Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues.

Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by 'brute force' surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or high-fat/high-sucrose (HFHS) diet. Variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver PCSK9) and genes encoding enzymes producing metabolites (adipose PNPLA2), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as gene-derived correlations across tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs.

Sense of purpose interventions for depression and anxiety in youth: A scoping review and cross-cultural youth consultation.

BACKGROUND/OBJECTIVES: To investigate the effectiveness, feasibility, and acceptability of sense of purpose (SOP) interventions in preventing or reducing anxiety or depression in youth aged 14-24 years. METHODS: A systematic search was conducted of the academic (PubMed/MEDLINE, PsycINFO, EMBASE) and grey literature. We also consulted two SOP experts and an Australian and Indian youth advisory group with lived experience of anxiety and/or depression. Consultations focused on the feasibility and acceptability of reviewed interventions. RESULTS: The search identified 25 studies reporting on 4408 participants from six countries (64.0 % of studies in the US). Multi-component interventions targeting several SOP components (i.e., value clarification, goal setting, gratitude enhancement) reported, on average, moderate reductions in depression and anxiety symptoms in youth. Interventions were generally more effective at reducing depression than anxiety symptoms. In terms of sub-populations or groups, there was some evidence for greater intervention effectiveness among youth with prior therapy experience, extraverted personalities, and those with already elevated anxiety/depression symptoms. Youth advisors and experts opined that group interventions were most acceptable to young people. LIMITATIONS: This review was limited to a recent 10-year timeframe and publications in English, potentially excluding relevant studies published prior to 2011 or in other languages. CONCLUSIONS: Fostering SOP can lead to better psychological wellbeing in youth. Potential harms resulting from interventions can occur without adequate consideration for a person's readiness for purpose discovery, environmental barriers, and familial and cultural settings. Further research in more diverse populations is required to determine who benefits and in what contexts.

Inference and analysis of cell-cell communication using CellChat.

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.

Unbiased analysis of peripheral blood mononuclear cells reveals CD4 T cell response to RSV matrix protein.

Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000-126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children. Flow cytometry analysis of cellular responses using "conventional" anti-viral markers (IL2, IFN-γ, TNF, IL10 and IL4) upon RSV-peptide stimulation detected an overall low RSV response in peripheral blood. Therefore we sought an unbiased approach to identify RSV-specific immune markers using RNA-sequencing upon stimulation of infant PBMCs with overlapping peptides representing RSV antigens. To understand the cellular response using transcriptional signatures, transcription factors and cell-type specific signatures were used to investigate breadth of response across peptides. Unexpected from the ICS data, M peptide induced a response equivalent to the F-peptide and was characterized by activation of GATA2, 3, STAT3 and IRF1. This along with upregulation of several unconventional T cell signatures was only observed upon M-peptide stimulation. Moreover, signatures of natural RSV infections were identified from the data available in the public domain to investigate similarities between transcriptional signatures from PBMCs and upon peptide stimulation. This analysis also suggested activation of T cell response upon M-peptide stimulation. Hence, based on transcriptional response, markers were chosen to validate the role of M-peptide in activation of T cells. Indeed, CD4+CXCL9+ cells were identified upon M-peptide stimulation by flow cytometry. Future work using additional markers identified in this study could reveal additional unconventional T cells responding to RSV infections in infants. In conclusion, T cell responses to RSV in infants may not follow the canonical Th1/Th2 patterns of effector responses but include additional functions that may be unique to the neonatal period and correlate with clinical outcomes.

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.

High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally-optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Local features determine Ty3 targeting frequency at RNA polymerase III transcription start sites.

