PROSE: phenotype-specific network signatures from individual proteomic samples.

Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

ProJect: a powerful mixed-model missing value imputation method.

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.

Serological response and immune-related adverse events following COVID-19 vaccination in cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis.

With the popularity of Coronavirus disease 2019 (COVID-19) vaccine and the development of vaccination strategies, the impact of COVID-19 vaccine on cancer patients receiving immune checkpoint inhibitors (ICIs) is still unclear. In the systematic review and meta-analysis of patients with ICIs, we assessed the serological response of cancer patients receiving COVID-19 vaccine, and explored the risk of immune related adverse events (irAEs). We searched PubMed, EMBASE and Cochrane Library as of 10 June 2023, and included cancer patients who received ICIs and COVID-19 vaccine. The systematic review and meta-analysis include cohort study, cross-sectional study and case report. The outcome included the serological response, Spike-specific T-cell response, irAEs and rare adverse events. When possible, the data were analysed by random effect analysis, and the statistical heterogeneity was assessed by Q-test and I2 statistics. We explored the sources of heterogeneity through L'Abbe plots, Galbraith radial plots, and sensitivity analysis. The publication bias was evaluated by Egger's, Begg's linear regression test and funnel plot, and the impact of publication bias was further analysed by trim and fill method. 27 studies were eligible (19 cohort studies, 1 cross-sectional study and 7 case reports), involving 8331 patients (with 4724 receiving ICIs). Most studies used mRNA vaccine (BNT162b2 or mRNA-1273). Compared with cancer patients receiving chemotherapy, cancer patients receiving ICIs were significantly more likely to have seroconversion (RR = 1.05, 95%CI 1.01-1.10, P = 0.02). There were no statistically significant differences in seroconversion rates when comparing cancer patients receiving ICIs with controls without cancer (RR = 0.95, 95% CI 0.89-1.01, P = 0.09) or with cancer patients receiving targeted therapy (RR = 1.05, 95% CI 0.79-1.39, P = 0.75). The incidence of irAEs in patients receiving ICIs before and after COVID-19 vaccination was (21.96%, 95%CI 16.66%-28.94%) and (14.88%, 95%CI 8.65%-25.57%), respectively. The most common irAEs were endocrine abnormalities, skin disorders, etc. The certainty of evidence was low in cancer patients with ICIs, compared with those receiving chemotherapy, and very low versus controls without cancer. Cancer patients treated with ICIs seem to be able to receive COVID-19 vaccine safely without increasing the incidence of irAEs.

Optimizing the PROTREC network-based missing protein prediction algorithm.

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Treatment of autosomal dominant hearing loss by in vivo delivery of genome editing agents.

Although genetic factors contribute to almost half of all cases of deafness, treatment options for genetic deafness are limited. We developed a genome-editing approach to target a dominantly inherited form of genetic deafness. Here we show that cationic lipid-mediated in vivo delivery of Cas9-guide RNA complexes can ameliorate hearing loss in a mouse model of human genetic deafness. We designed and validated, both in vitro and in primary fibroblasts, genome editing agents that preferentially disrupt the dominant deafness-associated allele in the Tmc1 (transmembrane channel-like gene family 1) Beethoven (Bth) mouse model, even though the mutant Tmc1Bth allele differs from the wild-type allele at only a single base pair. Injection of Cas9-guide RNA-lipid complexes targeting the Tmc1Bth allele into the cochlea of neonatal Tmc1Bth/+ mice substantially reduced progressive hearing loss. We observed higher hair cell survival rates and lower auditory brainstem response thresholds in injected ears than in uninjected ears or ears injected with control complexes that targeted an unrelated gene. Enhanced acoustic startle responses were observed among injected compared to uninjected Tmc1Bth/+ mice. These findings suggest that protein-RNA complex delivery of target gene-disrupting agents in vivo is a potential strategy for the treatment of some types of autosomal-dominant hearing loss.

Reversed-engineered human alveolar lung-on-a-chip model.

Here, we present a physiologically relevant model of the human pulmonary alveoli. This alveolar lung-on-a-chip platform is composed of a three-dimensional porous hydrogel made of gelatin methacryloyl with an inverse opal structure, bonded to a compartmentalized polydimethylsiloxane chip. The inverse opal hydrogel structure features well-defined, interconnected pores with high similarity to human alveolar sacs. By populating the sacs with primary human alveolar epithelial cells, functional epithelial monolayers are readily formed. Cyclic strain is integrated into the device to allow biomimetic breathing events of the alveolar lung, which, in addition, makes it possible to investigate pathological effects such as those incurred by cigarette smoking and severe acute respiratory syndrome coronavirus 2 pseudoviral infection. Our study demonstrates a unique method for reconstitution of the functional human pulmonary alveoli in vitro, which is anticipated to pave the way for investigating relevant physiological and pathological events in the human distal lung.

