Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues

Background  

The emergence of structural genomics presents significant challenges in the annotation of biologically uncharacterized proteins. Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs.     

Rapid screening of a large insert BAC library for specific 16S rRNA genes using TRFLP

It is widely believed that the vast majority of microbes in the environment have-yet-to-be cultured using standard techniques. Bulk DNA from microbial communities is therefore often cloned into large insert vectors (e.g. bacterial artificial chromosomes [BAC] or cosmids) in order to study the genetic properties of these as yet (un)-cultured bacteria. In a typical BAC experiment, tens of thousands of clones are generated with only a small fraction of colonies containing the target(s) of interest. Efficient screening methodologies are therefore needed to allow targeted clone isolation. In this paper, we describe a rapid, inexpensive protocol that allows for the identification of specific 16S ribosomal RNA genes in a metagenomic library arrayed into 384-well microtiter plates. The rapid screening protocol employs Terminal Restriction Fragment Length Polymorphism (TRFLP) analysis to identify wells containing specific T-RF peaks. A nested approach using multiplexed samples of 384, 48, 8, and single colony analysis is described and applied in order to survey a BAC library generated from a marine microbial community off the coast of New Jersey. Screening revealed a total of 50 different 16 rRNA genes within the BAC library. Overall, the multiplexing format provided a simple, cost effective methodology for detecting clones bearing a target gene of interest in a large clone library. However, the limitations of screening BAC libraries using PCR methodologies and recommendations for improved screening efficiency using this approach are also discussed.     

A decision tree approach to modelling nitrogen fertiliser use efficiency in New Zealand pastures

Quantifying nitrogen (N) fertiliser use efficiency (NFUE) in pastoral systems has important implications for fertiliser management from both economic and environmental points of view. The potential of a decision tree approach for modelling NFUE in New Zealand pastures was investigated. The decision tree model suggested that the time of applying N fertiliser was the most important factor influencing NFUE, with August or September (early spring in New Zealand) being the best time of application. The interaction of rainfall and temperature, rainfall, phosphorus (P) fertiliser history, soil Olsen P and slope were other important factors influencing NFUE. The model was validated for 11 of the 16 trials tested with a predictive accuracy of 69%. The mechanisms by which these factors influenced NFUE and the uncertainty associated with the model prediction were discussed. It was concluded that this type of modelling approach can be used to predict NFUE and thereby to assist decisions on when and where to apply N fertiliser in pastures for increasing productivity while reducing the environmental impact.     

Heterogeneous ribonucleoprotein m is a splicing regulatory protein that can enhance or silence splicing of alternatively spliced exons

Splicing of fibroblast growth factor receptor 2 (FGFR2) alternative exons IIIb and IIIc is regulated by the auxiliary RNA cis-element ISE/ISS-3 that promotes splicing of exon IIIb and silencing of exon IIIc. Using RNA affinity chromatography, we have identified heterogeneous nuclear ribonucleoprotein M (hnRNP M) as a splicing regulatory factor that binds to ISE/ISS-3 in a sequence-specific manner. Overexpression of hnRNP M promoted exon IIIc skipping in a cell line that normally includes it, and association of hnRNP M with ISE/ISS-3 was shown to contribute to this splicing regulatory function. Thus hnRNP M, along with other members of the hnRNP family of RNA-binding proteins, plays a combinatorial role in regulation of FGFR2 alternative splicing. We also determined that hnRNP M can affect the splicing of several other alternatively spliced exons. This activity of hnRNP M included the ability not only to induce exon skipping but also to promote exon inclusion. This is the first report demonstrating a role for this abundant hnRNP family member in alternative splicing in mammals and suggests that this protein may broadly contribute to the fidelity of splice site recognition and alternative splicing regulation.     

