Minimizing the number of optimizations for efficient community dynamic flux balance analysis

by James D. Brunner, Nicholas Chia

Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism’s metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem’s constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.

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Effectiveness of a video-based smoking cessation intervention focusing on maternal and child health in promoting quitting among expectant fathers in China: A randomized controlled trial

by Wei Xia, Ho Cheung William Li, Wenzhi Cai, Peige Song, Xiaoyu Zhou, Ka Wai Katherine Lam, Laurie Long Kwan Ho, Ankie Tan Cheung, Yuanhui Luo, Chunxian Zeng, Ka Yan Ho

Background

Secondhand smoke can cause adverse pregnancy outcomes, yet there is a lack of effective smoking cessation interventions targeted at expectant fathers. We examined the effectiveness of a video-based smoking cessation intervention focusing on maternal and child health in promoting quitting among expectant fathers.

Methods and findings

A single-blind, 3-arm, randomized controlled trial was conducted at the obstetrics registration centers of 3 tertiary public hospitals in 3 major cities (Guangzhou, Shenzhen, and Foshan) in China. Smoking expectant fathers who registered with their pregnant partners were invited to participate in this study. Between 14 August 2017 to 28 February 2018, 1,023 participants were randomized to a video (n = 333), text (n = 322), or control (n = 368) group. The video and text groups received videos or text messages on the risks of smoking for maternal and child health via instant messaging. The control group received a leaflet with information on smoking cessation. Follow-up visits were conducted at 1 week and at 1, 3, and 6 months. The primary outcome, by intention to treat (ITT), was validated abstinence from smoking at the 6-month follow-up. The secondary outcomes included 7-day point prevalence of abstinence (PPA) and level of readiness to quit at each follow-up. The mean age of participants was 32 years, and about half of them were first-time expectant fathers. About two-thirds of participants had completed tertiary education. The response rate was 79.7% (815 of 1,023) at 6 months. The video and text groups had higher rates of validated abstinence than the control group (video group: 22.5% [75 of 333], P Conclusions

This smoking cessation intervention for expectant fathers that focused on explaining the ramifications of smoking on maternal and child health was effective and feasible in promoting quitting, and video messages were more effective than texts in delivering the information.

Trial registration

ClinicalTrials.gov: NCT03236025.

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Short-chain fatty acids bind to apoptosis-associated speck-like protein to activate inflammasome complex to prevent Salmonella infection

by Hitoshi Tsugawa, Yasuaki Kabe, Ayaka Kanai, Yuki Sugiura, Shigeaki Hida, Shun’ichiro Taniguchi, Toshio Takahashi, Hidenori Matsui, Zenta Yasukawa, Hiroyuki Itou, Keiyo Takubo, Hidekazu Suzuki, Kenya Honda, Hiroshi Handa, Makoto Suematsu

Short-chain fatty acids (SCFAs) produced by gastrointestinal microbiota regulate immune responses, but host molecular mechanisms remain unknown. Unbiased screening using SCFA-conjugated affinity nanobeads identified apoptosis-associated speck-like protein (ASC), an adaptor protein of inflammasome complex, as a noncanonical SCFA receptor besides GPRs. SCFAs promoted inflammasome activation in macrophages by binding to its ASC PYRIN domain. Activated inflammasome suppressed survival of Salmonella enterica serovar Typhimurium (S. Typhimurium) in macrophages by pyroptosis and facilitated neutrophil recruitment to promote bacterial elimination and thus inhibit systemic dissemination in the host. Administration of SCFAs or dietary fibers, which are fermented to SCFAs by gut bacteria, significantly prolonged the survival of S. Typhimurium–infected mice through ASC-mediated inflammasome activation. SCFAs penetrated into the inflammatory region of the infected gut mucosa to protect against infection. This study provided evidence that SCFAs suppress Salmonella infection via inflammasome activation, shedding new light on the therapeutic activity of dietary fiber.

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Millipede genomes reveal unique adaptations during myriapod evolution

by Zhe Qu, Wenyan Nong, Wai Lok So, Tom Barton-Owen, Yiqian Li, Thomas C. N. Leung, Chade Li, Tobias Baril, Annette Y. P. Wong, Thomas Swale, Ting-Fung Chan, Alexander Hayward, Sai-Ming Ngai, Jerome H. L. Hui