Retroelement integration into host genomes affects chromosome structure and function. A goal of a considerable number of investigations is to elucidate features influencing insertion site selection. The Saccharomyces cerevisiae Ty3 retrotransposon inserts proximal to the transcription start sites (TSS) of genes transcribed by RNA polymerase III (RNAP3). In this study, differential patterns of insertion were profiled genome-wide using a random barcode-tagged Ty3. Saturation transposition showed that tRNA genes (tDNAs) are targeted at widely different frequencies even within isoacceptor families. Ectopic expression of Ty3 integrase (IN) showed that it localized to targets independent of other Ty3 proteins and cDNA. IN, RNAP3, and transcription factor Brf1 were enriched at tDNA targets with high frequencies of transposition. To examine potential effects of cis-acting DNA features on transposition, targeting was tested on high-copy plasmids with restricted amounts of 5' flanking sequence plus tDNA. Relative activity of targets was reconstituted in these constructions. Weighting of genomic insertions according to frequency identified an A/T-rich sequence followed by C as the dominant site of strand transfer. This site lies immediately adjacent to the adenines previously implicated in the RNAP3 TSS motif (CAA). In silico DNA structural analysis upstream of this motif showed that targets with elevated DNA curvature coincide with reduced integration. We propose that integration mediated by the Ty3 intasome complex (IN and cDNA) is subject to inputs from a combination of host factor occupancy and insertion site architecture, and that this results in the wide range of Ty3 targeting frequencies.

Nanopatterned silk fibroin films with high transparency and high haze for optical applications.

Simultaneous high transparency and high haze are necessary for high-efficiency optical, photonic, and optoelectronic applications. However, a typical highly transparent film lacks high optical haze or vice versa. Here, we report a silk fibroin-based optical film that exhibits both ultrahigh optical transparency (>93%) and ultrahigh optical transmission haze (>65%). Also, in combination with the soft lithography method, different nanostructured silk fibroin films are presented and their optical properties are characterized as well. To demonstrate its exceptional performance in both high transmission and high optical haze, we combine the silk fibroin with the silicon photodiode and show that the efficiency can be increased by 6.96% with the silk fibroin film without patterns and 14.9% with the nanopatterned silk fibroin film. Silk provides excellent mechanical, optical, and electrical properties, and the reported high-performance silk fibroin can enable the development of next-generation biocompatible eco-friendly flexible electronic and optical devices.

Functional genomics for the oleaginous yeast Yarrowia lipolytica.

Oleaginous yeasts are valuable systems for biosustainable production of hydrocarbon-based chemicals. Yarrowia lipolytica is one of the best characterized of these yeast with respect to genome annotation and flux analysis of metabolic processes. Nonetheless, progress is hampered by a dearth of genome-wide tools enabling functional genomics. In order to remedy this deficiency, we developed a library of Y. lipolytica insertion mutants via transposon mutagenesis. The Hermes DNA transposon was expressed to achieve saturation mutagenesis of the genome. Over 534,000 independent insertions were identified by next-generation sequencing. Poisson analysis of insertion density classified ~ 22% of genes as essential. As expected, most essential genes have homologs in Saccharomyces cerevisiae and Schizosaccharomyces pombe, and the majority of those are also essential. As an obligate aerobe, Y. lipolytica has significantly more respiration - related genes that are classified as essential than do S. cerevisiae and S. pombe. Contributions of non-essential genes to growth in glucose and glycerol carbon sources were assessed and used to evaluate two recent genome-scale models of Y. lipolytica metabolism. Fluorescence-activated cell sorting identified mutants in which lipid accumulation is increased. Our findings provide insights into biosynthetic pathways, compartmentalization of enzymes, and distinct functions of paralogs. This functional genomic analysis of the oleaginous yeast Y. lipolytica provides an important resource for modeling, bioengineering, and design of synthetic minimalized strains of respiratory yeasts.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

Cell type discovery and representation in the era of high-content single cell phenotyping.

BACKGROUND: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. RESULTS: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. CONCLUSION: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

Sequence Assembly of Yarrowia lipolytica Strain W29/CLIB89 Shows Transposable Element Diversity.

Yarrowia lipolytica, an oleaginous yeast, is capable of accumulating significant cellular mass in lipid making it an important source of biosustainable hydrocarbon-based chemicals. In spite of a similar number of protein-coding genes to that in other Hemiascomycetes, the Y. lipolytica genome is almost double that of model yeasts. Despite its economic importance and several distinct strains in common use, an independent genome assembly exists for only one strain. We report here a de novo annotated assembly of the chromosomal genome of an industrially-relevant strain, W29/CLIB89, determined by hybrid next-generation sequencing. For the first time, each Y. lipolytica chromosome is represented by a single contig. The telomeric rDNA repeats were localized by Irys long-range genome mapping and one complete copy of the rDNA sequence is reported. Two large structural variants and retroelement differences with reference strain CLIB122 including a full-length, novel Ty3/Gypsy long terminal repeat (LTR) retrotransposon and multiple LTR-like sequences are described. Strikingly, several of these are adjacent to RNA polymerase III-transcribed genes, which are almost double in number in Y. lipolytica compared to other Hemiascomycetes. In addition to previously-reported dimeric RNA polymerase III-transcribed genes, tRNA pseudogenes were identified. Multiple full-length and truncated LINE elements are also present. Therefore, although identified transposons do not constitute a significant fraction of the Y. lipolytica genome, they could have played an active role in its evolution. Differences between the sequence of this strain and of the existing reference strain underscore the utility of an additional independent genome assembly for this economically important organism.

