PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data.

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

The MTIST platform: a microbiome time series inference standardized test.

The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.

The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia.

Longitudinal microbiome data provide valuable insight into disease states and clinical responses, but they are challenging to mine and view collectively. To address these limitations, we present TaxUMAP, a taxonomically informed visualization for displaying microbiome states in large clinical microbiome datasets. We used TaxUMAP to chart a microbiome atlas of 1,870 patients with cancer during therapy-induced perturbations. Bacterial density and diversity were positively associated, but the trend was reversed in liquid stool. Low-diversity states (dominations) remained stable after antibiotic treatment, and diverse communities had a broader range of antimicrobial resistance genes than dominations. When examining microbiome states associated with risk for bacteremia, TaxUMAP revealed that certain Klebsiella species were associated with lower risk for bacteremia localize in a region of the atlas that is depleted in high-risk enterobacteria. This indicated a competitive interaction that was validated experimentally. Thus, TaxUMAP can chart comprehensive longitudinal microbiome datasets, enabling insights into microbiome effects on human health.

Gut microbiome dysbiosis during COVID-19 is associated with increased risk for bacteremia and microbial translocation.

The microbial populations in the gut microbiome have recently been associated with COVID-19 disease severity. However, a causal impact of the gut microbiome on COVID-19 patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. Antibiotics and other treatments during COVID-19 can potentially confound microbiome associations. We therefore first demonstrate in a mouse model that SARS-CoV-2 infection can induce gut microbiome dysbiosis, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Comparison with stool samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, paralleling our observations in the animal model. Specifically, we observed blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species in hospitalized COVID-19 patients. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data obtained from these patients indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

Gut microbiome dysbiosis in antibiotic-treated COVID-19 patients is associated with microbial translocation and bacteremia.

Although microbial populations in the gut microbiome are associated with COVID-19 severity, a causal impact on patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. We first demonstrate SARS-CoV-2 infection induces gut microbiome dysbiosis in mice, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, including blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

Gut microbiome dysbiosis during COVID-19 is associated with increased risk for bacteremia and microbial translocation.

The microbial populations in the gut microbiome have recently been associated with COVID-19 disease severity. However, a causal impact of the gut microbiome on COVID-19 patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. Antibiotics and other treatments during COVID-19 can potentially confound microbiome associations. We therefore first demonstrate that the gut microbiome is directly affected by SARS-CoV-2 infection in a dose-dependent manner in a mouse model, causally linking viral infection and gut microbiome dysbiosis. Comparison with stool samples collected from 97 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, paralleling our observations in the animal model. Specifically, we observed blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species in hospitalized COVID-19 patients. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data obtained from these patients suggest that bacteria translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID 19.

Highly coupled transport can be achieved in free-exchange transport models.

Secondary active transporters couple the transport of an ion species down its concentration gradient to the uphill transport of another substrate. Despite the importance of secondary active transport to multidrug resistance, metabolite transport, and nutrient acquisition, among other biological processes, the microscopic steps of the coupling mechanism are not well understood. Often, transport models illustrate coupling mechanisms through a limited number of "major" conformations or states, yet recent studies have indicated that at least some transporters violate these models. The small multidrug resistance transporter EmrE has been shown to couple proton influx to multidrug efflux via a mechanism that incorporates both "major" and "minor" conformational states and transitions. The resulting free exchange transport model includes multiple leak pathways and theoretically allows for both exchange and cotransport of ion and substrate. To better understand how coupled transport can be achieved in such a model, we numerically simulate a free-exchange model of transport to determine the step-by-step requirements for coupled transport. We find that only moderate biasing of rate constants for key transitions produce highly efficient net transport approaching a perfectly coupled, stoichiometric model. We show how a free-exchange model can enable complex phenotypes, including switching transport direction with changing environmental conditions or substrates. This research has broad implications for synthetic biology, as it demonstrates the utility of free-exchange transport models and the fine tuning required for perfectly coupled transport.

Blue/violet laser inactivates methicillin-resistant Staphylococcus aureus by altering its transmembrane potential.

The resistance of methicillin-resistant Staphylococcus aureus to antibiotics presents serious clinical problems that prompted the need for finding alternative or combination therapies. One such therapy is irradiation with blue light. To determine the alterations in metabolic processes implicated in the observed antimicrobial effects of blue light, we investigated the changes in membrane potential and the presence of free-radical-producing photo-acceptor molecules. Bacterial cultures irradiated with one or two doses of 405nm laser light (each consisting of 121J/cm2) were imaged with spectrally resolved laser-scanning microscopes to detect endogenous fluorescent species as well as the voltage sensitive dye 3,3'-Diethyloxacarbocyanine iodide. The endogenous fluorescence indicated the presence of photosensitizers (i.e., porphyrins, NADH, FAD) in the cells, while the exogenous signal allowed us to monitor rapid changes in transmembrane potential following treatment with light. The changes were drastic within the first 5min after irradiation with the first dose and continued slowly after the second irradiation. These results suggest that the early antimicrobial activity of blue light results from alteration of membrane integrity with a consequent decrease in membrane polarization and rapid alteration of vital cellular functions. The observation of an early antimicrobial activity of light is very encouraging, as it suggests that treatment does not necessarily have to be administered over a long period of time.