Mass Spectrometry Analysis of Glycopeptides Enriched by Anion Exchange-Mediated Methods Reveals PolyLacNAc-Extended N-Glycans in Integrins and Tetraspanins in Melanoma Cells.

Glycosylation is a key modulator of the functional state of proteins. Recent developments in large-scale analysis of intact glycopeptides have enabled the identification of numerous glycan structures that are relevant in pathophysiological processes. However, one motif found in N-glycans, poly-N-acetyllactosamine (polyLacNAc), still poses a substantial challenge to mass spectrometry-based glycoproteomic analysis due to its relatively low abundance and large size. In this work, we developed approaches for the systematic mapping of polyLacNAc-elongated N-glycans in melanoma cells. We first evaluated five anion exchange-based matrices for enriching intact glycopeptides and selected two materials that provided better overall enrichment efficiency. We then tested the robustness of the methodology by quantifying polyLacNAc-containing glycopeptides as well as changes in protein fucosylation and sialylation. Finally, we applied the optimal enrichment methods to discover glycopeptides containing polyLacNAc motifs in melanoma cells and found that integrins and tetraspanins are substantially modified with these structures. This study demonstrates the feasibility of glycoproteomic approaches for identification of glycoproteins with polyLacNAc motifs.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Gut microbiome carbon and sulfur metabolisms support Salmonella during pathogen infection.

Salmonella enterica serovar Typhimurium is a pervasive enteric pathogen and an ongoing global threat to public health. Ecological studies in the Salmonella impacted gut remain underrepresented in the literature, discounting the microbiome mediated interactions that may inform Salmonella physiology during colonization and infection. To understand the microbial ecology of Salmonella remodeling of the gut microbiome, here we performed multi-omics approaches on fecal microbial communities from untreated and Salmonella -infected mice. Reconstructed genomes recruited metatranscriptomic and metabolomic data providing a strain-resolved view of the expressed metabolisms of the microbiome during Salmonella infection. This data informed possible Salmonella interactions with members of the gut microbiome that were previously uncharacterized. Salmonella- induced inflammation significantly reduced the diversity of transcriptionally active members in the gut microbiome, yet increased gene expression was detected for 7 members, with Luxibacter and Ligilactobacillus being the most active. Metatranscriptomic insights from Salmonella and other persistent taxa in the inflamed microbiome further expounded the necessity for oxidative tolerance mechanisms to endure the host inflammatory responses to infection. In the inflamed gut lactate was a key metabolite, with microbiota production and consumption reported amongst transcriptionally active members. We also showed that organic sulfur sources could be converted by gut microbiota to yield inorganic sulfur pools that become oxidized in the inflamed gut, resulting in thiosulfate and tetrathionate that supports Salmonella respiration. Advancement of pathobiome understanding beyond inferences from prior amplicon-based approaches can hold promise for infection mitigation, with the active community outlined here offering intriguing organismal and metabolic therapeutic targets.

Sugar-Phosphate Toxicities Attenuate Salmonella Fitness in the Gut.

Pathogens are becoming resistant to antimicrobials at an increasing rate, and novel therapeutic strategies are needed. Using Salmonella as a model, we have investigated the induction of sugar-phosphate toxicity as a potential therapeutic modality. The approach entails providing a nutrient while blocking the catabolism of that nutrient, resulting in the accumulation of a toxic intermediate. We hypothesize that this build-up will decrease the fitness of the organism during infection given nutrient availability. We tested this hypothesis using mutants lacking one of seven genes whose mutation is expected to cause the accumulation of a toxic metabolic intermediate. The araD, galE, rhaD, glpD, mtlD, manA, and galT mutants were then provided the appropriate sugars, either in vitro or during gastrointestinal infection of mice. All but the glpD mutant had nutrient-dependent growth defects in vitro, suggestive of sugar-phosphate toxicity. During gastrointestinal infection of mice, five mutants had decreased fitness. Providing the appropriate nutrient in the animal's drinking water was required to cause fitness defects with the rhaD and manA mutants and to enhance the fitness defect of the araD mutant. The galE and mtlD mutants were severely attenuated regardless of the nutrient being provided in the drinking water. Homologs of galE are widespread among bacteria and in humans, rendering the specific targeting of bacterial pathogens difficult. However, the araD, mtlD, and rhaD genes are not present in humans, appear to be rare in most phyla of bacteria, and are common in several genera of Enterobacteriaceae, making the encoded enzymes potential narrow-spectrum therapeutic targets. IMPORTANCE Bacterial pathogens are becoming increasingly resistant to antibiotics. There is an urgent need to identify novel drug targets and therapeutic strategies. In this work we have assembled and characterized a collection of mutations in our model pathogen, Salmonella enterica, that block a variety of sugar utilization pathways in such a way as to cause the accumulation of a toxic sugar-phosphate. Mutations in three genes, rhaD, araD, and mtlD, dramatically decrease the fitness of Salmonella in a mouse model of gastroenteritis, suggesting that RhaD, AraD, and MtlD may be good narrow-spectrum drug targets. The induction of sugar-phosphate toxicities may be a therapeutic strategy that is broadly relevant to other bacterial and fungal pathogens.

