Environmental flexibility does not explain metabolic robustness.

NPJ Syst Biol Appl. 2020 Nov 27;6(1):39

Authors: Libiseller-Egger J, Coltman B, Gerstl MP, Zanghellini J

Abstract
Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc. are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. Building on an improved theoretical understanding, we analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

PMID: 33247119 [PubMed - as supplied by publisher]

Metagenomic characterization of parental and production CHO cell lines for detection of adventitious viruses.

Biologicals. 2020 Nov 24;:

Authors: Bačnik K, Kutnjak D, Jerič Kokelj B, Tuta N, Lončar T, Vogelsang M, Ravnikar M

Abstract
Viral contamination is a major concern for biological products. Therefore, virus testing of raw materials and cells is essential for the safety of the final product. We used high-throughput sequencing to detect viral-like sequences in selected CHO cell lines. Our aim was to test various approaches of sample preparation, to establish a pipeline for metagenomic analysis and to characterize standard viral metagenome of production and parental CHO cell lines. The comparison of the metagenomics composition of the differently prepared samples showed that among four tested approaches sequencing of ribosomal RNA depleted total RNA is the most promising approach. The metagenomics investigation of one production and three parental CHO cell lines of diverse origin did not indicate the presence of adventitious viral agents in the investigated samples. The study revealed an expected background of virus-like nucleic acids in the samples, which originate from remains of expression vectors, endogenized viral elements and residuals of bacteriophages.

PMID: 33246870 [PubMed - as supplied by publisher]

A Unique Feature of COVID-19 Infection in Chest CT; "Pulmonary Target" Appearance.

Acad Radiol. 2020 Nov 21;:

Authors: Jafari R, Maghsoudi H, Saburi A

PMID: 33246787 [PubMed - as supplied by publisher]

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"systems biology"

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HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes.

Nucleic Acids Res. 2020 Nov 22;:

Authors: Kasmanas JC, Bartholomäus A, Corrêa FB, Tal T, Jehmlich N, Herberth G, von Bergen M, Stadler PF, Carvalho ACPLF, Nunes da Rocha U

Abstract
Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.

PMID: 33221926 [PubMed - as supplied by publisher]

A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth.

Nucleic Acids Res. 2020 Nov 22;:

Authors: Mathis AD, Otto RM, Reynolds KA

Abstract
A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that 'escape' the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.

PMID: 33221881 [PubMed - as supplied by publisher]

Virome characterization of Cryphonectria parasitica isolates from Azerbaijan unveiled a new mymonavirus and a putative new RNA virus unrelated to described viral sequences.

Virology. 2020 Nov 13;553:51-61

Authors: Forgia M, Isgandarli E, Aghayeva DN, Huseynova I, Turina M

Abstract
Cryphonectria parasitica, the causal agent of chestnut blight is controlled in Europe through natural spread of Cryphonectria hypovirus 1 (CHV1), a mycovirus able to induce hypovirulence to the host. In recent years C. parasitica was reported infecting Azerbaijani population of chestnut, but the presence of CHV1 still needs to be confirmed. Aim of this work was to investigate fifty-five C. parasitica isolates collected in Azerbaijan to describe the associated viruses. Our work found i) the first negative-sense ssRNA virus known to infect C. parasitica naturally for which we propose the name Cryphonectria parasitica sclerotimonavirus 1 (CpSV1) and ii) an RNA sequence showing peculiar features suggesting a viral nature for which we propose the name Cryphonectria parasitica ambivirus 1 (CpaV1). The discovery of CpaV1 expands our knowledge of the RNA virosphere suggesting the existence of a new lineage that cannot presently be reliably associated to the monophyletic Riboviria.

PMID: 33221630 [PubMed - as supplied by publisher]

Systems toxicogenomics of prenatal low-dose BPA exposure on liver metabolic pathways, gut microbiota, and metabolic health in mice.

Environ Int. 2020 Nov 19;146:106260

Authors: Diamante G, Cely I, Zamora Z, Ding J, Blencowe M, Lang J, Bline A, Singh M, Lusis AJ, Yang X

Abstract
Bisphenol A (BPA) is an industrial plasticizer widely found in consumer products, and exposure to BPA during early development has been associated with the prevalence of various cardiometabolic diseases including obesity, metabolic syndrome, type 2 diabetes, and cardiovascular diseases. To elucidate the molecular perturbations underlying the connection of low-dose prenatal BPA exposure to cardiometabolic diseases, we conducted a multi-dimensional systems biology study assessing the liver transcriptome, gut microbial community, and diverse metabolic phenotypes in both male and female mouse offspring exposed to 5 μg/kg/day BPA during gestation. Prenatal exposure to low-dose BPA not only significantly affected liver genes involved in oxidative phosphorylation, PPAR signaling and fatty acid metabolism, but also affected the gut microbial composition in an age- and sex-dependent manner. Bacteria such as those belonging to the S24-7 and Lachnospiraceae families were correlated with offspring phenotypes, differentially expressed liver metabolic genes such as Acadl and Dgat1, and key drivers identified in our gene network modeling such as Malat1 and Apoa2. This multiomics study provides insight into the relationship between gut bacteria and host liver genes that could contribute to cardiometabolic disease risks upon low-dose BPA exposure.

PMID: 33221593 [PubMed - as supplied by publisher]

Machine learning for metabolic engineering: A review.

Metab Eng. 2020 Nov 19;:

Authors: Lawson C, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn J, Martin HG

Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

PMID: 33221420 [PubMed - as supplied by publisher]

Structure of an affinity-matured inhibitory recombinant fab against urokinase plasminogen activator reveals basis of potency and specificity.

