Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is estimated to affect 30% of the world's population, and its prevalence is increasing in line with obesity. Liver fibrosis is closely related to mortality, making it the most important clinical parameter for MASLD. It is currently assessed by liver biopsy - an invasive procedure that has some limitations. There is thus an urgent need for a reliable non-invasive means to diagnose earlier MASLD stages. METHODS: A discovery study was performed on 158 plasma samples from histologically-characterised MASLD patients using mass spectrometry (MS)-based quantitative proteomics. Differentially abundant proteins were selected for verification by ELISA in the same cohort. They were subsequently validated in an independent MASLD cohort (n = 200). RESULTS: From the 72 proteins differentially abundant between patients with early (F0-2) and advanced fibrosis (F3-4), we selected Insulin-like growth factor-binding protein complex acid labile subunit (ALS) and Galectin-3-binding protein (Gal-3BP) for further study. In our validation cohort, AUROCs with 95% CIs of 0.744 [0.673 - 0.816] and 0.735 [0.661 - 0.81] were obtained for ALS and Gal-3BP, respectively. Combining ALS and Gal-3BP improved the assessment of advanced liver fibrosis, giving an AUROC of 0.796 [0.731. 0.862]. The {ALS; Gal-3BP} model surpassed classic fibrosis panels in predicting advanced liver fibrosis. CONCLUSIONS: Further investigations with complementary cohorts will be needed to confirm the usefulness of ALS and Gal-3BP individually and in combination with other biomarkers for diagnosis of liver fibrosis. With the availability of ELISA assays, these findings could be rapidly clinically translated, providing direct benefits for patients.

Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters.

In proteomic differential analysis, FDR control is often performed through a multiple test correction (i.e., the adjustment of the original p-values). In this protocol, we apply a recent and alternative method, based on so-called knockoff filters. It shares interesting conceptual similarities with the target-decoy competition procedure, classically used in proteomics for FDR control at peptide identification. To provide practitioners with a unified understanding of FDR control in proteomics, we apply the knockoff procedure on real and simulated quantitative datasets. Leveraging these comparisons, we propose to adapt the knockoff procedure to better fit the specificities of quantitative proteomic data (mainly very few samples). Performances of knockoff procedure are compared with those of the classical Benjamini-Hochberg procedure, hereby shedding a new light on the strengths and weaknesses of target-decoy competition.

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.

Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Challenging Targets or Describing Mismatches? A Comment on Common Decoy Distribution by Madej et al.

In their recent article, Madej et al. (Madej, D.; Wu, L.; Lam, H.Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics. J. Proteome Res.2022, 21 (2), 339-348) proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-only search. Conceptually, this approach is appealing as it takes the best of two worlds, i.e., decoy-based approaches (which leverage a large-scale collection of empirical mismatches) and decoy-free approaches (which are not subject to the randomness of decoy generation while sparing an additional database search). Interestingly, CDD also corresponds to a middle-of-the-road approach in statistics with respect to the two main families of FDR control procedures: Although historically based on estimating the false-positive distribution, FDR control has recently been demonstrated to be possible thanks to competition between the original variables (in proteomics, target sequences) and their fictional counterparts (in proteomics, decoys). Discriminating between these two theoretical trends is of prime importance for computational proteomics. In addition to highlighting why proteomics was a source of inspiration for theoretical biostatistics, it provides practical insights into the improvements that can be made to FDR control methods used in proteomics, including CDD.

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics.

BACKGROUND: Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach. RESULTS: We establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible. CONCLUSIONS: In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications.

CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis.

BACKGROUND: The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. RESULTS: We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. CONCLUSIONS: Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics.

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Comprehensive and comparative exploration of the Atp7b-/- mouse plasma proteome.

