Loss of alveolar membrane diffusing capacity and pulmonary capillary blood volume in pulmonary arterial hypertension

Background

Reduced gas transfer in patients with pulmonary arterial hypertension (PAH) is traditionally attributed to remodeling and progressive loss of pulmonary arterial vasculature that results in decreased capillary blood volume available for gas exchange.

Methods

We tested this hypothesis by determination of lung diffusing capacity (DL) and its components, the alveolar capillary membrane diffusing capacity (D_m) and lung capillary blood volume (V_c) in 28 individuals with PAH in comparison to 41 healthy individuals, and in 19 PAH patients over time. Using single breath simultaneous measure of diffusion of carbon monoxide (DL_CO) and nitric oxide (DL_NO), DL and D_m were respectively determined, and V_c calculated. D_m and V_c were evaluated over time in relation to standard clinical indicators of disease severity, including brain natriuretic peptide (BNP), 6-minute walk distance (6MWD) and right ventricular systolic pressure (RVSP) by echocardiography.

Results

Both DL_CO and DL_NO were reduced in PAH as compared to controls and the lower DL in PAH was due to loss of both D_m and V_c (all p < 0.01). While DL_CO of PAH patients did not change over time, DL_NO decreased by 24 ml/min/mmHg/year (p = 0.01). Consequently, D_m decreased and V_c tended to increase over time, which led to deterioration of the D_m/V_c ratio, a measure of alveolar-capillary membrane functional efficiency without changes in clinical markers.

Conclusions

The findings indicate that lower than normal gas transfer in PAH is due to loss of both D_m and V_c, but that deterioration of D_m/V_c over time is related to worsening membrane diffusion.     

Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks

Despite rapidly increasing numbers of available 3D structures, membrane proteins still account for less than 1% of all structures in the Protein Data Bank. Recent high-resolution structures indicate a clearly broader structural diversity of membrane proteins than initially anticipated, motivating the development of reliable structure prediction methods specifically tailored for this class of molecules. One important prediction target capturing all major aspects of a protein's 3D structure is its contact map. Our analysis shows that computational methods trained to predict residue contacts in globular proteins perform poorly when applied to membrane proteins. We have recently published a method to identify interacting alpha-helices in membrane proteins based on the analysis of coevolving residues in predicted transmembrane regions. Here, we present a substantially improved algorithm for the same problem, which uses a newly developed neural network approach to predict helix-helix contacts. In addition to the input features commonly used for contact prediction of soluble proteins, such as windowed residue profiles and residue distance in the sequence, our network also incorporates features that apply to membrane proteins only, such as residue position within the transmembrane segment and its orientation toward the lipophilic environment. The obtained neural network can predict contacts between residues in transmembrane segments with nearly 26% accuracy. It is therefore the first published contact predictor developed specifically for membrane proteins performing with equal accuracy to state-of-the-art contact predictors available for soluble proteins. The predicted helix-helix contacts were employed in a second step to identify interacting helices. For our dataset consisting of 62 membrane proteins of solved structure, we gained an accuracy of 78.1%. Because the reliable prediction of helix interaction patterns is an important step in the classification and prediction of membrane protein folds, our method will be a helpful tool in compiling a structural census of membrane proteins.     

Camps 2.0: exploring the sequence and structure space of prokaryotic, eukaryotic, and viral membrane proteins

Structural bioinformatics of membrane proteins is still in its infancy, and the picture of their fold space is only beginning to emerge. Because only a handful of three-dimensional structures are available, sequence comparison and structure prediction remain the main tools for investigating sequence-structure relationships in membrane protein families. Here we present a comprehensive analysis of the structural families corresponding to α-helical membrane proteins with at least three transmembrane helices. The new version of our CAMPS database (CAMPS 2.0) covers nearly 1300 eukaryotic, prokaryotic, and viral genomes. Using an advanced classification procedure, which is based on high-order hidden Markov models and considers both sequence similarity as well as the number of transmembrane helices and loop lengths, we identified 1353 structurally homogeneous clusters roughly corresponding to membrane protein folds. Only 53 clusters are associated with experimentally determined three-dimensional structures, and for these clusters CAMPS is in reasonable agreement with structure-based classification approaches such as SCOP and CATH. We therefore estimate that ～1300 structures would need to be determined to provide a sufficient structural coverage of polytopic membrane proteins. CAMPS 2.0 is available at http://webclu.bio.wzw.tum.de/CAMPS2.0/.     

