A tour de force provides novel insights into CD19-CAR T cell resistance mechanisms.

Sworder et al.1 developed an integrated simultaneous tumor and effector profiling (STEP) approach to study resistance mechanisms to CD19-CAR T cell therapy in large B-cell lymphomas. Their study provides novel biological insights and paves the way for future interventions.

Strategies to increase uptake of voluntary medical male circumcision among men aged 25-39 years in Nyanza Region, Kenya: Results from a cluster randomized controlled trial (the TASCO study).

INTRODUCTION: Voluntary medical male circumcision (VMMC) for HIV prevention began in Nyanza Region, Kenya in 2008. By 2014, approximately 800,000 VMMCs had been conducted, and 84.9% were among males aged 15-24 years. We evaluated the impact of interpersonal communication (IPC) and dedicated service outlets (DSO) on VMMC uptake among men aged 25-39 years in Nyanza Region. MATERIALS AND METHODS: We conducted a cluster randomized controlled trial in 45 administrative Locations (clusters) in Nyanza Region between May 2014 and June 2016 among uncircumcised men aged 25-34 years. In arm one, an IPC toolkit was used to address barriers to VMMC. In the second arm, men were referred to DSO that were modified to address their preferences. Arm three combined the IPC and DSO arms, and arm four was standard of care (SOC). Randomization was done at Location level (11-12 per arm). The primary outcome was the proportion of enrolled men who received VMMC within three months. Generalized estimating equations were used to evaluate the effect of interventions on the outcome. RESULTS: At baseline, 9,238 households with men aged 25-39 years were enumerated, 9,679 men were assessed, and 2,792 (28.8%) were eligible. For enrollment, 577 enrolled in the IPC arm, 825 in DSO, 723 in combined IPC + DSO, and 667 in SOC. VMMC uptake among men in the SOC arm was 3.2%. In IPC, DSO, and combined IPC + DSO arms, uptake was 3.3%, 4.5%, and 4.4%, respectively. The adjusted odds ratio (aOR) of VMMC uptake in the study arms compared to SOC were IPC aOR = 1.03; 95% CI: 0.50-2.13, DSO aOR = 1.31; 95% CI: 0.67-2.57, and IPC + DSO combined aOR = 1.31, 95% CI: 0.65-2.67. DISCUSSION: Using these interventions among men aged 25-39 years did not significantly impact VMMC uptake. These findings suggest that alternative demand creation strategies for VMMC services are needed to reach men aged 25-39 years. TRIAL REGISTRATION: clinicaltrials.gov identifier: NCT02497989.

Epistasis between promoter activity and coding mutations shapes gene evolvability.

The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.

MeganServer: facilitating interactive access to metagenomic data on a server.

MOTIVATION: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. AVAILABILITY AND IMPLEMENTATION: MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome.

Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.

The interplay of active and passive mechanisms in slow axonal transport.

A combination of intermittent active movement of transient aggregates and a paused state that intervenes between periods of active transport has been proposed to underlie the slow, directed transport of soluble proteins in axons. A component of passive diffusion in the axoplasm may also contribute to slow axonal transport, although quantitative estimates of the relative contributions of diffusive and active movement in the slow transport of a soluble protein, and in particular how they might vary across developmental stages, are lacking. Here, we propose and study a model for slow axonal transport, addressing data from bleach recovery measurements on a small, soluble, protein, choline acetyltransferase, in thin axons of the lateral chordotonal (lch5) sensory neurons of Drosophila. Choline acetyltransferase is mainly present in soluble form in the axon and catalyzes the acetylation of choline at the synapse. It does not form particulate structures in axons and moves at rates characteristic of slow component b (≈ 1-10 mm/day or 0.01-0.1 µm/s). Using our model, which incorporates active transport with paused and/or diffusive states, we predict bleach recovery, transport rates, and cargo trajectories obtained through kymographs, comparing these with experimental observations at different developmental stages. We show that changes in the diffusive fraction of cargo during these developmental stages dominate bleach recovery and that a combination of active motion with a paused state alone cannot reproduce the data. We compared predictions of the model with results from photoactivation experiments. The importance of the diffusive state in reproducing the bleach recovery signal in the slow axonal transport of small soluble proteins is our central result.

An observability and detectability analysis for non-linear uncertain CSTR model of biochemical processes.