Ty3 Retrotransposon Hijacks Mating Yeast RNA Processing Bodies to Infect New Genomes.

Retrotransposition of the budding yeast long terminal repeat retrotransposon Ty3 is activated during mating. In this study, proteins that associate with Ty3 Gag3 capsid protein during virus-like particle (VLP) assembly were identified by mass spectrometry and screened for roles in mating-stimulated retrotransposition. Components of RNA processing bodies including DEAD box helicases Dhh1/DDX6 and Ded1/DDX3, Sm-like protein Lsm1, decapping protein Dcp2, and 5' to 3' exonuclease Xrn1 were among the proteins identified. These proteins associated with Ty3 proteins and RNA, and were required for formation of Ty3 VLP retrosome assembly factories and for retrotransposition. Specifically, Dhh1/DDX6 was required for normal levels of Ty3 genomic RNA, and Lsm1 and Xrn1 were required for association of Ty3 protein and RNA into retrosomes. This role for components of RNA processing bodies in promoting VLP assembly and retrotransposition during mating in a yeast that lacks RNA interference, contrasts with roles proposed for orthologous components in animal germ cell ribonucleoprotein granules in turnover and epigenetic suppression of retrotransposon RNAs.

A unifying kinetic framework for modeling oxidoreductase-catalyzed reactions.

MOTIVATION: Oxidoreductases are a fundamental class of enzymes responsible for the catalysis of oxidation-reduction reactions, crucial in most bioenergetic metabolic pathways. From their common root in the ancient prebiotic environment, oxidoreductases have evolved into diverse and elaborate protein structures with specific kinetic properties and mechanisms adapted to their individual functional roles and environmental conditions. While accurate kinetic modeling of oxidoreductases is thus important, current models suffer from limitations to the steady-state domain, lack empirical validation or are too specialized to a single system or set of conditions. RESULTS: To address these limitations, we introduce a novel unifying modeling framework for kinetic descriptions of oxidoreductases. The framework is based on a set of seven elementary reactions that (i) form the basis for 69 pairs of enzyme state transitions for encoding various specific microscopic intra-enzyme reaction networks (micro-models), and (ii) lead to various specific macroscopic steady-state kinetic equations (macro-models) via thermodynamic assumptions. Thus, a synergistic bridge between the micro and macro kinetics can be achieved, enabling us to extract unitary rate constants, simulate reaction variance and validate the micro-models using steady-state empirical data. To help facilitate the application of this framework, we make available RedoxMech: a Mathematica™ software package that automates the generation and customization of micro-models. AVAILABILITY: The Mathematica™ source code for RedoxMech, the documentation and the experimental datasets are all available from: http://www.igb.uci.edu/tools/sb/metabolic-modeling. CONTACT: pfbaldi@ics.uci.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling of mitochondria bioenergetics using a composable chemiosmotic energy transduction rate law: theory and experimental validation.

