Handedness preference and switching of peptide helices. Part I: Helices based on protein amino acids

In this article, we review the relevant results obtained during almost 60 years of research on a specific aspect of stereochemistry, namely handedness preference and switches between right‐handed and left‐handed helical peptide structures generated by protein amino acids or appropriately designed, side‐chain modified analogs. In particular, we present and discuss here experimental and theoretical data on three categories of those screw‐sense issues: (i) right‐handed/left‐handed α‐helix transitions underwent by peptides rich in Asp, specific Asp β‐esters, and Asn; (ii) comparison of the preferred conformations adopted by helical host–guest peptide series, each characterized by an amino acid residue (e.g. Ile or its diastereomer aIle) endowed with two chiral centers in its chemical structure; and (iii) right‐handed (type I)/left‐handed (type II) poly‐(Pro)_n helix transitions monitored for peptides rich in Pro itself or its analogs with a pyrrolidine ring substitution, particularly at the biologically important position 4. The unique modular and chiral properties of peptides, combined with their relatively easy synthesis, the chance to shape them into the desired conformation, and the enormous chemical diversity of their coded and non‐coded α‐amino acid building blocks, offer a huge opportunity to structural chemists for applications to bioscience and nanoscience problems. Copyright © 2014 European Peptide Society and John Wiley & Sons, Ltd.     

Microbial diversity of landslide soils assessed by RFLP and SSCP fingerprints

Landslides are a significant component of natural disasters in most countries around the world. Understanding these destructive phenomena through the analysis of possible correlations between microbial communities and the alteration of the soil responsible for landslides is important in order to reduce their negative consequences. To address this issue, bacterial and fungal communities in soils triggering landslides in Termini-Nerano and Massa Lubrense-Nerano (Naples, Italy) were analysed by genetic profiling techniques. Fingerprints were generated by single-strand conformation polymorphisms (SSCP) and random amplified polymorphic DNA (RAPD). The microbial community in both soil types was enriched in species which could contribute to the degradation process occurring during landslides, forming biofilms and leading to the transformation or the formation of minerals. Indeed, some of the identified bacteria were found to favour the transformation of clay minerals. These findings suggest a possible relationship between bacterial and fungal community-colonising soils and the occurrence of landslides.     

Vegetation productivity patterns at high northern latitudes: a multi‐sensor satellite data assessment

Satellite‐derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long‐term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long‐term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR‐based GIMMS‐NDVI version G (GIMMS_g) series, and its recent successor version 3g (GIMMS_3g), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT‐VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS_3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMS_g), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMS_g, particularly in Eurasia where greening is especially pronounced in the GIMMS_3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem‐response to climate change using satellite‐derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas.     

When Structure Affects Function – The Need for Partial Volume Effect Correction in Functional and Resting State Magnetic Resonance Imaging Studies

Both functional and also more recently resting state magnetic resonance imaging have become established tools to investigate functional brain networks. Most studies use these tools to compare different populations without controlling for potential differences in underlying brain structure which might affect the functional measurements of interest. Here, we adapt a simulation approach combined with evaluation of real resting state magnetic resonance imaging data to investigate the potential impact of partial volume effects on established functional and resting state magnetic resonance imaging analyses. We demonstrate that differences in the underlying structure lead to a significant increase in detected functional differences in both types of analyses. Largest increases in functional differences are observed for highest signal-to-noise ratios and when signal with the lowest amount of partial volume effects is compared to any other partial volume effect constellation. In real data, structural information explains about 25% of within-subject variance observed in degree centrality – an established resting state connectivity measurement. Controlling this measurement for structural information can substantially alter correlational maps obtained in group analyses. Our results question current approaches of evaluating these measurements in diseased population with known structural changes without controlling for potential differences in these measurements.     

Evaluation of the Growth Assessment Protocol (GAP) for antenatal detection of small for gestational age: The DESiGN cluster randomised trial