PROSO II--a new method for protein solubility prediction

Many fields of science and industry depend on efficient production of active protein using heterologous expression in Escherichia coli. The solubility of proteins upon expression is dependent on their amino acid sequence. Prediction of solubility from sequence is therefore highly valuable. We present a novel machine-learning-based model called PROSO II which makes use of new classification methods and growth in experimental data to improve coverage and accuracy of solubility predictions. The classification algorithm is organized as a two-layered structure in which the output of a primary Parzen window model for sequence similarity and a logistic regression classifier of amino acid k-mer composition serve as input for a second-level logistic regression classifier. Compared with previously published research our model is trained on five times more data than used by any other method before (82 000 proteins). When tested on a separate holdout set not used at any point of method development our server attained the best results in comparison with other currently available methods: accuracy 75.4%, Matthew's correlation coefficient 0.39, sensitivity 0.731, specificity 0.759, gain (soluble) 2.263. In summary, due to utilization of cutting edge machine learning technologies combined with the largest currently available experimental data set the PROSO II server constitutes a substantial improvement in protein solubility predictions. PROSO II is available at http://mips.helmholtz-muenchen.de/prosoII.     

Phylogenetic web profiler

SUMMARY: Phylogenetic Web Profiler (PWP) is a web-based service designed to perform phylogenetic profiling of proteins against genomes. The current version offers a selection of 63 completed genomes and available plasmids as annotated in the PEDANT genome database. Unlike currently available applications, this tool offers several choices of ortholog prediction parameters including E-value cutoff, percent length difference tolerance, and annotation similarity. Additional features include tight integration with the PEDANT database and tools to analyze properties of predicted proteins. PWP should prove very useful for the analysis of functional-linkage between proteins.     

What we have learned about prokaryotes from structural genomics

Five years ago systematic determination and theoretical analysis of all protein structures encoded in model prokaryotic organisms was proposed as a powerful way to obtain new insights into protein function and the variety of protein folds. What has been the pay-off from studying structures in genomic context? Have we learned anything new about protein structure? Can we now predict protein function better? In this contribution, I summarize the status of large-scale structure determination projects on prokaryotes and provide an overview of the main results obtained from experimental and theoretical studies in this dynamic research field.     

Ganglion cells are required for normal progenitor- cell proliferation but not cell-fate determination or patterning in the developing mouse retina

The vertebrate retina develops from an amorphous sheet of dividing retinal progenitor cells (RPCs) through a sequential process that culminates in an exquisitely patterned neural tissue. A current model for retinal development posits that sequential cell-type differentiation is the result of changes in the intrinsic competence state of multipotent RPCs as they advance in time and that the intrinsic changes are influenced by continuous changes in the extracellular environment. Although several studies support the proposition that newly differentiated cells alter the extrinsic state of the developing retina, it is still far from clear what role they play in modifying the extracellular environment and in influencing the properties of RPCs. Here, we specifically ablate retinal ganglion cells (RGCs) as they differentiate, and we determine the impact of RGC absence on retinal development. We find that RGCs are not essential for changing the competence of RPCs, but they are necessary for maintaining sufficient numbers of RPCs by regulating cell proliferation via growth factors. Intrinsic rather than extrinsic factors are likely to play the critical roles in determining retinal cell fate.     

MIPS bacterial genomes functional annotation benchmark dataset

MOTIVATION: Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. RESULTS: The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. AVAILABILITY: BFAB is available at http://mips.gsf.de/proj/bfab