Mitochondrial bioenergetic processes are central to the production of cellular energy, and a decrease in the expression or activity of enzyme complexes responsible for these processes can result in energetic deficit that correlates with many metabolic diseases and aging. Unfortunately, existing computational models of mitochondrial bioenergetics either lack relevant kinetic descriptions of the enzyme complexes, or incorporate mechanisms too specific to a particular mitochondrial system and are thus incapable of capturing the heterogeneity associated with these complexes across different systems and system states. Here we introduce a new composable rate equation, the chemiosmotic rate law, that expresses the flux of a prototypical energy transduction complex as a function of: the saturation kinetics of the electron donor and acceptor substrates; the redox transfer potential between the complex and the substrates; and the steady-state thermodynamic force-to-flux relationship of the overall electro-chemical reaction. Modeling of bioenergetics with this rate law has several advantages: (1) it minimizes the use of arbitrary free parameters while featuring biochemically relevant parameters that can be obtained through progress curves of common enzyme kinetics protocols; (2) it is modular and can adapt to various enzyme complex arrangements for both in vivo and in vitro systems via transformation of its rate and equilibrium constants; (3) it provides a clear association between the sensitivity of the parameters of the individual complexes and the sensitivity of the system's steady-state. To validate our approach, we conduct in vitro measurements of ETC complex I, III, and IV activities using rat heart homogenates, and construct an estimation procedure for the parameter values directly from these measurements. In addition, we show the theoretical connections of our approach to the existing models, and compare the predictive accuracy of the rate law with our experimentally fitted parameters to those of existing models. Finally, we present a complete perturbation study of these parameters to reveal how they can significantly and differentially influence global flux and operational thresholds, suggesting that this modeling approach could help enable the comparative analysis of mitochondria from different systems and pathological states. The procedures and results are available in Mathematica notebooks at http://www.igb.uci.edu/tools/sb/mitochondria-modeling.html.

Mutation rate switch inside Eurasian mitochondrial haplogroups: impact of selection and consequences for dating settlement in Europe.

R-lineage mitochondrial DNA represents over 90% of the European population and is significantly present all around the planet (North Africa, Asia, Oceania, and America). This lineage played a major role in migration "out of Africa" and colonization in Europe. In order to determine an accurate dating of the R lineage and its sublineages, we analyzed 1173 individuals and complete mtDNA sequences from Mitomap. This analysis revealed a new coalescence age for R at 54.500 years, as well as several limitations of standard dating methods, likely to lead to false interpretations. These findings highlight the association of a striking under-accumulation of synonymous mutations, an over-accumulation of non-synonymous mutations, and the phenotypic effect on haplogroup J. Consequently, haplogroup J is apparently not a Neolithic group but an older haplogroup (Paleolithic) that was subjected to an underestimated selective force. These findings also indicated an under-accumulation of synonymous and non-synonymous mutations localized on coding and non-coding (HVS1) sequences for haplogroup R0, which contains the major haplogroups H and V. These new dates are likely to impact the present colonization model for Europe and confirm the late glacial resettlement scenario.

A scalable and integrative system for pathway bioinformatics and systems biology.

MOTIVATION: Progress in systems biology depends on developing scalable informatics tools to predictively model, visualize, and flexibly store information about complex biological systems. Scalability of these tools, as well as their ability to integrate within larger frameworks of evolving tools, is critical to address the multi-scale and size complexity of biological systems. RESULTS: Using current software technology, such as self-generation of database and object code from UML schemas, facilitates rapid updating of a scalable expert assistance system for modeling biological pathways. Distribution of key components along with connectivity to external data sources and analysis tools is achieved via a web service interface. AVAILABILITY: All sigmoid modeling software components and supplementary information are available through: http://www.igb.uci.edu/servers/sb.html.

A three-dimensional, stochastic simulation of biofilm growth and transport-related factors that affect structure.

Biofilm structural heterogeneity affects a broad range of microbially catalysed processes. Solute transport limitation and autoinhibitor production, two factors that contribute to heterogeneous biofilm development, were investigated using BacMIST, a computer simulation model. BacMIST combines a cellular automaton algorithm for biofilm growth with Brownian diffusion for solute transport. The simulation represented the growth of microbial unit cells in a three-dimensional domain modelled after a repeating section of a constant depth film fermenter. The simulation was implemented to analyse the effects of various levels of transport limitation on a growing single-species biofilm. In a system with rapid solute diffusion, cells throughout the biofilm grew at their maximum rate, and no solute gradient was formed over the biofilm thickness. In increasingly transport-limited systems, the rapidly growing fraction of the biofilm population decreased, and was found exclusively at the biofilm-liquid interface. Trans-biofilm growth substrate gradients also deepened with increasing transport limitation. Autoinhibitory biofilm growth was simulated for various rates of microbially produced inhibitor transport. Inhibitor transport rates affected both the biofilm population dynamics and the resulting biofilm structures. The formation of networks of void spaces in slow-growing regions of the biofilm and the development of columns in the fast-growing regions suggested a possible mechanism for the microscopically observed evolution of channels in biofilms.