Biochim Biophys Acta Proteins Proteom. 2020 Nov 19;:140562

Authors: Sevillano N, Bohn MF, Zimanyi M, Chen Y, Petzold C, Gupta S, Ralston CY, Craik CS

Abstract
Affinity maturation of U33, a recombinant Fab inhibitor of uPA, was used to improve the affinity and the inhibitory effect compared to the parental Fab. Arginine scanning of the six CDR loops of U33 was done to identify initial binding determinants since uPA prefers arginine in its primary substrate binding pocket. Two CDR loops were selected to create an engineered affinity maturation library of U33 that was diversified around ArgL91 (CDR L3) and ArgH52 (CDR H2). Biopanning of the randomized U33 library under stringent conditions resulted in eight Fabs with improved binding properties. One of the most potent inhibitors, AB2, exhibited a 13-fold decrease in IC50 when compared to U33 largely due to a decrease in its off rate. To identify contributions of interfacial residues that might undergo structural rearrangement upon interface formation we used X-ray footprinting and mass spectrometry (XFMS). Four residues showed a pronounced decrease in solvent accessibility, and their clustering suggests that AB2 targets the active site and also engages residues in an adjacent pocket unique to human uPA. The 2.9 Å resolution crystal structure of AB2-bound to uPA shows a binding mode in which the CDR L1 loop inserts into the active site cleft and acts as a determinant of inhibition. The selectivity determinant of this binding mode is unlike previously identified inhibitory Fabs against uPA related serine proteases, MTSP-1, HGFA and FXIa. CDRs H2 and L3 loops aid in interface formation and provide critical salt-bridges to remodel loops surrounding the active site of uPA providing specificity and further evidence that antibodies can be potent and selective inhibitors of proteolytic enzymes.

PMID: 33221341 [PubMed - as supplied by publisher]

Comparing the Efficacy of Cancer Therapies between Subgroups in Basket Trials.

Cell Syst. 2020 Nov 18;11(5):449-460.e2

Authors: Palmer AC, Plana D, Sorger PK

Abstract
The need to test anticancer drugs in multiple indications has been addressed by basket trials, which are Phase I or II clinical trials involving multiple tumor subtypes and a single master protocol. Basket trials typically involve few patients per type, making it challenging to rigorously compare responses across types. We describe the use of permutation testing to test for differences among subgroups using empirical null distributions and the Benjamini-Hochberg procedure to control for false discovery. We apply the approach retrospectively to tumor-volume changes and progression-free survival in published basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib and uncover examples of therapeutic benefit missed by conventional binomial testing. For example, we identify an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations. Permutation testing can be used to design basket trials but is more conservatively introduced alongside established approaches to enrollment such as Simon's two-stage design.

PMID: 33220857 [PubMed - as supplied by publisher]

Microbial function and genital inflammation in young South African women at high risk of HIV infection.

Microbiome. 2020 Nov 21;8(1):165

Authors: Alisoltani A, Manhanzva MT, Potgieter M, Balle C, Bell L, Ross E, Iranzadeh A, du Plessis M, Radzey N, McDonald Z, Calder B, Allali I, Mulder N, Dabee S, Barnabas S, Gamieldien H, Godzik A, Blackburn JM, Tabb DL, Bekker LG, Jaspan HB, Passmore JS, Masson L

Abstract
BACKGROUND: Female genital tract (FGT) inflammation is an important risk factor for HIV acquisition. The FGT microbiome is closely associated with inflammatory profile; however, the relative importance of microbial activities has not been established. Since proteins are key elements representing actual microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT microbial function and inflammation in 113 young and adolescent South African women at high risk of HIV infection. Women were grouped as having low, medium, or high FGT inflammation by K-means clustering according to pro-inflammatory cytokine concentrations.
RESULTS: A total of 3186 microbial and human proteins were identified in lateral vaginal wall swabs using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. However, differences in the predicted relative abundance of most bacteria were observed between 16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations (gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based on 16S rRNA gene sequence data (p < 0.0001). The majority of microbial biological processes were underrepresented in women with high inflammation compared to those with low inflammation, including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 women 9 weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with high FGT inflammation in an independent sample of ten women.
CONCLUSIONS: Both the presence of specific microbial taxa in the FGT and their properties and activities are critical determinants of FGT inflammation. Our findings support those of previous studies suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of microbial activities, we have developed the FGT-DB application that is available at http://fgtdb.org/ . Video Abstract.

PMID: 33220709 [PubMed - as supplied by publisher]

Normal Somatic Mutations in Cancer Transformation.

Cancer Cell. 2020 Nov 17;:

Authors: Wijewardhane N, Dressler L, Ciccarelli FD

Abstract
Gene alterations play a prominent role in driving cancer initiation and progression. However, the genetic events that occur in normal cells prior to tumorigenesis are still unknown. Recent studies have started to map somatic mutations in normal human tissues, and here we discuss their implications for our understanding of tumorigenesis.

PMID: 33220180 [PubMed - as supplied by publisher]

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies.

Nucleic Acids Res. 2020 Nov 21;:

Authors: Shin J, Marx H, Richards A, Vaneechoutte D, Jayaraman D, Maeda J, Chakraborty S, Sussman M, Vandepoele K, Ané JM, Coon J, Roy S

Abstract
Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

PMID: 33219668 [PubMed - as supplied by publisher]