Novel serum protein biomarker panel for early diagnosis of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

Therapeutic drug monitoring of osimertinib in EGFR mutant non-small cell lung cancer by dried blood spot and plasma collection: A pilot study.

AIMS: Therapeutic drug monitoring (TDM) has led to significant improvements in individualized medical care, although its implementation in oncology has been limited to date. Tyrosine kinase inhibitors (TKIs) are a group of therapies for which TDM has been suggested. Osimertinib is one such therapy used in the treatment of epidermal growth factor receptor (EGFR) mutation-driven lung cancer. Herein, we describe a prospective pilot study involving 21 patients on osimertinib primarily as a preliminary evaluation of drug levels in a real-world setting. METHODS: Concentrations of the drug and its primary metabolites were measured with a validated liquid chromatography-mass spectrometry (LC-MS) assay across serial timepoints. As part of this study, inter-individual variability by dose and ethnicity as well as intra-individual variability across timepoints are explored. Furthermore, we attempted to validate dried blood spot (DBS)-based quantitation as an accurate alternative to plasma quantitation. RESULTS: Successful quantitation of osimertinib and primary metabolites was achieved for our subjects. Compound plasma levels were highly correlated to DBS levels. There was no significant difference in concentrations with ethnicity or dosing or intra-individual variability across timepoints. CONCLUSIONS: As such, we demonstrate that TDM for osimertinib is practical for future trials. We also validated the use of DBS as an alternative to conventional quantitation for exploration of TDM for osimertinib in larger trials and for other targeted therapies.

Proteome profiling of enriched membrane-associated proteins unraveled a novel sophorose and cello-oligosaccharide transporter in Trichoderma reesei.

BACKGROUND: Trichoderma reesei is an organism extensively used in the bioethanol industry, owing to its capability to produce enzymes capable of breaking down holocellulose into simple sugars. The uptake of carbohydrates generated from cellulose breakdown is crucial to induce the signaling cascade that triggers cellulase production. However, the sugar transporters involved in this process in T. reesei remain poorly identified and characterized. RESULTS: To address this gap, this study used temporal membrane proteomics analysis to identify five known and nine putative sugar transporters that may be involved in cellulose degradation by T. reesei. Docking analysis pointed out potential ligands for the putative sugar transporter Tr44175. Further functional validation of this transporter was carried out in Saccharomyces cerevisiae. The results showed that Tr44175 transports a variety of sugar molecules, including cellobiose, cellotriose, cellotetraose, and sophorose. CONCLUSION: This study has unveiled a transporter Tr44175 capable of transporting cellobiose, cellotriose, cellotetraose, and sophorose. Our study represents the first inventory of T. reesei sugar transportome once exposed to cellulose, offering promising potential targets for strain engineering in the context of bioethanol production.

A Validated Assay to Quantify Osimertinib and Its Metabolites, AZ5104 and AZ7550, from Microsampled Dried Blood Spots and Plasma.

BACKGROUND: Osimertinib is an oral small-molecule tyrosine kinase receptor inhibitor used to treat non-small cell lung cancer (NSCLC) with a sensitizing epidermal growth factor receptor mutation. Patients may experience drug toxicity and require dose deescalation. The study aimed to quantitate osimertinib and its 2 active metabolites, AZ5104 and AZ7550, in microsampled dried blood spots (DBS) collected from patients with NSCLC using a hemaPEN device and compare them with plasma drug levels. METHODS: A 6-min ultrahigh-performance liquid chromatography-tandem mass spectrometry method was developed and validated using plasma and DBS. The accuracy, selectivity, matrix effect, recovery, and stability were assessed using bioanalytical validation criteria. The hematocrit effect was investigated in DBS. Drug levels were measured in 15 patients with NSCLC, and the Bland-Altman method was used to compare measurements between plasma and DBS. RESULTS: The validated assay determined accurate and precise quantities, respectively, for osimertinib in both plasma (93.2%-99.3%; 0.2%-2.3%) and DBS (96.7%-99.6%; 0.5%-10.3%) over a concentration of 1-729 ng/mL. The osimertinib metabolites, AZ5104 and AZ7550, were similarly validated in accordance with bioanalytical guidelines. For 30%-60% patient hematocrit, no hematocrit bias was observed with DBS for all analytes. The Bland-Altman method showed high concordance between plasma and DBS analyte levels. Stability experiments revealed that osimertinib and its metabolites were poorly stable in plasma at room temperature, whereas all analytes were stable in DBS for 10 days at room temperature. CONCLUSIONS: The measurement of osimertinib, AZ5104, and AZ7550 from hemaPEN microsampled DBS is a convenient and reliable approach for therapeutic drug monitoring that produces measurements consistent with plasma drug levels.

