Molecular characterisation of protist parasites in human-habituated mountain gorillas (Gorilla beringei beringei), humans and livestock, from Bwindi impenetrable National Park, Uganda

Over 60 % of human emerging infectious diseases are zoonotic, and there is growing evidence of the zooanthroponotic transmission of diseases from humans to livestock and wildlife species, with major implications for public health, economics, and conservation. Zooanthroponoses are of relevance to critically endangered species; amongst these is the mountain gorilla (Gorilla beringei beringei) of Uganda. Here, we assess the occurrence of Cryptosporidium, Cyclospora, Giardia, and Entamoeba infecting mountain gorillas in the Bwindi Impenetrable National Park (BINP), Uganda, using molecular methods. We also assess the occurrence of these parasites in humans and livestock species living in overlapping/adjacent geographical regions

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

How predictive is the MMSE for cognitive performance after stroke?

Cognitive deficits are commonly observed in stroke patients. Neuropsychological testing is time-consuming and not easy to administer after hospital discharge. Standardised screening measures are desirable. The Mini-Mental State Examination (MMSE) is the test most widely applied to screen for cognitive deficits. Despite its broad use, its predictive characteristics after stroke have not been exhaustively investigated. The aim of this study was to determine whether the MMSE is able to adequately screen for cognitive impairment and dementia after stroke and whether or not the MMSE can predict further deterioration or recovery in cognitive function over time. To this end, we studied 194 first-ever stroke patients without pre-stroke cognitive deterioration who underwent MMSEs and neuropsychological test batteries at 1, 6, 12, and 24 months after stroke. The MMSE score 1 month after stroke predicted cognitive functioning at later follow-up visits. It could not predict deterioration or improvement in cognitive functioning over time. The cut-off score in the screening for 1 cognitive disturbed domain was 27/28 with a sensitivity of 0.72. The cut-off score in the screening for at least 4 impaired domains and dementia were 26/27 and 23/24 with a sensitivity of 0.82 and 0.96, respectively. The results indicated that the MMSE has modest qualities in screening for mild cognitive disturbances and is adequate in screening for moderate cognitive deficits or dementia in stroke patients 1 month after stroke. Poor performance on the MMSE is predictive for cognitive impairment in the long term. However, it cannot be used to predict further cognitive deterioration or improvement over time

Molecular decoding using luminescence from an entangled porous framework

Chemosensors detect a single target molecule from among several molecules, but cannot differentiate targets from one another. In this study, we report a molecular decoding strategy in which a single host domain accommodates a class of molecules and distinguishes between them with a corresponding readout. We synthesized the decoding host by embedding naphthalenediimide into the scaffold of an entangled porous framework that exhibited structural dynamics due to the dislocation of two chemically non-interconnected frameworks. An intense turn-on emission was observed on incorporation of a class of aromatic compounds, and the resulting luminescent colour was dependent on the chemical substituent of the aromatic guest. This unprecedented chemoresponsive, multicolour luminescence originates from an enhanced naphthalenediimide–aromatic guest interaction because of the induced-fit structural transformation of the entangled framework. We demonstrate that the cooperative structural transition in mesoscopic crystal domains results in a nonlinear sensor response to the guest concentration

Genome Wide Expression Profiling Reveals Suppression of Host Defence Responses during Colonisation by Neisseria meningitides but not N. lactamica

Both Neisseria meningitidis and the closely related bacterium Neisseria lactamica colonise human nasopharyngeal mucosal surface, but only N. meningitidis invades the bloodstream to cause potentially life-threatening meningitis and septicaemia. We have hypothesised that the two neisserial species differentially modulate host respiratory epithelial cell gene expression reflecting their disease potential. Confluent monolayers of 16HBE14 human bronchial epithelial cells were exposed to live and/or dead N. meningitidis (including capsule and pili mutants) and N. lactamica, and their transcriptomes were compared using whole genome microarrays. Changes in expression of selected genes were subsequently validated using Q-RT-PCR and ELISAs. Live N. meningitidis and N. lactamica induced genes involved in host energy production processes suggesting that both bacterial species utilise host resources. N. meningitidis infection was associated with down-regulation of host defence genes. N. lactamica, relative to N. meningitidis, initiates up-regulation of proinflammatory genes. Bacterial secreted proteins alone induced some of the changes observed. The results suggest N. meningitidis and N. lactamica differentially regulate host respiratory epithelial cell gene expression through colonisation and/or protein secretion, and that this may contribute to subsequent clinical outcomes associated with these bacteria

Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology

Yeast is an ideal organism for the development and application of synthetic biology, yet there remain relatively few well-characterised biological parts suitable for precise engineering of this chassis. In order to address this current need, we present here a strategy that takes a single biological part, a promoter, and re-engineers it to produce a fine-graded output range promoter library and new regulated promoters desirable for orthogonal synthetic biology applications. A highly constitutive Saccharomyces cerevisiae promoter, PFY1p, was identified by bioinformatic approaches, characterised in vivo and diversified at its core sequence to create a 36-member promoter library. TetR regulation was introduced into PFY1p to create a synthetic inducible promoter (iPFY1p) that functions in an inverter device. Orthogonal and scalable regulation of synthetic promoters was then demonstrated for the first time using customisable Transcription Activator-Like Effectors (TALEs) modified and designed to act as orthogonal repressors for specific PFY1-based promoters. The ability to diversify a promoter at its core sequences and then independently target Transcription Activator-Like Orthogonal Repressors (TALORs) to virtually any of these sequences shows great promise toward the design and construction of future synthetic gene networks that encode complex “multi-wire” logic functions

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings

Rise and Demise of Bioinformatics? Promise and Progress

The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive changes in science and technology. Despite the apparent fatigue of the linguistic use of the term itself, bioinformatics has grown perhaps to a point beyond recognition. We explore both historical aspects and future trends and argue that as the field expands, key questions remain unanswered and acquire new meaning while at the same time the range of applications is widening to cover an ever increasing number of biological disciplines. These trends appear to be pointing to a redefinition of certain objectives, milestones, and possibly the field itself

The Implications of Relationships between Human Diseases and Metabolic Subpathways

One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the “k-clique” subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway