An expanded role for microbial physiology in metabolic engineering and functional genomics: moving towards systems biology

Microbial physiology has traditionally played a very important role in both fundamental research and in industrial applications of microorganisms. The classical approach in microbial physiology has been to analyze the role of individual components (genes or proteins) in the overall cell function. With the progress in molecular biology it has become possible to optimize industrial fermentations through introduction of directed genetic modification - an approach referred to as metabolic engineering. Furthermore, as a consequence of large sequencing programs the complete genomic sequence has become available for an increasing number of microorganisms. This has resulted in substantial research efforts in assigning function to all identified open reading frames - referred to as functional genomics. In both metabolic engineering and functional genomics there is a trend towards application of a macroscopic view on cell function, and this leads to an expanded role of the classical approach applied in microbial physiology. With the increased understanding of the molecular mechanisms it is envisaged that in the future it will be possible to describe the interaction between all the components in the system (the cell), also at the quantitative level, and this is the goal of systems biology. Clearly this will have a significant impact on microbial physiology as well as on metabolic engineering.     

An integrative functional genomics approach for discovering biomarkers in schizophrenia

Schizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders.     

An optically stabilized fast-switching light emitting diode as a light source for functional neuroimaging

Neuroscience research increasingly relies on optical methods for evoking neuronal activity as well as for measuring it, making bright and stable light sources critical building blocks of modern experimental setups. This paper presents a method to control the brightness of a high-power light emitting diode (LED) light source to an unprecedented level of stability. By continuously monitoring the actual light output of the LED with a photodiode and feeding the result back to the LED's driver by way of a proportional-integral controller, drift was reduced to as little as 0.007% per hour over a 12-h period, and short-term fluctuations to 0.005% root-mean-square over 10 seconds. The LED can be switched on and off completely within 100 μs, a feature that is crucial when visual stimuli and light for optical recording need to be interleaved to obtain artifact-free recordings. The utility of the system is demonstrated by recording visual responses in the central nervous system of the medicinal leech Hirudo verbana using voltage-sensitive dyes.     

Structure of the mammalian mitochondrial ribosome reveals an expanded functional role for its component proteins

The mitochondrial ribosome is responsible for the biosynthesis of protein components crucial to the generation of ATP in the eukaryotic cell. Because the protein:RNA ratio in the mitochondrial ribosome (approximately 69:approximately 31) is the inverse of that of its prokaryotic counterpart (approximately 33:approximately 67), it was thought that the additional and/or larger proteins of the mitochondrial ribosome must compensate for the shortened rRNAs. Here, we present a three-dimensional cryo-electron microscopic map of the mammalian mitochondrial 55S ribosome carrying a tRNA at its P site, and we find that instead, many of the proteins occupy new positions in the ribosome. Furthermore, unlike cytoplasmic ribosomes, the mitochondrial ribosome possesses intersubunit bridges composed largely of proteins; it has a gatelike structure at its mRNA entrance, perhaps involved in recruiting unique mitochondrial mRNAs; and it has a polypeptide exit tunnel that allows access to the solvent before the exit site, suggesting a unique nascent-polypeptide exit mechanism.     

Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging

The etiology of schizophrenia likely involves genetic interactions. DISC1, a promising candidate susceptibility gene, encodes a protein which interacts with many other proteins, including CIT, NDEL1, NDE1, FEZ1 and PAFAH1B1, some of which also have been associated with psychosis. We tested for epistasis between these genes in a schizophrenia case–control study using machine learning algorithms (MLAs: random forest, generalized boosted regression and Monte Carlo logic regression). Convergence of MLAs revealed a subset of seven SNPs that were subjected to 2-SNP interaction modeling using likelihood ratio tests for nested unconditional logistic regression models. Of the ⁷C₂ = 21 interactions, four were significant at the α = 0.05 level: DISC1 rs1411771–CIT rs10744743 OR = 3.07 (1.37, 6.98) p = 0.007; CIT rs3847960–CIT rs203332 OR = 2.90 (1.45, 5.79) p = 0.003; CIT rs3847960–CIT rs440299 OR = 2.16 (1.04, 4.46) p = 0.038; one survived Bonferroni correction (NDEL1 rs4791707–CIT rs10744743 OR = 4.44 (2.22, 8.88) p = 0.00013). Three of four interactions were validated via functional magnetic resonance imaging (fMRI) in an independent sample of healthy controls; risk associated alleles at both SNPs predicted prefrontal cortical inefficiency during the N-back task, a schizophrenia-linked intermediate biological phenotype: rs3847960–rs440299; rs1411771–rs10744743, rs4791707–rs10744743 (SPM5 p < 0.05, corrected), although we were unable to statistically replicate the interactions in other clinical samples. Interestingly, the CIT SNPs are proximal to exons that encode the DISC1 interaction domain. In addition, the 3′ UTR DISC1 rs1411771 is predicted to be an exonic splicing enhancer and the NDEL1 SNP is ~3,000 bp from the exon encoding the region of NDEL1 that interacts with the DISC1 protein, giving a plausible biological basis for epistasis signals validated by fMRI.     

