Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

The composition, richness, and evenness of seedlings from the soil seed bank of a semi-arid steppe in northern China are affected by long-term stocking rates of sheep and rainfall variation

The soil seed bank has a large influence on the potential for grassland restoration. This study aimed to characterise the composition, density, richness, and evenness of seedlings emerging from the soil seed bank under different sheep stocking rates, in a summer grazing system, in semi-arid China. Soil was sampled in 2015, a year with extreme drought conditions and in 2016, a normal rainfall year. The soil seed bank was assessed by measuring seedling emergence under laboratory conditions. Comprising 16 species, 85.4% of the seedlings were concentrated within a depth of 0–5cm. Drought significantly reduced the density and richness of the seedlings. Grazing increased the richness of seedlings by increasing the richness of aboveground species, and grazing significantly reduced the evenness of the seedlings by reducing the evenness of aboveground species. Drought significantly reduced the similarities between the seedlings and the aboveground species, whereas grazing increased similarities in both years. This study revealed that the density and richness of seedlings were higher in higher stocking rate in drought year. We conclude that negative effects on density, richness and evenness of the seedlings caused by drought can be overcome by rotational grazing especially at higher stocking rate.

Multicomponent intermetallic nanoparticles and superb mechanical behaviors of complex alloys

Alloy design based on single–principal-element systems has approached its limit for performance enhancements. A substantial increase in strength up to gigapascal levels typically causes the premature failure of materials with reduced ductility. Here, we report a strategy to break this trade-off by controllably introducing high-density ductile multicomponent intermetallic nanoparticles (MCINPs) in complex alloy systems. Distinct from the intermetallic-induced embrittlement under conventional wisdom, such MCINP-strengthened alloys exhibit superior strengths of 1.5 gigapascals and ductility as high as 50% in tension at ambient temperature. The plastic instability, a major concern for high-strength materials, can be completely eliminated by generating a distinctive multistage work-hardening behavior, resulting from pronounced dislocation activities and deformation-induced microbands. This MCINP strategy offers a paradigm to develop next-generation materials for structural applications.

SHORT CHAIN FATTY ACIDS UPREGULATE ADIPOKINE PRODUCTION IN TYPE 2 DIABETES-DERIVED HUMAN ADIPOCYTES

PURPOSE

Short chain fatty acids (SCFAs) play a major regulatory role in adipocyte function and metabolism. The aim of this study was to investigate the effects of SCFAs on adiponectin and leptin expression in adipocytes, and also to determine whether the effects of SCFA treatment in visceral adipocytes obtained from healthy subjects are different relative to the effects in adipocytes from patients with type 2 diabetes.

MATERIALS AND METHODS

Human pericardiac preadipocytes and human pericardiac preadipocytes type 2 diabetes were differentiated into adipocytes for 21 days in 48-well plates. After differentiation, two kinds of mature adipocytes, human pericardiac adipocytes (HPAd) and human pericardiac adipocytes-type 2 diabetes (HPAd-T2D) were incubated with or without 1 mM of acetic acid (AA), butyrate acid (BA), and propionic acid (PA). After 48 hours of incubation, intracellular lipid accumulation was measured using oil red staining. In addition, mRNA levels of adiponectin, leptin and Peroxisome Proliferator-Activated Receptor γ (PPARγ) were determined by Real-Time PCR system.

RESULTS

In HPAd, SCFA supplementation did not inhibit lipid accumulation. By contrast, both AA (p<0.01) and PA (p<0.01) significantly inhibited lipid accumulation in HPAd-T2D. Regarding mRNA levels of adiponectin, no significant changes were found in HPAd, while all three types of SCFAs significantly increased (p<0.05) adiponectin expression in HPAd-T2D. Leptin mRNA expression levels were significantly increased by treatment with all three types of SCFAs in both HPAd (p<0.05) and HPAd-T2D (p<0.05).

CONCLUSION

SCFAs inhibited lipid droplet accumulation and increased mRNA expression of adiponectin and leptin in T2D-derived adipocytes.

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

Kaposi’s Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells.