Patterns of gene expression in pollen of cotton (Gossypium hirsutum) indicate downregulation as a feature of thermotolerance.

Reproductive performance in plants is impaired as maximum temperatures consistently approach 40°C. However, the timing of heatwaves critically affects their impact. We studied the molecular responses during pollen maturation in cotton to investigate the vulnerability to high temperature. Tetrads (TEs), uninucleate and binucleate microspores, and mature pollen were subjected to SWATH-MS and RNA-seq analyses after exposure to 38/28°C (day/night) for 5 days. The results indicated that molecular signatures were downregulated progressively in response to heat during pollen development. This was even more evident in leaves, where three-quarters of differentially changed proteins decreased in abundance during heat. Functional analysis showed that translation of genes increased in TEs after exposure to heat; however, the reverse pattern was observed in mature pollen and leaves. For example, proteins involved in transport were highly abundant in TEs whereas in later stages of pollen formation and leaves, heat suppressed synthesis of proteins involved in cell-to-cell communication. Moreover, a large number of heat shock proteins were identified in heat-affected TEs, but these proteins were less abundant in mature pollen and leaves. We speculate that the sensitivity of TE cells to heat is related to high rates of translation targeted to pathways that might not be essential for thermotolerance. Molecular signatures during stages of pollen development after heatwaves could provide markers for future genetic improvement.

Inferring Epistasis from Genetic Time-series Data.

Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.

Overlapping roles for PLK1 and Aurora A during meiotic centrosome biogenesis in mouse spermatocytes.

The establishment of bipolar spindles during meiotic divisions ensures faithful chromosome segregation to prevent gamete aneuploidy. We analyzed centriole duplication, as well as centrosome maturation and separation during meiosis I and II using mouse spermatocytes. The first round of centriole duplication occurs during early prophase I, and then, centrosomes mature and begin to separate by the end of prophase I to prime formation of bipolar metaphase I spindles. The second round of centriole duplication occurs at late anaphase I, and subsequently, centrosome separation coordinates bipolar segregation of sister chromatids during meiosis II. Using a germ cell-specific conditional knockout strategy, we show that Polo-like kinase 1 and Aurora A kinase are required for centrosome maturation and separation prior to metaphase I, leading to the formation of bipolar metaphase I spindles. Furthermore, we show that PLK1 is required to block the second round of centriole duplication and maturation until anaphase I. Our findings emphasize the importance of maintaining strict spatiotemporal control of cell cycle kinases during meiosis to ensure proficient centrosome biogenesis and, thus, accurate chromosome segregation during spermatogenesis.

Proteomic Analysis of Whole Blood Using Volumetric Absorptive Microsampling for Precision Medicine Biomarker Studies.

Microsampling of patient blood promises several benefits over conventional phlebotomy practices to facilitate precision medicine studies. These include at-home patient blood collection, supporting telehealth monitoring, minimal postcollection processing, and compatibility with nonrefrigerated transport and storage. However, for proteomic biomarker studies, mass spectrometry of whole blood has generally been avoided in favor of using plasma or serum obtained from venepuncture. We evaluated the use of a volumetric absorptive microsampling (VAMS) device as a sample preparation matrix to enable LC-MS proteomic analyses of dried whole blood. We demonstrated the detection and robust quantitation of up to 1600 proteins from single-shot shotgun-LC-MS analysis of dried whole blood, greatly enhancing proteome depth compared with conventional single-shot LC-MS analyses of undepleted plasma. Some proteins not previously reported in blood were detected using this approach. Various washing reagents were used to demonstrate that proteins can be preferentially removed from VAMS devices prior to downstream analyses. We provide a demonstration that archival frozen blood cell pellets housed under long-term storage (exceeding 5 years) are compatible with VAMS to enable quantitation of potential biomarker proteins from biobank repositories. These demonstrations are important steps in establishing viable analysis workflows to underpin large-scale precision medicine studies. Data are available via ProteomeXchange with the identifier PXD028605.

pH- and Calcium-Dependent Aromatic Network in the SARS-CoV-2 Envelope Protein.

