Modern microbial mats can provide key insights into early Earth ecosystems, and Shark Bay, Australia, holds one of the best examples of these systems. Identifying the spatial distribution of microorganisms with mat depth facilitates a greater understanding of specific niches and potentially novel microbial interactions. High throughput sequencing coupled with elemental analyses and biogeochemical measurements of two distinct mat types (smooth and pustular) at a millimeter scale were undertaken in the present study. A total of 8,263,982 16S rRNA gene sequences were obtained, which were affiliated to 58 bacterial and candidate phyla. The surface of both mats were dominated by Cyanobacteria, accompanied with known or putative members of Alphaproteobacteria and Bacteroidetes. The deeper anoxic layers of smooth mats were dominated by Chloroflexi, while Alphaproteobacteria dominated the lower layers of pustular mats. In situ microelectrode measurements revealed smooth mats have a steeper profile of O2 and H2S concentrations, as well as higher oxygen production, consumption, and sulfate reduction rates. Specific elements (Mo, Mg, Mn, Fe, V, P) could be correlated with specific mat types and putative phylogenetic groups. Models are proposed for these systems suggesting putative surface anoxic niches, differential nitrogen fixing niches, and those coupled with methane metabolism.

Saccharomyces cerevisiae has the most comprehensively characterized protein–protein interaction network, or interactome, of any eukaryote. This has predominantly been generated through multiple, systematic studies of protein–protein interactions by two-hybrid techniques and of affinity-purified protein complexes. A pressing question is to understand how large-scale cross-linking mass spectrometry (XL-MS) can confirm and extend this interactome. Here, intact yeast nuclei were subject to cross-linking with disuccinimidyl sulfoxide (DSSO) and analyzed using hybrid MS2-MS3 methods. XlinkX identified a total of 2,052 unique residue pair cross-links at 1% FDR. Intraprotein cross-links were found to provide extensive structural constraint data, with almost all intralinks that mapped to known structures and slightly fewer of those mapping to homology models being within 30 Å. Intralinks provided structural information for a further 366 proteins. A method for optimizing interprotein cross-link score cut-offs was developed, through use of extensive known yeast interactions. Its application led to a high confidence, yeast nuclear interactome. Strikingly, almost half of the interactions were not previously detected by two-hybrid or AP-MS techniques. Multiple lines of evidence existed for many such interactions, whether through literature or ortholog interaction data, through multiple unique interlinks between proteins, and/or through replicates. We conclude that XL-MS is a powerful means to measure interactions, that complements two-hybrid and affinity-purification techniques.

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Introduction: Pancreatic cancer has a very poor prognosis, with no established prognostic biomarkers in clinical use. This project aims to identify a prognostic proteomic-based signature for pancreatic adenocarcinomas. Methods: Fresh frozen tumors and matched normal samples from 125 patients were prepared for proteomic analyses using data-independent acquisition mass spectrometry (DIA-MS). Differential expression analyses were conducted on the normalized protein matrix to identify the top differentially expressed proteins (DEP) within the tumor samples. DEP were subjected to crosstalk and pathway enrichment analysis (PEA). Survival analysis based on initial univariate and subsequent 100 runs of multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) was performed to obtain a reduced list of candidate proteins associated with Overall Survival (OS). The proteins that appeared in greater than 95% of the LASSO runs were then used in a multivariate Cox model with recursive feature selection, which yielded the final 29 proteins. A risk score was built from the final 29 proteins. Consensus clustering was performed on the median absolute deviation-based top 20% highly variable proteins in tumor samples to identify proteomic-based subtypes. Results: Proteomic analyses revealed 5614 proteins identified from 599 sample runs. Differential expression analyses revealed 398 DEP in tumor samples (FDR-adjusted p-value <0.05, and |logFC|>1). PEA showed that these proteins were related to focal adhesion, extracellular matrix interaction (ECM), angiogenesis, and PI3K signaling pathways. A total of 803 proteins were significantly associated with OS in a univariate Cox regression analysis (p<0.05). PEA on the top 200 proteins associated with poorer OS revealed pathways related to focal adhesion, PI3K signaling, ECM and hypoxia-induced factor-1. Using LASSO multivariate Cox regression modeling, a 29-protein signature was identified, from which a risk score was calculated that dichotomized patients into high- and low-risk groups in terms of OS (Hazard ratio (HR) 2.8, 95% Confidence Interval (CI) [2.3, 3.3], concordance index of 0.91). This risk score was also prognostic for recurrence and three-year survival (both p<0.0001). A multivariate Cox regression model adjusted for other clinical variables revealed a significant association of the risk score with OS (HR 2.91, 95% CI [2.4, 3.5], p<0.001) while maintaining the concordance index (0.907). Consensus clustering analyses revealed four proteomic-based clusters, with cluster 3 showing the worst OS (p<0.001), independent of other clinical variables. PEA on the DEP within cluster 3 showed upregulation of proteins related to cell adhesion, angiogenesis, and immune-related pathways. Conclusion: A 29-protein signature identified a sub-group of patients with pancreatic adenocarcinoma with a poorer prognosis independent of clinical variables. Citation Format: Adel T. Aref, AKM Azad, Asim Anees, Mohashin Pathan, Jason Grealey, Daniela-Lee Smith, Erin M. Humphries, Daniel Bucio-Noble, Jennifer M. Koh, Erin Sykes, Steven G. Williams, Ruth Lyons, Natasha Lucas, Dylan Xavier, Sumit Sahni, Anubhav Mittal, Jaswinder S. Samra, John V. Pearson, Nicola Waddell, Peter G. Hains, Phil J. Robinson, Qing Zhong, Roger R. Reddel, Anthony J. Gill. A proteomic-based prognostic signature of pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2209.