Blastocystis sp. in Small Ruminants: A Universal Systematic Review and Meta-analysis.

PURPOSE: The present review was done to evaluate the prevalence and subtype distribution of Blastocystis infection among small ruminants, at a global perspective. METHODS: Systematic search was performed in PubMed, Scopus, Google Scholar, and Web of Science until 30th January 2022 and total estimates along with 95% confidence intervals (CIs) were computed using a random-effects model. RESULTS: Ultimately, the required data were extracted from 25 papers including 19 datasets for each animal. Among 3125 sheep, the Blastocystis prevalence was 25.3% (95% CI 16.1-37.4%) (10 countries), being lower in comparison to that in 2869 examined goats [20.5% (95% CI 11-35.1%)] (12 countries). Regarding STs distribution, fourteen genetically diverse STs of Blastocystis (ST1-ST5, ST7, ST10, ST12, ST14, ST15, ST21, ST23, ST24, ST26) have been reported in sheep, and the highest pooled prevalence was related to ST10 [11 datasets, 57.8% (95% CI 43.7-70.8%)], followed by ST14 [8 datasets, 28.4% (95% CI 20.2-38.4%)], and ST7 [2 datasets, 21.1% (95% CI 4.5-60.3%)]. Compared to sheep, more STs (ST1, ST3-ST7, ST10, ST12, ST14, ST21, ST23-ST26, and ST32) were reported from goats, and the highest weighted frequency was related to ST10 [6 datasets, 45.1% (95% CI: 25.6-66.2%)], followed by ST7 [2 datasets, 40.4% (95% CI 30-51.7%)], and ST14 [4 datasets, 29% (95% CI 15.5-47.7%)]. Out of ten known zoonotic STs reported for Blastocystis (ST1-ST9, and ST12), 7 were isolated from sheep (ST1-ST5, ST7, and ST12) and 7 were reported from goats (ST1, ST3-ST7, ST12). CONCLUSIONS: Overall, Blastocystis epidemiology in sheep and goats is yet to be elucidated and demands more comprehensive studies.

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

Optimization of proteomics sample preparation for forensic analysis of skin samples.

We present an efficient protein extraction and in-solution enzymatic digestion protocol optimized for mass spectrometry-based proteomics studies of human skin samples. Human skin cells are a proteinaceous matrix that can enable forensic identification of individuals. We performed a systematic optimization of proteomic sample preparation for a protein-based human forensic identification application. Digestion parameters, including incubation duration, temperature, and the type and concentration of surfactant, were systematically varied to maximize digestion completeness. Through replicate digestions, parameter optimization was performed to maximize repeatability and increase the number of identified peptides and proteins. Final digestion conditions were selected based on the parameters that yielded the greatest percent of peptides with zero missed tryptic cleavages, which benefit the analysis of genetically variable peptides (GVPs). We evaluated the final digestion conditions for identification of GVPs by applying MS-based proteomics on a mixed-donor sample. The results were searched against a human proteome database appended with a database of GVPs constructed from known non-synonymous single nucleotide polymorphisms (SNPs) that occur at known population frequencies. The aim of this study was to demonstrate the potential of our proteomics sample preparation for future implementation of GVP analysis by forensic laboratories to facilitate human identification. SIGNIFICANCE: Genetically variable peptides (GVPs) can provide forensic evidence that is complementary to traditional DNA profiling and be potentially used for human identification. An efficient protein extraction and reproducible digestion method of skin proteins is a key contributor for downstream analysis of GVPs and further development of this technology in forensic application. In this study, we optimized the enzymatic digestion conditions, such as incubation time and temperature, for skin samples. Our study is among the first attempts towards optimization of proteomics sample preparation for protein-based skin identification in forensic applications such as touch samples. Our digestion method employs RapiGest (an acid-labile surfactant), trypsin enzymatic digestion, and an incubation time of 16 h at 37 °C.

COVID-19 in Asia: Transmission factors, re-opening policies, and vaccination simulation.

This work aims to provide insights on the COVID-19 pandemic in three prime aspects. First, we attempted to understand the association between the COVID-19 transmission rate, environmental factors (air pollution, weather, mobility), and socio-political parameters (Government Stringency Index, GSI). Second, we evaluated the efficiency of various strategies, including radical opening, intermittent lockdown, phase lift, and contact tracing, to exit the COVID-19 pandemic and get back to pre-pandemic conditions using a stochastic individual-based epidemiology model. Third, we used a deep learning approach and simulated the vaccination rate and the time for reaching herd immunity. The analysis was done based on the collected data from eight countries in Asia, including Iran, Turkey, India, Saudi Arabia, United Arab Emirates, the Philippines, South Korea, and Russia (as a transcontinental country). Our findings in the first part highlighted a noninfluential impact from the weather-driven parameters and short-term exposure to pollutants on the transmission rate; however, long-term exposure could potentially increase the risk of COVID-19 mortality rates (based on 1998-2017 p.m.2.5 data). Mobility was highly correlated with the COVID-19 transmission and based on our causal analysis reducing mobility could curb the COVID-19 transmission rate with a 6-day lag time (on average). Secondly, among all the tested policies for exiting the COVID-19 pandemic, the contact tracing was the most efficient if executed correctly. With a 2-day delay in tracing the virus hosts, a 60% successful host tracing, and a 70% contact reduction with the hosts, a pandemic will end in a year without overburdening a healthcare system with 6000 hospital beds capacity per million. Lastly, our vaccine simulations showed that the target date for achieving herd immunity significantly varied among the countries and could be delayed to October-november 2022 in countries like India and Iran (based on 60% immunized population and assuming no intermediate factors affecting the vaccination rate).

