Population genomic analyses of protected incense trees Aquilaria sinensis reveal the existence of genetically distinct subpopulations

The incense tree Aquilaria sinensis (Thymelaeaceae) can produce agarwood with commercial values and is now under threat from illegal exploitation in Hong Kong, impairing the local population and biodiversity. Together with other species of Aquilaria, it is listed in the CITES Appendix II, which strictly regulates its international trade. To understand the population structure of A. sinensis and to make relevant conservation measures, we have sequenced 346 individuals collected in Hong Kong and southern mainland China. Population genomic analyses including principal component analysis, neighbor-joining tree construction, ADMIXTURE, and hierarchical pairwise-FST analyses suggested that genetically distinct populations are contained in certain areas. Genomic scan analyses further detected single-nucleotide polymorphism (SNP) outliers related to plant defense, including the CYP71BE gene cluster. In addition to the population analyses, we have developed a modified hexadecyltrimethyl-ammonium bromide (CTAB) DNA extraction protocol for obtaining DNA from agarwood samples in this study, and resequencing of DNA extracted from two agarwood samples using this method allows us to successfully map to the sample corresponding localities in the phylogenetic tree. To sum up, this study suggested that there is a genetically distinct subpopulation of incense tree in Hong Kong that would require special conservation measures and established a foundation for future conservation measures

The distinctive gastric fluid proteome in gastric cancer reveals a multi-biomarker diagnostic profile

<p>Abstract</p> <p>Background</p> <p>Overall gastric cancer survival remains poor mainly because there are no reliable methods for identifying highly curable early stage disease. Multi-protein profiling of gastric fluids, obtained from the anatomic site of pathology, could reveal diagnostic proteomic fingerprints.</p> <p>Methods</p> <p>Protein profiles were generated from gastric fluid samples of 19 gastric cancer and 36 benign gastritides patients undergoing elective, clinically-indicated gastroscopy using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry on multiple ProteinChip arrays. Proteomic features were compared by significance analysis of microarray algorithm and two-way hierarchical clustering. A second blinded sample set (24 gastric cancers and 29 clinically benign gastritides) was used for validation.</p> <p>Results</p> <p>By significance analysyis of microarray, 60 proteomic features were up-regulated and 46 were down-regulated in gastric cancer samples (<it>p </it>< 0.01). Multimarker clustering showed two distinctive proteomic profiles independent of age and ethnicity. Eighteen of 19 cancer samples clustered together (sensitivity 95%) while 27/36 of non-cancer samples clustered in a second group. Nine non-cancer samples that clustered with cancer samples included 5 pre-malignant lesions (1 adenomatous polyp and 4 intestinal metaplasia). Validation using a second sample set showed the sensitivity and specificity to be 88% and 93%, respectively. Positive predictive value of the combined data was 0.80. Selected peptide sequencing identified pepsinogen C and pepsin A activation peptide as significantly down-regulated and alpha-defensin as significantly up-regulated.</p> <p>Conclusion</p> <p>This simple and reproducible multimarker proteomic assay could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy for gastric cancer and pre-malignant lesions.</p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Identification of serum amyloid A protein as a potentially useful biomarker for nasopharyngeal carcinoma (multiple letters)

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ProteinChip array profiling for identification of disease- and chemotherapy-associated biomarkers of nasopharyngeal carcinoma

Background: We previously used ProteinChip array profiling analysis to discover a serum biomarker associated with nasopharyngeal carcinoma (NPC). In this study, we used the same method to examine other biomarkers associated with NPC and response to chemotherapy (CT) in NPC patients. Methods: We performed ProteinChip array analysis in 209 serum samples from 66 relapsed patients before and after salvage CT with gemcitabine and cisplatin or etoposide and cisplatin combinations, 11 patients in remission, and 35 healthy individuals. Intensities of the biomarker peaks were correlated with CT response of the patients and other clinical parameters. Results: We discovered 13 candidate biomarkers associated with different clinical parameters. Two biomarkers (2803 and 3953 Da) were significantly increased in patients compared with controls at all stages of disease. Analysis of pre- and post-CT paired serum samples revealed 7 biomarkers correlated with impact of CT. Of these 7 biomarkers, 2 (2509 and 2756 Da) were significantly increased and 5 (7588, 7659, 7765, 7843, and 8372 Da) were significantly decreased post-CT in either 1 or both CT cohorts. Four biomarkers from pre-CT sera were correlated with CT response, with 3 (2950, 13 510, and 14 855 Da) being significantly decreased and 1 (6701 Da) significantly increased in patients who did not respond to CT. Tandem mass spectrometric sequencing and/or immunoaffinity capture assay identified the 3953 Da biomarker as a fragment of intera-trypsin inhibitor precursor and 7765 Da biomarker as platelet factor-4. Conclusions: Treatment-associated serum biomarkers found might serve to triage NPC patients for appropriate CT treatment. © 2007 American Association for Clinical Chemistry.Link_to_subscribed_fulltex

Identification of Serum Amyloid A Protein As a Potentially Useful Biomarker to Monitor Relapse of Nasopharyngeal Cancer by Serum Proteomic Profiling

Purpose: Nasopharyngeal cancer (NPC) is a common cancer in Hong Kong, and relapse can occur frequently. Using protein chip profiling analysis, we aimed to identify serum biomarkers that were useful in the diagnosis of relapse in NPC. Experimental Design: Profiling analysis was performed on 704 sera collected from 42 NPC patients, 39 lung cancer patients, 30 patients with the benign metabolic disorder thyrotoxicosis (TX), and 35 normal individuals (NM). Protein profile in each NPC patient during clinical follow up was correlated with the relapse status. Results: Profiling analysis identified two biomarkers with molecular masses of 11.6 and 11.8 kDa, which were significantly elevated in 22 of 31 (71%) and 21 of 31 (68%) NPC patients, respectively, at the time of relapse (RP) as compared with 11 patients in complete remission (CR; RP versus CR, P = 0.009), 30 TX (RP versus TX, P < 0.001), or 35 NM (RP versus NM, P < 0.001). The markers were also elevated in 16 of 39 (41%) lung cancer patients at initial diagnosis. By tryptic digestion, followed by tandem mass spectrometry fragmentation, the markers were identified as two isoforms of serum amyloid A (SAA) protein. Monitoring the patients longitudinally for SAA level both by protein chip and immunoassay showed a dramatic SAA increase, which correlated with relapse and a drastic fall correlated with response to salvage chemotherapy. Serum SAA findings were compared with those of serum Epstein-Barr virus DNA in three relapsed patients showing a similar correlation with relapse and chemo-response. Conclusions: SAA could be a useful biomarker to monitor relapse of NPC.Link_to_subscribed_fulltex