Wilson's disease (WD), a rare genetic disease caused by mutations in the ATP7B gene, is associated with altered expression and/or function of the copper-transporting ATP7B protein, leading to massive toxic accumulation of copper in the liver and brain. The Atp7b-/- mouse, a genetic and phenotypic model of WD, was developed to provide new insights into the pathogenic mechanisms of WD. Many plasma proteins are secreted by the liver, and impairment of liver function can trigger changes to the plasma proteome. High standard proteomics workflows can identify such changes. Here, we explored the plasma proteome of the Atp7b-/- mouse using a mass spectrometry (MS)-based proteomics workflow combining unbiased discovery analysis followed by targeted quantification. Among the 367 unique plasma proteins identified, 7 proteins were confirmed as differentially abundant between Atp7b-/- mice and wild-type littermates, and were directly linked to WD pathophysiology (regeneration of liver parenchyma, plasma iron depletion, etc.). We then adapted our targeted proteomics assay to quantify human orthologues of these proteins in plasma from copper-chelator-treated WD patients. The plasma proteome changes observed in the Atp7b-/- mouse were not confirmed in these samples, except for alpha-1 antichymotrypsin, levels of which were decreased in WD patients compared to healthy individuals. Plasma ceruloplasmin was investigated in both the Atp7b-/- mouse model and human patients; it was significantly decreased in the human form of WD only. In conclusion, MS-based proteomics is a method of choice to identify proteome changes in murine models of disrupted metal homeostasis, and allows their validation in human cohorts.

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

Two new genera of hard ticks, Robertsicus n. gen. and Archaeocroton n. gen., and the solution to the mystery of Hoogstraal's and Kaufman's "primitive" tick from the Carpathian Mountains.

We establish two new monotypic genera, Robertsicus n. gen. and Archaeocroton n. gen., based on two enigmatic species of Amblyomma. Robertsicus, with the type species R. elaphensis (Price, 1959) n. comb., is proposed for Amblyomma       elaphense, the tick of the Trans-Pecos rat-snake of the Chihuahuan Desert of Mexico and southeastern USA (parts of Arizona, West Texas and New Mexico). Archaeocroton, with the type species Ar. sphenodonti (Dumbleton, 1943) n. comb., is proposed for Amblyomma sphenodonti, the tuatara tick of New Zealand. This is another step in the resolution of the systematic problems concerning the genus Aponomma, a heterogeneous group of eyeless ticks with a predilection for reptiles. By removing R. elaphensis (Price, 1959) and Ar. sphenodonti (Dumbleton, 1943) from the genus Amblyomma we have resolved, for now at least, the polyphyly of the genus Amblyomma. We have also resolved a 50-year old mystery: the identity of Hoogstraal's and Kaufman's "primitive" tick from the Carpathian Mountains that had a "striking resemblance" to Ar. sphenodonti n. comb.; it was an Ar. sphenodonti n. comb. that had travelled to the Carpathian       Mountains from New Zealand on a captive tuatara.

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: samuel.wieczorek@cea.fr, florence.combes@cea.fr, thomas.burger@cea.fr.

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

Phylogenetic analysis of the mitochondrial genomes and nuclear rRNA genes of ticks reveals a deep phylogenetic structure within the genus Haemaphysalis and further elucidates the polyphyly of the genus Amblyomma with respect to Amblyomma sphenodonti and Amblyomma elaphense.

We sequenced the entire mitochondrial genomes of 3 species of metastriate ticks: Haemaphysalis formosensis, H. parva, and Amblyomma cajennense. We also sequenced two thirds (ca. 9500bp) of the mitochondrial genomes of H. humerosa and H. hystricis. We used these 5 mitochondrial genome sequences together with the 13 tick mitochondrial genomes we sequenced previously and the 2 tick mitochondrial genomes sequenced by Black and Roehrdanz (1998), as well as the nuclear rRNA genes from 84 ticks and mites, in phylogenetic analyses. Our analyses reveal deep phylogenetic structure within the genus Haemaphysalis, with at least 2 species, H. parva and H. inermis that are highly divergent from the rest of the genus Haemaphysalis. We identify a region of the 18S rRNA gene which correlates with this division of the genus Haemaphysalis as well as a novel insertion in the mitochondrial genome of H. parva. We reject the hypotheses of Hoogstraal and Aeschlimann (1982) and Barker and Murrell (2004) on the relationships among metastriate genera. Instead, our analysis provides further evidence for the division of the Metastriata into 2 major lineages: (i) Amblyomma s.s. plus Rhipicephalinae (i.e. Rhipicephalus, Hyalomma, Rhipicentor, and Dermacentor); and (ii) Haemaphysalis plus Bothriocroton plus Amblyomma sphenodonti. We also provide further evidence for the polyphyly of the genus Amblyomma with respect to A. sphenodonti and A. elaphense. The most likely position of A. elaphense is sister to the rest of the Metastriata; the most likely position of A. sphenodonti is sister to the genus Bothriocroton. These 2 species do not belong in the genus Amblyomma, and we propose that new genera are required for A. sphenodonti and A. elaphense.