The Integration of Transcriptome and Metabolome Analyses Provides Insights into the Determinants of the Wood Properties in Toona ciliata.

Toona ciliata, also known as Chinese mahogany, is a high-quality and fast-growing wood species with a high economic value. The wood properties of T. ciliata of different provenances vary significantly. In this study, we conducted comprehensive transcriptome and metabolome analyses of red and non-red T. ciliata wood cores of different provenances to compare their wood properties and explore the differential metabolites and genes that govern the variation in their wood properties. Through combined analyses, three differential genes and two metabolites were identified that are possibly related to lignin synthesis. The lignin content in wood cores from T. ciliata of different provenances shows significant variation following systematic measurement and comparisons. The gene Tci09G002190, one of the three differential genes, was identified as a member of the CAD (Cinnamyl alcohol dehydrogenase) gene family of T. ciliata, which is associated with lignin synthesis. Our data provide insights into the determinants of the wood properties in T. ciliata, providing a solid foundation for research into the subsequent mechanisms of the formation of T. ciliata wood.

Association of Hearing Status and Cognition With Fall Among the Oldest-Old Chinese: A Nationally Representative Cohort Study.

OBJECTIVES: The oldest-old (aged ≥80 years) are the most rapidly growing population and age is related to hearing impairment (HI) and cognitive decline. We aimed to estimate the association between HI and fall, and the effect of different cognitive states on this association among the oldest-old Chinese population. DESIGN: A total of 6931 Chinese oldest-old were included in the 2018 cross-cohort from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The presence of HI was identified by using a dichotomized metric of self-reported hearing status. Cognitive function was evaluated by using the modified Mini-Mental State Examination (MMSE). Cognitive impairment was defined as the MMSE score below 24 points. Data on fall history were collected by questionnaires survey from the participants or their relatives. We studied the association of hearing status and cognitive function with fall by using multivariable logistic regressions, upon adjustment of sociodemographic characteristics, lifestyles, and health conditions. RESULTS: Our participants were aged 92 (range 80 to 117) on average, with 60.1% being women. In total, 39.1% of the participants had reported HI, 50.1% had cognitive impairment, and 26.2% had a history of falling. Participants with HI had a higher incidence of cognitive impairment (79.4%), as compared with their counterparts without HI (31.3%). Compared with those without HI, HI patients had a higher risk of falling after full adjustment for potential confounders (OR = 1.16 [95% confidence interval, CI, 1.01, 1.32], p = 0.031). In comparison with HI participants without cognitive impairment, HI patients with cognitive impairment had a higher fall risk (OR = 1.45 [95% CI = 1.23, 1.72], p < 0.001). CONCLUSIONS: Association of hearing status and cognition with fall was, for the first time, examined on the basis of a nationally-representative oldest-old Chinese population. Poor cognitive performance was common in individuals with HI, and those with HI and cognitive impairment further increased the risk of falling.

Residential proximity to major roadways and hearing impairment in Chinese older adults: a population-based study.

BACKGROUND: With rapid urban sprawl, growing people are living in the vicinity of major roadways. However, little is known about the relationship between residential proximity to major roadways and hearing impairment (HI). METHODS: We derived data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey, and included 13,775 participants aged 65 years or older. Multivariate logistic regressions were employed to examine the association between residential proximity to major roadways and HI. The effects of corresponding potentially modifiable factors were studied by three-way interaction analyses. Sensitivity analyses were performed to verify the robustness of the results. RESULTS: The prevalence of HI was 38.3%. Participants living near major roadways were more likely to have a higher socioeconomic status. An exposure-response relation between residential proximity to major roadways and HI was observed (Ptrend < 0.05). Compared with individuals living > 300 m away from major roadways, the adjusted odds ratios (OR) were 1.07 (95% CI: 0.96-1.24), 1.15 (95% CI: 1.07-1.34), and 1.12 (95% CI: 1.01-1.31) for those living 101-200 m, 50-100 m, and < 50 m away from the roadways, respectively. Particularly, the association was more pronounced among individuals exposed to carbon monoxide (CO) pollution or opening windows frequently (Pinteraction < 0.05). Three-way interaction analyses confirmed that participants exposed to CO pollution and frequently leaving windows open had the highest OR of 1.73 (95% CI: 1.58-1.89). CONCLUSIONS: This nation-wide cohort study suggested that residential proximity to major roadways was significantly associated with an increased exposure-response risk of HI in Chinese older adults. Exposure to CO pollution and opening windows frequently might strengthen the relations.

Dealing with missing values in proteomics data.

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.