecting thyroid hormone homeostasis. As a follow-up to a recent study showing high PBDE levels in household cats and linking PBDE levels with cat hyperthyroidism, we measured PBDEs, polychlorinated biphenyls (PCBs), and organochlorinated pesticides (OCPs) in serum samples from 26 California household cats (16 hyperthyroid, 10 controls) using liquid-liquid extraction and high-resolution gas chromatography/high-resolution mass spectrometry. In the present pilot study, we found that PBDE levels in California house cats were extremely high (ΣPBDEs median = 2,904 ng/g lipid; range, 631-22,537 ng/g lipid). This is approximately 50 times higher than levels in California residents (ΣPBDEs geomean = 62 ± 8.9 ng/g lipid, National Health and Nutrition Examination Survey), who have among the highest human levels in the world. Polybrominated diphenyl ethers congener patterns (BDE-99 major congener, BDE-209 significant) differed markedly from patterns found in California residents (BDE-47 major) or wildlife but resembled patterns found in house dust. Polychlorinated biphenyls and OCPs in cats were highly correlated, consistent with a shared dietary source or pathway of exposure, but did not correlate with PBDEs. This suggests a different source or pathway of exposure for PBDEs, which was most likely house dust. The authors found no evidence that linked levels of PBDEs, PCBs, or OCPs with hyperthyroidism. This may be because of the small sample size, competing or confounding risk factors, or complicated causal mechanisms.