Biology subjects

Zhan, D.

Publications and source records attributed to Zhan, D..

4 recordsLinked to original sources

bioRxiv2023

'Drifting' Buchnera genomes track the microevolutionary trajectories of their aphid hosts

Evolution of Buchnera-aphid host symbioses is often studied among species at macroevolutionary scales. Investigations within species offer a different perspective about how eco-evolutionary processes shape patterns of genetic variation at microevolutionary scales. Our study leverages new and publicly available whole-genome sequencing data to study Buchnera-aphid host evolution in Myzus persicae, the peach potato aphid, a globally invasive and polyphagous pest. Across 43 different asexual, clonally reproducing isofemale strains, we examined patterns of genomic covariation between Buchnera and their aphid host and considered the distribution of mutations in protein-coding regions of the Buchnera genome. We found Buchnera polymorphisms within aphid strains, suggesting the presence of genetically different Buchnera strains within the same clonal lineage. Genetic distance between pairs of Buchnera samples was positively correlated to genetic distance between their aphid hosts, indicating shared evolutionary histories. However, there was no segregation in the genetic variation of both M. persicae and Buchnera with plant host (Brassicaceae and non-tobacco Solanaceae) and no associations between genetic and geographic distance at global or regional spatial scales. Abundance patterns of non-synonymous mutations were similar to synonymous mutations in the Buchnera genome, and both mutation classes had similar site frequency spectra. We hypothesize that a predominance of neutral processes results in the Buchnera of M. persicae to simply drift with the evolutionary trajectory of their aphid hosts. Our study presents a unique microevolutionary characterisation of Buchnera-aphid host genomic covariation across multiple aphid clones. This provides a new perspective on the eco-evolutionary processes generating and maintaining polymorphisms in a major pest aphid species and its obligate primary endosymbiont.

evolutionary biology↗

bioRxiv2023

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of site harmonization, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2,101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 years and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1,600 participants. These findings have been incorporated into CentileBrain [https://centilebrain.org/#/brainAGE2], an open-science, web-based platform for individualized neuroimaging metrics.

bioinformatics↗

bioRxiv2023

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current TNM or Veterans Administration Lung Study Group (VALG) staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent center, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggest potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better Progression Free Survival (PFS) and Overall Survival (OS) after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

cancer biology↗

bioRxiv2022

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics

Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. We developed the Quartet as a proteome reference material with built-in truths, and distributed the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 MS files were obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There was a wide dynamic range of proteomes spanning ~7 orders of magnitude (101-108 copies/cell), and the injection order had marked effects on quantitative instead of qualitative. Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

molecular biology↗