Kaposi’s Sarcoma herpesvirus (KSHV) is the etiologic agent of Kaposi’s Sarcoma, the most common tumor of AIDS patients. KSHV modulates host cell signaling and metabolism to maintain a life-long latent infection. To unravel the underlying cellular mechanisms modulated by KSHV, we used multiple global systems biology platforms to identify and integrate changes in both cellular protein expression and transcription following KSHV infection of endothelial cells, the relevant cell type for KS tumors. The analysis identified several interesting pathways including peroxisome biogenesis. Peroxisomes are small cytoplasmic organelles involved in redox reactions and lipid metabolism. KSHV latent infection increases the number of peroxisomes per cell and proteins involved in peroxisomal lipid metabolism are required for the survival of latently infected cells. In summary, through integration of multiple global systems biology analyses we were able to identify novel pathways that could not be predicted by one platform alone and found that lipid metabolism in a small cytoplasmic organelle is necessary for the survival of latent infection with a herpesvirus.

[The surgery of thyrotropin-secreting pituitary adenomas and the significance of thyroid stimulating hormone level in follow-up]

Objective: To explore the clinical characteristics of thyrotropin-secreting pituitary adenomas and the effect of the transsphenoidal approach of procedure with/without drug treatment for thyrotropin-secreting pituitary adenomas, and to investigate the correlation between the prognosis of thyrotropin-secreting pituitary adenomas and the postoperative level of thyroid stimulating hormone (TSH). Methods: A total of 45 patients with thyrotropin-secreting pituitary adenoma who hospitalized in Peking Union Medical College Hospital from 2000 to 2015 were enrolled in the study.We collected the clinical features, hormone levels, imaging findings, treatment and follow-up data of these patients. Results: The average age of the patients was 40.26 years old and the average disease duration was 5.83 years.Among them, 8 cases were microadenomas and 37 cases were macroadenoma.Thirty patients were treated with somatostatin analogues before surgery.TSH level was significantly decreased after operation (P=0.012). All of them had done the pituitary surgery, including 44 transsphenoidal surgery and 1 subfrontal surgery that had recurrence one year after the transsphenoidal surgery.The TSH level of 34 cases went back to normal on the third day after surgery.The remission rates on three months and one year after surgery in patients with TSH level<0.1 mIU/L on postoperative day (POD) 3 were significantly higher than those in patients with TSH level>0.1 mIU/L on POD3.As well as the remission rates on three months and one year after surgery in patients with TSH level normal on postoperative one month were significantly higher than those in patients with TSH level abnormal on postoperative one month. Conclusions: TSH level of postoperative one month could be used to predict prognosis and monitor recurrence.

Elimination of blinding trachoma in China

OBJECTIVE

To present the change in the prevalence of blindness caused by trachoma between 1987 and 2006 by secondary data analysis based on two China National Sample Surveys on Disability (CNSSD).

METHODS

Secondary data analysis was performed on two China National Sample Surveys on Disability (CNSSD), which were national representative household surveys conducted in 1987 and 2006. The prevalence of blindness caused by trachoma was estimated by 10-year age group. In addition, the proportion of various causes of blindness was evaluated. The geographical distribution of blindness caused by trachoma both in 1987 and 2006 was analyzed in order to visualize the hot spots of blinding trachoma in China.

RESULTS

The prevalence of blindness caused by trachoma in China decreased from 51.5/100,000 in 1987 to 17.6/100,000 in 2006. In addition, the proportion of blindness attributed to trachoma also decreased from 10.1% (1987) to 0.9% (2006). Moreover, the prevalence of blindness caused by trachoma was over 200/100,000 in 2.2% of sampled counties in 2006 as compared to 8.6% in 1987. The hot spots of blinding trachoma were shown to be limited to underdeveloped mountain areas in Hubei and Guizhou provinces.

CONCLUSION

Although blinding trachoma is no longer the leading cause of blindness in China since the 2000's, the prevalence of trachoma should still be monitored in some underdeveloped mountain areas. Therefore, health organization must continue to fight against blinding trachoma in underdeveloped areas.