BackgroundAntenatal detection and management of small for gestational age (SGA) is a strategy to reduce stillbirth. Large observational studies provide conflicting results on the effect of the Growth Assessment Protocol (GAP) in relation to detection of SGA and reduction of stillbirth; to the best of our knowledge, there are no reported randomised control trials. Our aim was to determine if GAP improves antenatal detection of SGA compared to standard care.Methods and findingsThis was a pragmatic, superiority, 2-arm, parallel group, open, cluster randomised control trial. Maternity units in England were eligible to participate in the study, except if they had already implemented GAP. All women who gave birth in participating clusters (maternity units) during the year prior to randomisation and during the trial (November 2016 to February 2019) were included. Multiple pregnancies, fetal abnormalities or births before 24⁺¹ weeks were excluded. Clusters were randomised to immediate implementation of GAP, an antenatal care package aimed at improving detection of SGA as a means to reduce the rate of stillbirth, or to standard care. Randomisation by random permutation was stratified by time of study inclusion and cluster size. Data were obtained from hospital electronic records for 12 months prerandomisation, the washout period (interval between randomisation and data collection of outcomes), and the outcome period (last 6 months of the study). The primary outcome was ultrasound detection of SGA (estimated fetal weight <10th centile using customised centiles (intervention) or Hadlock centiles (standard care)) confirmed at birth (birthweight <10th centile by both customised and population centiles). Secondary outcomes were maternal and neonatal outcomes, including induction of labour, gestational age at delivery, mode of birth, neonatal morbidity, and stillbirth/perinatal mortality. A 2-stage cluster–summary statistical approach calculated the absolute difference (intervention minus standard care arm) adjusted using the prerandomisation estimate, maternal age, ethnicity, parity, and randomisation strata. Intervention arm clusters that made no attempt to implement GAP were excluded in modified intention to treat (mITT) analysis; full ITT was also reported. Process evaluation assessed implementation fidelity, reach, dose, acceptability, and feasibility. Seven clusters were randomised to GAP and 6 to standard care. Following exclusions, there were 11,096 births exposed to the intervention (5 clusters) and 13,810 exposed to standard care (6 clusters) during the outcome period (mITT analysis). Age, height, and weight were broadly similar between arms, but there were fewer women: of white ethnicity (56.2% versus 62.7%), and in the least deprived quintile of the Index of Multiple Deprivation (7.5% versus 16.5%) in the intervention arm during the outcome period. Antenatal detection of SGA was 25.9% in the intervention and 27.7% in the standard care arm (adjusted difference 2.2%, 95% confidence interval (CI) −6.4% to 10.7%; p = 0.62). Findings were consistent in full ITT analysis. Fidelity and dose of GAP implementation were variable, while a high proportion (88.7%) of women were reached. Use of routinely collected data is both a strength (cost-efficient) and a limitation (occurrence of missing data); the modest number of clusters limits our ability to study small effect sizes.ConclusionsIn this study, we observed no effect of GAP on antenatal detection of SGA compared to standard care. Given variable implementation observed, future studies should incorporate standardised implementation outcomes such as those reported here to determine generalisability of our findings.Trial registrationThis trial is registered with the ISRCTN registry, ISRCTN67698474.

Matias C Vieira and colleagues evaluate the Growth Assessment Protocol (GAP) for antenatal detection of small for gestational age in the DESiGN cluster randomised trial.     

Predicting potentially pathogenic effects of hRPE65 missense mutations: a computational strategy based on molecular dynamics simulations

The human retinal pigment epithelium-specific 65-kDa protein (hRPE65) plays a crucial role within the retinoid visual cycle and several mutations affecting either its expression level or its enzymatic function are associated with inherited retinal diseases such as Retinitis Pigmentosa. The gene therapy product voretigene neparvovec (Luxturna) has been recently approved for treating hereditary retinal dystrophies; however, the treatment is currently accessible only to patients presenting confirmed biallelic mutations that severely impair hRPE65 function, and many reported hRPE65 missense mutations lack sufficient evidences for proving their pathogenicity. In this context, we developed a computational approach aimed at evaluating the potential pathogenic effect of hRPE65 missense variants located on the dimerisation domain of the protein. The protocol evaluates how mutations may affect folding and conformation stability of this protein region, potentially helping clinicians to evaluate the eligibility for gene therapy of patients diagnosed with this type of hRPE65 variant of uncertain significance.     

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.     

Uniparental Genetic Heritage of Belarusians: Encounter of Rare Middle Eastern Matrilineages with a Central European Mitochondrial DNA Pool

Ethnic Belarusians make up more than 80% of the nine and half million people inhabiting the Republic of Belarus. Belarusians together with Ukrainians and Russians represent the East Slavic linguistic group, largest both in numbers and territory, inhabiting East Europe alongside Baltic-, Finno-Permic- and Turkic-speaking people. Till date, only a limited number of low resolution genetic studies have been performed on this population. Therefore, with the phylogeographic analysis of 565 Y-chromosomes and 267 mitochondrial DNAs from six well covered geographic sub-regions of Belarus we strove to complement the existing genetic profile of eastern Europeans. Our results reveal that around 80% of the paternal Belarusian gene pool is composed of R1a, I2a and N1c Y-chromosome haplogroups – a profile which is very similar to the two other eastern European populations – Ukrainians and Russians. The maternal Belarusian gene pool encompasses a full range of West Eurasian haplogroups and agrees well with the genetic structure of central-east European populations. Our data attest that latitudinal gradients characterize the variation of the uniparentally transmitted gene pools of modern Belarusians. In particular, the Y-chromosome reflects movements of people in central-east Europe, starting probably as early as the beginning of the Holocene. Furthermore, the matrilineal legacy of Belarusians retains two rare mitochondrial DNA haplogroups, N1a3 and N3, whose phylogeographies were explored in detail after de novo sequencing of 20 and 13 complete mitogenomes, respectively, from all over Eurasia. Our phylogeographic analyses reveal that two mitochondrial DNA lineages, N3 and N1a3, both of Middle Eastern origin, might mark distinct events of matrilineal gene flow to Europe: during the mid-Holocene period and around the Pleistocene-Holocene transition, respectively.