Fractionation of DNA and protein from individual latent fingerprints for forensic analysis.

Human touch samples represent a significant portion of forensic DNA casework. Yet, the generally low abundance of genetic material combined with the predominantly extracellular nature of DNA in these samples makes DNA-based forensic analysis exceptionally challenging. Human proteins present in these same touch samples offer an abundant and environmentally-robust alternative. Proteogenomic methods, using protein sequence variants arising from nonsynonymous DNA mutations, have recently been applied to forensic analysis and may represent a viable option looking forward. However, DNA analysis remains the gold standard and any proteomics-based methods would need to consider how DNA could be co-extracted from samples without significant loss. Herein, we describe a simple workflow for the collection, enrichment and fractionation of DNA and protein in latent fingerprint samples. This approach ensures that DNA collected from a latent fingerprint can be analyzed by traditional DNA casework methods, while protein can be proteolytically digested and analyzed via standard liquid chromatography-tandem mass spectrometry-based proteomics methods from the same touch sample. Sample collection from non-porous surfaces (i.e., glass) is performed through the application of an anionic surfactant over the fingermark. The sample is then split into separate DNA and protein fractions following centrifugation to enrich the protein fraction by pelleting skin cells. The results indicate that this workflow permits analysis of DNA within the sample, yet highlights the challenge posed by the trace nature of DNA in touch samples and the potential for DNA to degrade over time. Protein deposited in touch samples does not appear to share this limitation, with robust protein quantities collected across multiple human donors. The quantity and quality of protein remains robust regardless of fingerprint age. The proteomic content of these samples is consistent across individual donors and fingerprint age, supporting the future application of genetically variable peptide (GVP) analysis of touch samples for forensic identification.

Global to USA County Scale Analysis of Weather, Urban Density, Mobility, Homestay, and Mask Use on COVID-19.

Prior evaluations of the relationship between COVID-19 and weather indicate an inconsistent role of meteorology (weather) in the transmission rate. While some effects due to weather may exist, we found possible misconceptions and biases in the analysis that only consider the impact of meteorological variables alone without considering the urban metabolism and environment. This study highlights that COVID-19 assessments can notably benefit by incorporating factors that account for urban dynamics and environmental exposure. We evaluated the role of weather (considering equivalent temperature that combines the effect of humidity and air temperature) with particular consideration of urban density, mobility, homestay, demographic information, and mask use within communities. Our findings highlighted the importance of considering spatial and temporal scales for interpreting the weather/climate impact on the COVID-19 spread and spatiotemporal lags between the causal processes and effects. On global to regional scales, we found contradictory relationships between weather and the transmission rate, confounded by decentralized policies, weather variability, and the onset of screening for COVID-19, highlighting an unlikely impact of weather alone. At a finer spatial scale, the mobility index (with the relative importance of 34.32%) was found to be the highest contributing factor to the COVID-19 pandemic growth, followed by homestay (26.14%), population (23.86%), and urban density (13.03%). The weather by itself was identified as a noninfluential factor (relative importance < 3%). The findings highlight that the relation between COVID-19 and meteorology needs to consider scale, urban density and mobility areas to improve predictions.

An algorithm for random match probability calculation from peptide sequences.

For the past three decades, forensic genetic investigations have focused on elucidating DNA signatures. While DNA has a number of desirable properties (e.g., presence in most biological materials, an amenable chemistry for analysis and well-developed statistics), DNA also has limitations. DNA may be in low quantity in some tissues, such as hair, and in some tissues it may degrade more readily than its protein counterparts. Recent research efforts have shown the feasibility of performing protein-based human identification in cases in which recovery of DNA is challenged; however, the methods involved in assessing the rarity of a given protein profile have not been addressed adequately. In this paper an algorithm is proposed that describes the computation of a random match probability (RMP) resulting from a genetically variable peptide signature. The approach described herein explicitly models proteomic error and genetic linkage, makes no assumptions as to allelic drop-out, and maps the observed proteomic alleles to their expected protein products from DNA which, in turn, permits standard corrections for population structure and finite database sizes. To assess the feasibility of this approach, RMPs were estimated from peptide profiles of skin samples from 25 individuals of European ancestry. 126 common peptide alleles were used in this approach, yielding a mean RMP of approximately 10-2.