The envelope (E) protein of the SARS-CoV-2 virus is a membrane-bound viroporin that conducts cations across the endoplasmic reticulum Golgi intermediate compartment (ERGIC) membrane of the host cell to cause virus pathogenicity. The structure of the closed state of the E transmembrane (TM) domain, ETM, was recently determined using solid-state NMR spectroscopy. However, how the channel pore opens to mediate cation transport is unclear. Here, we use 13C and 19F solid-state NMR spectroscopy to investigate the conformation and dynamics of ETM at acidic pH and in the presence of calcium ions, which mimic the ERGIC and lysosomal environment experienced by the E protein in the cell. Acidic pH and calcium ions increased the conformational disorder of the N- and C-terminal residues and also increased the water accessibility of the protein, indicating that the pore lumen has become more spacious. ETM contains three regularly spaced phenylalanine (Phe) residues in the center of the peptide. 19F NMR spectra of para-fluorinated Phe20 and Phe26 indicate that both residues exhibit two sidechain conformations, which coexist within each channel. These two Phe conformations differ in their water accessibility, lipid contact, and dynamics. Channel opening by acidic pH and Ca2+ increases the population of the dynamic lipid-facing conformation. These results suggest an intricate aromatic network that regulates the opening of the ETM channel pore. This aromatic network may be a target for E inhibitors against SARS-CoV-2 and related coronaviruses.

Activating Killer-Cell Immunoglobulin-Like Receptors Are Associated With the Severity of Coronavirus Disease 2019.

BACKGROUND: Etiopathogenesis of the clinical variability of the coronavirus disease 2019 (COVID-19) remains mostly unknown. In this study, we investigate the role of killer cell immunoglobulin-like receptor (KIR)/human leukocyte antigen class-I (HLA-I) interactions in the susceptibility and severity of COVID-19. METHODS: We performed KIR and HLA-I genotyping and natural killer cell (NKc) receptors immunophenotyping in 201 symptomatic patients and 210 noninfected controls. RESULTS: The NKcs with a distinctive immunophenotype, suggestive of recent activation (KIR2DS4low CD16low CD226low CD56high TIGIThigh NKG2Ahigh), expanded in patients with severe COVID-19. This was associated with a higher frequency of the functional A-telomeric activating KIR2DS4 in severe versus mild and/or moderate patients and controls (83.7%, 55.7% and 36.2%, P < 7.7 × 10-9). In patients with mild and/or moderate infection, HLA-B*15:01 was associated with higher frequencies of activating B-telomeric KIR3DS1 compared with patients with other HLA-B*15 subtypes and noninfected controls (90.9%, 42.9%, and 47.3%; P < .002; Pc = 0.022). This strongly suggests that HLA-B*15:01 specifically presenting severe acute respiratory syndrome coronavirus 2 peptides could form a neoligand interacting with KIR3DS1. Likewise, a putative neoligand for KIR2DS4 could arise from other HLA-I molecules presenting severe acute respiratory syndrome coronavirus 2 peptides expressed on infected an/or activated lung antigen-presenting cells. CONCLUSIONS: Our results support a crucial role of NKcs in the clinical variability of COVID-19 with specific KIR/ligand interactions associated with disease severity.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins.

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Plasma metabolites associated with biomarker evidence of neurodegeneration in cognitively normal older adults.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently has no cure. Identifying biochemical changes associated with neurodegeneration prior to symptom onset, will provide insight into the biological mechanisms associated with neurodegenerative processes, that may also aid in identifying potential drug targets. The current study therefore investigated associations between plasma neurofilament light chain (NF-L), a marker of neurodegeneration, with plasma metabolites that are products of various cellular processes. Plasma NF-L, measured by ultrasensitive Single molecule array (Simoa) technology (Quanterix) and plasma metabolites, measured by mass-spectrometry (AbsoluteIDQ® p400HR kit, BIOCRATES), were assessed in the Kerr Anglican Retirement Village Initiative in Ageing Health (KARVIAH) cohort comprising 100 cognitively normal older adults. Metabolites belonging to biogenic amine (creatinine, symmetric dimethylarginine, asymmetric dimethylarginine; ADMA, kynurenine, trans-4-hydroxyproline), amino acid (citrulline, proline, arginine, asparagine, phenylalanine, threonine) and acylcarnitine classes were observed to have positive correlations with plasma NF-L, suggesting a link between neurodegeneration and biological pathways associated with neurotransmitter regulation, nitric oxide homoeostasis, inflammation and mitochondrial function. Additionally, after stratifying participants based on low/high brain amyloid-ß load (Aß ±) assessed by positron emission tomography, while creatinine, SDMA and citrulline correlated with NF-L in both Aß- and Aß+ groups, ADMA, proline, arginine, asparagine, phenylalanine and acylcarnitine species correlated with NF-L only in the Aß+ group after adjusting for confounding variables, suggesting that the association of these metabolites with neurodegeneration may be relevant to AD-related neuropathology. Metabolites identified to be associated with plasma NF-L may have the potential to serve as prognostic markers for neurodegenerative diseases, however, further studies are required to validate the current findings in an independent cohort, both cross-sectionally and longitudinally.