The problem of proving observability/detectability properties for selected non-linear uncertain model of biochemical processes has been addressed in this paper. In particular, the analysis of observability/detectability in the face of parametric and unstructured uncertainty in system dynamics transformed into unknown inputs, and unknown initial conditions has been performed. Various sets of system measured outputs were taken into account during the research. The considered biochemical processes were modelled as a continuous stirred tank reactor with the microbial growth reaction and microbial mortality with the aggregated substrate and biomass concentrations in aerobic phase. Classical tools based on differential geometry and the method of indistinguishable state trajectories (indistinguishable dynamics) were used to verify the properties of the system. The observability/detectability analysis was performed for nine cases covering a wide range of possible combinations of system measured outputs and unknown inputs. The obtained results of are crucial meaning for system state reconstruction (estimation), which involves the synthesis of state observers.

Exploration of the Mechanism of Linoleic Acid Metabolism Dysregulation in Metabolic Syndrome.

We aimed to explore the mechanism of the linoleic acid metabolism in metabolic syndrome (MetS). RNA-seq data for 16 samples with or without MetS from the GSE145412 dataset were collected. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and gene differential expression analysis were performed. Expression data of differentially expressed genes (DEGs) involved in the linoleic acid metabolism pathway were mapped to the pathway by using Pathview for visualization. There were 19 and 10 differentially expressed biological processes in the disease group and healthy group, respectively. 9 KEGG pathways were differentially expressed in the disease group. Linoleic acid metabolism was the only differentially expressed pathway in the healthy group. The GSVA enrichment score of the linoleic acid metabolism pathway in the disease group was markedly lower than that in the healthy group. The GSEA result showed that the linoleic acid metabolism pathway was significantly downregulated in the disease group. JMJD7-PLA2G4B, PLA2G1B, PLA2G2D, CYP2C8, and CYP2J2 involved in the pathway were significantly downregulated in the disease group. This study may provide novel insight into MetS from the point of linoleic acid metabolism dysregulation.

Thermostable Proteins from HaCaT Keratinocytes Identify a Wide Breadth of Intrinsically Disordered Proteins and Candidates for Liquid-Liquid Phase Separation.

Intrinsically disordered proteins (IDPs) move through an ensemble of conformations which allows multitudinous roles within a cell. Keratinocytes, the predominant cell type in mammalian epidermis, have had only a few individual proteins assessed for intrinsic disorder and its possible contribution to liquid-liquid phase separation (LLPS), especially in regard to what functions or structures these proteins provide. We took a holistic approach to keratinocyte IDPs starting with enrichment via the isolation of thermostable proteins. The keratinocyte protein involucrin, known for its resistance to heat denaturation, served as a marker. It and other thermostable proteins were identified by liquid chromatography tandem mass spectrometry and subjected to extensive bioinformatic analysis covering gene ontology, intrinsic disorder, and potential for LLPS. Numerous proteins unique to keratinocytes and other proteins with shared expression in multiple cell types were identified to have IDP traits (e.g., compositional bias, nucleic acid binding, and repeat motifs). Among keratinocyte-specific proteins, many that co-assemble with involucrin into the cell-specific structure known as the cornified envelope scored highly for intrinsic disorder and potential for LLPS. This suggests intrinsic disorder and LLPS are previously unrecognized traits for assembly of the cornified envelope, echoing the contribution of intrinsic disorder and LLPS to more widely encountered features such as stress granules and PML bodies.

A Review of Current Evidence on the Relationship between Phosphate Metabolism and Metabolic Syndrome.

Phosphorus, present as phosphate in biological systems, is an essential mineral for various biological activities and biochemical processes. Numerous studies have indicated that disturbed phosphate balance may contribute to the development of metabolic syndrome (MetS). However, no consistent result was found on the association between phosphorus intake and serum phosphate concentration with MetS. It is believed that both positive and negative impacts of phosphorus/phosphate co-exist in parallel during MetS condition. Reduced phosphate level contributed to the development of obesity and hyperglycaemia. Low phosphate is believed to compromise energy production, reduce exercise capacity, increase food ingestion, and impair glucose metabolism. On the other hand, the effects of phosphorus/phosphate on hypertension are rather complex depending on the source of phosphorus and subjects' health conditions. Phosphorus excess activates sympathetic nervous system, renin-angiotensin-aldosterone system, and induces hormonal changes under pathological conditions, contributing to the blood pressure-rising effects. For lipid metabolism, adequate phosphate content ensures a balanced lipid profile through regulation of fatty acid biosynthesis, oxidation, and bile acid excretion. In conclusion, phosphate metabolism serves as a potential key feature for the development and progression of MetS. Dietary phosphorus and serum phosphate level should be under close monitoring for the management of MetS.

KinMod database: a tool for investigating metabolic regulation.

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz.