Metaproteomic analysis from cervical biopsies and cytologies identifies proteinaceous biomarkers representing both human and microbial species.

The detection of HPV infection and microbial colonization in cervical lesions is currently done through PCR-based viral or bacterial DNA amplification. Our objective was to develop a methodology to expand the metaproteomic landscape of cervical disease and determine if protein biomarkers from both human and microbes could be detected in distinct cervical samples. This would lead to the development of multi-species proteomics, which includes protein-based lateral flow diagnostics that can define patterns of microbes and/or human proteins relevant to disease status. In this study, we collected both non-frozen tissue biopsy and exfoliative non-fixed cytology samples to assess the consistency of detecting human proteomic signatures between the cytology and biopsy samples. Our results show that proteomics using biopsies or cytologies can detect both human and microbial organisms. Across patients, Lumican and Galectin-1 were most highly expressed human proteins in the tissue biopsy, whilst IL-36 and IL-1RA were most highly expressed human proteins in the cytology. We also used mass spectrometry to assess microbial proteomes known to reside based on prior 16S rRNA gene signatures. Lactobacillus spp. was the most highly expressed proteome in patient samples and specific abundant Lactobacillus proteins were identified. These methodological approaches can be used in future metaproteomic clinical studies to interrogate the vaginal human and microbiome structure and metabolic diversity in cytologies or biopsies from the same patients who have pre-invasive cervical intraepithelial neoplasia, invasive cervical cancer, as well as in healthy controls to assess how human and pathogenic proteins may correlate with disease presence and severity.

Multi-Omic Analysis of Esophageal Adenocarcinoma Uncovers Candidate Therapeutic Targets and Cancer-Selective Posttranscriptional Regulation.

Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNA-seq and proteomic data from seven patients and further integrated these data with a cohort of EAC RNA-seq data (n = 264 patients), EAC whole-genome sequencing (n = 454 patients), and external published datasets. We quantified protein expression from 5879 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-selective expression were identified, including the transmembrane protein GPA33. We further verified the EAC-enriched expression of GPA33 in an external cohort of 115 patients and confirm this as an attractive diagnostic and therapeutic target. To further extend the insights gained from our proteomic data, an integrated analysis of protein and RNA expression in EAC and normal tissues revealed several genes with poorly correlated protein and RNA abundance, suggesting posttranscriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, only rarely demonstrated somatic mutation, suggesting post-transcriptional drivers for this EAC-specific phenotype. AGMAT was demonstrated to be overexpressed at the protein level in EAC compared to adjacent normal tissues with an EAC-selective, post-transcriptional mechanism of regulation of protein abundance proposed. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-selective, posttranscriptional regulation of protein expression, which may be an exploitable vulnerability.

Biochemical evidence for conformational variants in the anti-viral and pro-metastatic protein IFITM1.

Interferon induced transmembrane proteins (IFITMs) play a dual role in the restriction of RNA viruses and in cancer progression, yet the mechanism of their action remains unknown. Currently, there is no data about the basic biochemical features or biophysical properties of the IFITM1 protein. In this work, we report on description and biochemical characterization of three conformational variants/oligomeric species of recombinant IFITM1 protein derived from an Escherichia coli expression system. The protein was extracted from the membrane fraction, affinity purified, and separated by size exclusion chromatography where two distinct oligomeric species were observed in addition to the expected monomer. These species remained stable upon re-chromatography and were designated as "dimer" and "oligomer" according to their estimated molecular weight. The dimer was found to be less stable compared to the oligomer using circular dichroism thermal denaturation and incubation with a reducing agent. A two-site ELISA and HDX mass spectrometry suggested the existence of structural motif within the N-terminal part of IFITM1 which might be significant in oligomer formation. Together, these data show the unusual propensity of recombinant IFITM1 to naturally assemble into very stable oligomeric species whose study might shed light on IFITM1 anti-viral and pro-oncogenic functions in cells.

N-glycan profiling of tissue samples to aid breast cancer subtyping.

Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.