Technical advances in proteomics: new developments in data-independent acquisition

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

Kaleidoscope of quantum phases in a long-range interacting spin-1 chain

Motivated directly by recent trapped-ion quantum simulation experiments, we carry out a comprehensive study of the phase diagram of a spin-1 chain with XXZ-type interactions that decay as 1/r^α , using a combination of finite and infinite-size DMRG calculations, spin-wave analysis, and field theory. In the absence of long-range interactions, varying the spin-coupling anisotropy leads to four distinct and well-studied phases: a ferromagnetic Ising phase, a disordered XY phase, a topological Haldane phase, and an antiferromagnetic Ising phase. If long-range interactions are antiferromagnetic and thus frustrated, we find primarily a quantitative change of the phase boundaries. On the other hand, ferromagnetic (nonfrustrated) long-range interactions qualitatively impact the entire phase diagram. Importantly, for α ≲ 3 long-range interactions destroy the Haldane phase, break the conformal symmetry of the XY phase, give rise to a new phase that spontaneously breaks a U(1) continuous symmetry, and introduce a possibly exotic tricritical point with no direct parallel in short-range interacting spin chains. Importantly, we show that the main signatures of all five phases found could be observed experimentally in the near future.

Topological phases with long-range interactions

Topological phases of matter are primarily studied in systems with short-range interactions. In nature, however, nonrelativistic quantum systems often exhibit long-range interactions. Under what conditions topological phases survive such interactions, and how they are modified when they do, is largely unknown. By studying the symmetry-protected topological phase of an antiferromagnetic spin-1 chain with 1/r α interactions, we show that two very different outcomes are possible, depending on whether or not the interactions are frustrated. While unfrustrated long-range interactions can destroy the topological phase for α ≲ 3, the topological phase survives frustrated interactions for all α > 0. Our conclusions are based on strikingly consistent results from large-scale matrix-product-state simulations and effective-field-theory calculations, and we expect them to hold for more general interacting spin systems. The models we study can be naturally realized in trapped-ion quantum simulators, opening the prospect for experimental investigation of the issues confronted here.

Evaluation of Immune Profiles and MicroRNA Expression Profiles in Peripheral Blood Mononuclear Cells of Long-Term Stable Liver Transplant Recipients and Recipients With Acute Rejection Episodes

OBJECTIVE

This study aimed to document the difference of immunophenotypes in peripheral blood mononuclear cells (PBMCs) between long-term stable liver transplant recipients and recipients with acute rejection. We also sought to identify whether there is any correlation between microRNA (miRNA) expression profile and the differential immunoprofile in these 2 groups to establish a specific miRNA biomarker to identify potential liver transplant recipients.

METHODS

PBMCs were isolated from 53 stable liver transplant recipients (STA group) and 15 liver transplant recipients with repeated biopsy-proven rejection episodes admitted to our hospital. Immunoprofiles were analyzed by means of flow cytometry. Analysis of miRNA expression in the PBMCs was performed by means of real-time polymerase chain reaction.

RESULTS

The immune profiling analysis showed increased frequency of peripheral natural killer cells and regulatory T cells in stable liver transplant recipients compared with the acute rejection recipients and healthy volunteers (P < .05). There was no significant difference in the immune cell levels (CD19(+) B cells, CD4(+) T cells, and CD8(+) T cells) in PBMCs among the transplant recipient groups and healthy control subjects. Three miRNAs, miR-18b, miR-340, and miR-106b, were up-regulated in the PBMCs of the STA recipients compared with recipients with acute rejection.

CONCLUSIONS

These results suggest that miR-18b, miR-340, and miR-106b, which regulate the expression of specific immunophenotypes, can be used as potential biomarkers to identify long-term stable liver transplant recipients from recipients with acute rejection.

Inferring clonal composition from multiple sections of a breast cancer

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.

Cancers arise from a series of mutations that occur over time. As a result, as a tumor grows each cell inherits a distinctive genotype, defined by the set of all somatic mutations that distinguish the tumor cell from normal cells. Acertaining these genotype patterns, and identifying which ones are associated with the growth of the cancer and its ability to metastasize, can potentially give clinicians insights into how to treat the cancer. In this work, we describe a method for inferring the predominant genotypes within a single tumor. The method requires that a tumor be sectioned and that each section be subjected to a high-throughput sequencing procedure. The resulting mutations and their associated frequencies within each tumor section are then used as input to a probabilistic model that infers the underlying genotypes and their relative frequencies within the tumor. We use simulated data to demonstrate the validity of the approach, and then we apply our algorithm to data from a primary breast cancer and associated metastatic lymph node. We demonstrate that our algorithm predicts genotypes that are consistent with an evolutionary model and with the physical topology of the tumor itself. Applying this method to larger numbers of tumors should cast light on the evolution of cancers in space and time.