RocaSec: a standalone GUI-based package for robust co-evolutionary analysis of proteins.

SUMMARY: Patterns of mutational correlations, learnt from protein sequences, have been shown to be informative of co-evolutionary sectors that are tightly linked to functional and/or structural properties of proteins. Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA), to reliably predict co-evolutionary sectors of proteins, while controlling for statistical errors caused by limited data. RoCA was demonstrated on multiple viral proteins, with the inferred sectors showing close correspondences with experimentally-known biochemical domains. To facilitate seamless use of RoCA and promote more widespread application to protein data, here we present a standalone cross-platform package 'RocaSec' which features an easy-to-use GUI. The package only requires the multiple sequence alignment of a protein for inferring the co-evolutionary sectors. In addition, when information on the protein biochemical domains is provided, RocaSec returns the corresponding statistical association between the inferred sectors and biochemical domains. AVAILABILITY AND IMPLEMENTATION: The RocaSec software is publicly available under the MIT License at https://github.com/ahmedaq/RocaSec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MPF-BML: a standalone GUI-based package for maximum entropy model inference.

SUMMARY: Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow-Boltzmann Machine Learning (MPF-BML), for performing fast and accurate inference of maximum entropy model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF-BML and encourage more widespread application to data in diverse fields, we present a standalone cross-platform package of MPF-BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. AVAILABILITY AND IMPLEMENTATION: The MPF-BML software is publicly available under the MIT License at https://github.com/ahmedaq/MPF-BML-GUI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Two of a kind: transmissible Schwann cell cancers in the endangered Tasmanian devil (Sarcophilus harrisii).

Devil facial tumour disease (DFTD) comprises two genetically distinct transmissible cancers (DFT1 and DFT2) endangering the survival of the Tasmanian devil (Sarcophilus harrisii) in the wild. DFT1 first arose from a cell of the Schwann cell lineage; however, the tissue-of-origin of the recently discovered DFT2 cancer is unknown. In this study, we compared the transcriptome and proteome of DFT2 tumours to DFT1 and normal Tasmanian devil tissues to determine the tissue-of-origin of the DFT2 cancer. Our findings demonstrate that DFT2 expresses a range of Schwann cell markers and exhibits expression patterns consistent with a similar origin to the DFT1 cancer. Furthermore, DFT2 cells express genes associated with the repair response to peripheral nerve damage. These findings suggest that devils may be predisposed to transmissible cancers of Schwann cell origin. The combined effect of factors such as frequent nerve damage from biting, Schwann cell plasticity and low genetic diversity may allow these cancers to develop on rare occasions. The emergence of two independent transmissible cancers from the same tissue in the Tasmanian devil presents an unprecedented opportunity to gain insight into cancer development, evolution and immune evasion in mammalian species.

Fitness landscape of the human immunodeficiency virus envelope protein that is targeted by antibodies.

HIV is a highly mutable virus, and over 30 years after its discovery, a vaccine or cure is still not available. The isolation of broadly neutralizing antibodies (bnAbs) from HIV-infected patients has led to renewed hope for a prophylactic vaccine capable of combating the scourge of HIV. A major challenge is the design of immunogens and vaccination protocols that can elicit bnAbs that target regions of the virus's spike proteins where the likelihood of mutational escape is low due to the high fitness cost of mutations. Related challenges include the choice of combinations of bnAbs for therapy. An accurate representation of viral fitness as a function of its protein sequences (a fitness landscape), with explicit accounting of the effects of coupling between mutations, could help address these challenges. We describe a computational approach that has allowed us to infer a fitness landscape for gp160, the HIV polyprotein that comprises the viral spike that is targeted by antibodies. We validate the inferred landscape through comparisons with experimental fitness measurements, and various other metrics. We show that an effective antibody that prevents immune escape must selectively bind to high escape cost residues that are surrounded by those where mutations incur a low fitness cost, motivating future applications of our landscape for immunogen design.

Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models.

Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply random matrix theory to derive asymptotic first-order and distributional results for both leading eigenvalues and eigenvectors of sample correlation matrices, assuming a high-dimensional regime in which the ratio p/n, of number of variables p to sample size n, converges to a positive constant. While the first-order spectral properties of sample correlation matrices match those of sample covariance matrices, their asymptotic distributions can differ significantly. Indeed, the correlation-based fluctuations of both sample eigenvalues and eigenvectors are often remarkably smaller than those of their sample covariance counterparts.

Retinal proteomics of experimental glaucoma model reveal intraocular pressure-induced mediators of neurodegenerative changes.

Current evidence suggests that exposure to chronically induced intraocular pressure (IOP) leads to neurodegenerative changes in the inner retina. This study aimed to determine retinal proteomic alterations in a rat model of glaucoma and compared findings with human retinal proteomics changes in glaucoma reported previously. We developed an experimental glaucoma rat model by subjecting the rats to increased IOP (9.3 ± 0.1 vs 20.8 ± 1.6 mm Hg) by weekly microbead injections into the eye (8 weeks). The retinal tissues were harvested from control and glaucomatous eyes and protein expression changes analysed using a multiplexed quantitative proteomics approach (TMT-MS3). Immunofluorescence was performed for selected protein markers for data validation. Our study identified 4304 proteins in the rat retinas. Out of these, 139 proteins were downregulated (≤0.83) while the expression of 109 proteins was upregulated (≥1.2-fold change) under glaucoma conditions (P ≤ .05). Computational analysis revealed reduced expression of proteins associated with glutathione metabolism, mitochondrial dysfunction/oxidative phosphorylation, cytoskeleton, and actin filament organisation, along with increased expression of proteins in coagulation cascade, apoptosis, oxidative stress, and RNA processing. Further functional network analysis highlighted the differential modulation of nuclear receptor signalling, cellular survival, protein synthesis, transport, and cellular assembly pathways. Alterations in crystallin family, glutathione metabolism, and mitochondrial dysfunction associated proteins shared similarities between the animal model of glaucoma and the human disease condition. In contrast, the activation of the classical complement pathway and upregulation of cholesterol transport proteins were exclusive to human glaucoma. These findings provide insights into the neurodegenerative mechanisms that are specifically affected in the retina in response to chronically elevated IOP.

Sub-dominant principal components inform new vaccine targets for HIV Gag.

MOTIVATION: Patterns of mutational correlations, learnt from patient-derived sequences of human immunodeficiency virus (HIV) proteins, are informative of biochemically linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design. RESULTS: By using sequence data for HIV Gag, complemented by model-based simulations, we revealed that certain networks of interacting sites that appear important for vaccine design purposes are not accurately reflected by the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. By incorporating information from the sub-dominant PCs, we identified a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this network, we propose several new candidates for a potent T-cell-based HIV vaccine. AVAILABILITY AND IMPLEMENTATION: Accession numbers of all sequences used and the source code scripts for all analysis and figures reported in this work are available online at https://github.com/faraz107/HIV-Gag-Immunogens. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteome analysis of endometrial tissue from patients with PCOS reveals proteins predicted to impact the disease.

Polycystic ovary syndrome (PCOS) is a complex disease that causes an ovulatory infertility in approximately 10% of reproductive-age women. We searched for candidate proteins that might contribute to endometrial receptivity defects in PCOS patients, and result in adverse reproductive outcomes. Shotgun proteomics approach was used to investigate the proteome profile of the endometrium at the luteal phase in PCOS patients compared to healthy fertile individuals. Biological process and pathway analyses were conducted to categorize the proteins with differential expressions. Confirmation was performed for a number of proteins via immunoblotting in new samples. 150 proteins with higher abundance, and 46 proteins with lower abundance were identified in the endometrial tissue from PCOS patients compared to healthy fertile individuals. The proteins with higher abundance were enriched in protein degradation, cell cycle, and signaling cascades. Proteins with lower abundance in PCOS patients were enriched in extracellular matrix (ECM) composition and function, as well as the salvage pathway of purine biosynthesis. Metabolism was the most affected biological process with over 100 up-regulated, and approximately 30 down-regulated proteins. Our results indicate significant imbalances in metabolism, proteasome, cell cycle, ECM related proteins, and signaling cascades in endometrial tissue of PCOS, which may contribute to poor reproductive outcomes in these patients. We postulate that the endometria in PCOS patients may not be well-differentiated and synchronized for implantation. Possible roles of the above-mentioned pathways that underlie implantation failure in PCOS will be discussed. Our findings need to be confirmed in larger populations.