Using surface envelopes to constrain molecular modeling

Molecular density information (as measured by electron microscopic reconstructions or crystallographic density maps) can be a powerful source of information for molecular modeling. Molecular density constrains models by specifying where atoms should and should not be. Low-resolution density information can often be obtained relatively quickly, and there is a need for methods that use it effectively. We have previously described a method for scoring molecular models with surface envelopes to discriminate between plausible and implausible fits. We showed that we could successfully filter out models with the wrong shape based on this discrimination power. Ideally, however, surface information should be used during the modeling process to constrain the conformations that are sampled. In this paper, we describe an extension of our method for using shape information during computational modeling. We use the envelope scoring metric as part of an objective function in a global optimization that also optimizes distances and angles while avoiding collisions. We systematically tested surface representations of proteins (using all nonhydrogen heavy atoms) with different abundance of distance information and showed that the root mean square deviation (RMSD) of models built with envelope information is consistently improved, particularly in data sets with relatively small sets of short-range distances.     

Virus-host interactions in salt lakes

Natural hypersaline waters are widely distributed around the globe, as both continental surface waters and sea floor lakes, the latter being maintained by the large density difference between the hypersaline and overlying marine water. Owing to the extreme salt concentrations, close to or at saturation (approximately 35%, w/v), such waters might be expected to be devoid of life but, in fact, maintain dense populations of microbes. The majority of these microorganisms are halophilic prokaryotes belonging to the Domain Archaea, 'haloarchaea'. Viruses infecting haloarchaea are a vital part of hypersaline ecosystems, in many circumstances outnumbering cells by 10-100-fold. However, few of these 'haloviruses' have been isolated and even fewer have been characterised in molecular detail. In this review, we explore the methods used by haloviruses to replicate within their hosts and consider the implications of haloviral-haloarchaeal interactions for salt lake ecology.     

Optimization of FDG-PET/CT imaging protocol for evaluation of patients with primary and metastatic liver disease

Background

Accurate determination of the extrahepatic extent and intrahepatic distribution of disease is very important in patients with primary and metastatic liver disease for deciding whether a patient receives potentially curable surgery or palliative treatment. Our objective was to evaluate the efficacy of delayed phase FDG-PET/CT imaging in lesion detection and to define its clinical impact compared to triple-phase contrast enhanced CT (CECT).

Methods

30 patients underwent delayed phase FDG-PET/CT imaging (90 min whole body scan followed by a delayed abdominal scan at 120 min). Maximum standard uptake values (SUVs) and SUV ratios between tumor and normal liver parenchyma (T/N) were evaluated. In addition, comparison was made to CECT obtained within 10 days of the FDG-PET/CT to evaluate for lesion concordance within individual liver segments (Couinaud designation).

Results

Sites of primary malignancies included: colorectal (19), breast (3), pancreas (2), lung (2), carcinoid (2), cholangiocarcinoma (1), and hepatocellular carcinoma (1). There was a significant increase in SUV value of liver lesions between early and delayed acquisition (P < 0.001). Although there was not a significant reduction in liver background activity between the two studies, there was a strong increase in T/N ratio (P < 0.001) allowing better lesion detection by visual inspection. New lesions were identified in 5 of the 30 patients, which were not appreciated on the early scan. Delayed phase FDG-PET/CT identified one lesion which was not present on the corresponding CECT. Delayed phase FDG-PET/CT revealed extrahepatic sites of metastases not appreciated on CECT in 6 patients.

Conclusion

Delayed phase FDG-PET/CT protocol improved lesion detectability in primary and metastatic liver disease, revealing new lesions in 17% of the patients. Moreover, FDG-PET/CT identified extrahepatic disease not seen on CECT in 20% of the patients.

     

Correction to: Who is the ocean? Preface to the future seas 2030 special issue

Reviews in Fish Biology and Fisheries - A correction to this paper has been published: https://doi.org/10.1007/s11160-021-09657-9     

Safeguarding marine life: conservation of biodiversity and ecosystems

Reviews in Fish Biology and Fisheries - Marine ecosystems and their associated biodiversity sustain life on Earth and hold intrinsic value. Critical marine ecosystem services include maintenance of...     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.