Phylogenetic analysis of ticks (Acari: Ixodida) using mitochondrial genomes and nuclear rRNA genes indicates that the genus Amblyomma is polyphyletic.

Our understanding of the phylogenetic relationships among tick lineages has been limited by the lack of resolution provided by the most commonly used phylogenetic markers. Mitochondrial genomes are increasingly used to address controversial phylogenetic relationships. To date, the complete mitochondrial genomes of eleven tick species have been sequenced; however, only three of these species are metastriate ticks, the most speciose lineage of ticks. In this study, we present the nucleotide sequences of the complete mitochondrial genomes of five more species of metastriate ticks: Amblyomma elaphense, Amblyomma fimbriatum, Amblyomma sphenodonti, Bothriocroton concolor and Bothriocroton undatum. We use complete mitochondrial genome sequences to address the phylogenetic placement of two morphologically 'primitive' species -Am. elaphense and Am. sphenodonti - with respect to the genus Amblyomma. Our analysis of these five mitochondrial genomes with the other eleven tick mitochondrial genomes, as well as analysis of nuclear rRNA genes, provides strong evidence that the genus Amblyomma is polyphyletic with the inclusion of Am. sphenodonti and Am. elaphense. A new genus or two new genera may be required to describe Am. sphenodonti and Am. elaphense. It is also possible that these two species are sisters to two established genera, Bothriocroton in the case of Am. sphenodonti, and Haemaphysalis in the case of Am. elaphense. However, other arrangements of these taxa cannot be excluded with the current data. Thus, while Am. sphenodonti and Am. elaphense do not belong in the genus Amblyomma, the phylogenetic placement of these two species cannot be resolved without more data from metastriate ticks, either greater sampling of mitochondrial genomes, or a large data set of nuclear genes.

Mental training in surgical education: a randomized controlled trial.

OBJECTIVE: To evaluate the impact of a cognitive training method on the performance of simulated laparoscopic cholecystectomy in laparoscopic training courses. SUMMARY BACKGROUND DATA: Surgeons are like professional sportsmen in that they have to be able to perform complicated, fine-motor movements under stressful conditions. Mental training, systematically and repeatedly imagining a movement's performance, is a well-established technique in sports science, and this study aimed to determine its value in training surgeons. METHODS: A total of 98 surgeons undergoing basic laparoscopic training participated in a randomized controlled trial; 31 received additional mental training, 32 additional practical training, and 35 received no additional training (control group). All used a Pelvi-Trainer simulator to perform laparoscopic cholecystectomy at baseline and follow-up, after any additional intervention. We used a modified Objective Structured Assessment of Technical Skills (OSATS) instrument to assess performance. Principle outcome variables were the OSATS task-specific checklist (11 procedural steps, scored as correctly [1] or wrongly [0] performed) and the global rating scale (an overall performance evaluation, scored 1-5). RESULTS: Improvement in the task-specific checklist score between baseline and follow-up differed significantly between groups (P = 0.046 on ANOVA). Least significant difference tests yielded differences between the mental and practical training groups (P = 0.024) and between the mental training and control groups (P = 0.040), but not between the practical training and control groups (P = 0.789). Paired Student t test showed that performance at follow-up was significantly better in the mental training and control groups (mental training group, P = 0.001; control group, P = 0.018) but not the practical training group (P = 0.342). There were no significant intergroup differences in global rating scale results. CONCLUSION: Additional mental training is an effective way of optimizing the outcomes of further training for laparoscopic cholecystectomy. It is associated with fewer costs and with better outcomes in some crucial assessment scales than additional practical training.