A cerebrovascular response model for functional neuroimaging including dynamic cerebral autoregulation

Functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) can be used to isolate an evoked response to a stimulus from significant background physiological fluctuations. Data analysis approaches typically use averaging or linear regression to remove this physiological baseline with varying degrees of success. Biophysical model-based analysis of the functional hemodynamic response has also been advanced previously with the Balloon and Windkessel models. In the present work, a biophysical model of systemic and cerebral circulation and gas exchange is applied to resting state NIRS neuroimaging data from 10 human subjects. The model further includes dynamic cerebral autoregulation, which modulates the cerebral arteriole compliance to control cerebral blood flow. This biophysical model allows for prediction, from noninvasive blood pressure measurements, of the background hemodynamic fluctuations in the systemic and cerebral circulations. Significantly higher correlations with the NIRS data were found using the biophysical model predictions compared to blood pressure regression and compared to transfer function analysis (multifactor ANOVA, p < 0.0001). This finding supports the further development and use of biophysical models for removing baseline activity in functional neuroimaging analysis. Future extensions of this work could model changes in cerebrovascular physiology that occur during development, aging, and disease.     

SgRNA engineering for improved genome editing and expanded functional assays

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Large-scale automated synthesis of human functional neuroimaging data

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.     

An expanded auxin-inducible degron toolkit for Caenorhabditis elegans

The auxin-inducible degron (AID) system has emerged as a powerful tool to conditionally deplete proteins in a range of organisms and cell types. Here, we describe a toolkit to augment the use of the AID system in Caenorhabditis elegans. We have generated a set of single-copy, tissue-specific (germline, intestine, neuron, muscle, pharynx, hypodermis, seam cell, anchor cell) and pan-somatic TIR1-expressing strains carrying a co-expressed blue fluorescent reporter to enable use of both red and green channels in experiments. These transgenes are inserted into commonly used, well-characterized genetic loci. We confirmed that our TIR1-expressing strains produce the expected depletion phenotype for several nuclear and cytoplasmic AID-tagged endogenous substrates. We have also constructed a set of plasmids for constructing repair templates to generate fluorescent protein::AID fusions through CRISPR/Cas9-mediated genome editing. These plasmids are compatible with commonly used genome editing approaches in the C. elegans community (Gibson or SapTrap assembly of plasmid repair templates or PCR-derived linear repair templates). Together these reagents will complement existing TIR1 strains and facilitate rapid and high-throughput fluorescent protein::AID tagging of genes. This battery of new TIR1-expressing strains and modular, efficient cloning vectors serves as a platform for straightforward assembly of CRISPR/Cas9 repair templates for conditional protein depletion.     

An expanded modern coexistence theory for empirical applications

Understanding long‐term coexistence of numerous competing species is a longstanding challenge in ecology. Progress requires determining which processes and species differences are most important for coexistence when multiple processes operate and species differ in many ways. Modern coexistence theory (MCT), formalised by Chesson, holds out the promise of doing that, but empirical applications remain scarce. We argue that MCT's mathematical complexity and subtlety have obscured the simplicity and power of its underlying ideas and hindered applications. We present a general computational approach that extends our previous solution for the storage effect to all of standard MCT's spatial and temporal coexistence mechanisms, and also process‐defined mechanisms amenable to direct study such as resource partitioning, indirect competition, and life history trade‐offs. The main components are a method to partition population growth rates into contributions from different mechanisms and their interactions, and numerical calculations in which some mechanisms are removed and others retained. We illustrate how our approach handles features that have not been analysed in the standard framework through several case studies: competing diatom species under fluctuating temperature, plant–soil feedbacks in grasslands, facilitation in a beach grass community, and niche differences with independent effects on recruitment, survival and growth in sagebrush steppe.