The Myriapoda, composed of millipedes and centipedes, is a fascinating but poorly understood branch of life, including species with a highly unusual body plan and a range of unique adaptations to their environment. Here, we sequenced and assembled 2 chromosomal-level genomes of the millipedes Helicorthomorpha holstii (assembly size = 182 Mb; shortest scaffold/contig length needed to cover 50% of the genome [N50] = 18.11 Mb mainly on 8 pseudomolecules) and Trigoniulus corallinus (assembly size = 449 Mb, N50 = 26.78 Mb mainly on 17 pseudomolecules). Unique genomic features, patterns of gene regulation, and defence systems in millipedes, not observed in other arthropods, are revealed. Both repeat content and intron size are major contributors to the observed differences in millipede genome size. Tight Hox and the first loose ecdysozoan ParaHox homeobox clusters are identified, and a myriapod-specific genomic rearrangement including Hox3 is also observed. The Argonaute (AGO) proteins for loading small RNAs are duplicated in both millipedes, but unlike in insects, an AGO duplicate has become a pseudogene. Evidence of post-transcriptional modification in small RNAs—including species-specific microRNA arm switching—providing differential gene regulation is also obtained. Millipedes possesses a unique ozadene defensive gland unlike the venomous forcipules found in centipedes. We identify sets of genes associated with the ozadene that play roles in chemical defence as well as antimicrobial activity. Macro-synteny analyses revealed highly conserved genomic blocks between the 2 millipedes and deuterostomes. Collectively, our analyses of millipede genomes reveal that a series of unique adaptations have occurred in this major lineage of arthropod diversity. The 2 high-quality millipede genomes provided here shed new light on the conserved and lineage-specific features of millipedes and centipedes. These findings demonstrate the importance of the consideration of both centipede and millipede genomes—and in particular the reconstruction of the myriapod ancestral situation—for future research to improve understanding of arthropod evolution, and animal evolutionary genomics more widely.

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Cocoonase is indispensable for Lepidoptera insects breaking the sealed cocoon

by Tingting Gai, Xiaoling Tong, Minjin Han, Chunlin Li, Chunyan Fang, Yunlong Zou, Hai Hu, Hui Xiang, Zhonghuai Xiang, Cheng Lu, Fangyin Dai

Many insects spin cocoons to protect the pupae from unfavorable environments and predators. After emerging from the pupa, the moths must escape from the sealed cocoons. Previous works identified cocoonase as the active enzyme loosening the cocoon to form an escape-hatch. Here, using bioinformatics tools, we show that cocoonase is specific to Lepidoptera and that it probably existed before the occurrence of lepidopteran insects spinning cocoons. Despite differences in cocooning behavior, we further show that cocoonase evolved by purification selection in Lepidoptera and that the selection is more intense in lepidopteran insects spinning sealed cocoons. Experimentally, we applied gene editing techniques to the silkworm Bombyx mori, which spins a dense and sealed cocoon, as a model of lepidopteran insects spinning sealed cocoons. We knocked out cocoonase using the CRISPR/Cas9 system. The adults of homozygous knock-out mutants were completely formed and viable but stayed trapped and died naturally in the cocoon. This is the first experimental and phenotypic evidence that cocoonase is the determining factor for breaking the cocoon. This work led to a novel silkworm strain yielding permanently intact cocoons and provides a new strategy for controlling the pests that form cocoons.

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Mutation bias interacts with composition bias to influence adaptive evolution

by Alejandro V. Cano, Joshua L. Payne

Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias—a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths—that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.

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Tuning network dynamics from criticality to an asynchronous state

by Jingwen Li, Woodrow L. Shew

According to many experimental observations, neurons in cerebral cortex tend to operate in an asynchronous regime, firing independently of each other. In contrast, many other experimental observations reveal cortical population firing dynamics that are relatively coordinated and occasionally synchronous. These discrepant observations have naturally led to competing hypotheses. A commonly hypothesized explanation of asynchronous firing is that excitatory and inhibitory synaptic inputs are precisely correlated, nearly canceling each other, sometimes referred to as ‘balanced’ excitation and inhibition. On the other hand, the ‘criticality’ hypothesis posits an explanation of the more coordinated state that also requires a certain balance of excitatory and inhibitory interactions. Both hypotheses claim the same qualitative mechanism—properly balanced excitation and inhibition. Thus, a natural question arises: how are asynchronous population dynamics and critical dynamics related, how do they differ? Here we propose an answer to this question based on investigation of a simple, network-level computational model. We show that the strength of inhibitory synapses relative to excitatory synapses can be tuned from weak to strong to generate a family of models that spans a continuum from critical dynamics to asynchronous dynamics. Our results demonstrate that the coordinated dynamics of criticality and asynchronous dynamics can be generated by the same neural system if excitatory and inhibitory synapses are tuned appropriately.

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Characterizing chromatin folding coordinate and landscape with deep learning

by Wen Jun Xie, Yifeng Qi, Bin Zhang

Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell imaging and to identify a reaction coordinate for chromatin folding. This coordinate connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along a folding pathway that leads to the formation of topologically associating domains (TAD). We showed that folding into wild-type-like structures remain energetically favorable in cohesin-depleted cells, potentially as a result of the phase separation between the two chromatin segments with active and repressive histone marks. The energetic stabilization, however, is not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Our study suggests that machine learning techniques, when combined with rigorous statistical mechanical analysis, are powerful tools for analyzing structural ensembles of chromatin.

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A bacterial size law revealed by a coarse-grained model of cell physiology

by François Bertaux, Julius von Kügelgen, Samuel Marguerat, Vahid Shahrezaei

Universal observations in Biology are sometimes described as “laws”. In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate. Because they formalize complex emerging principles in biology, growth laws have been instrumental in shaping our understanding of bacterial physiology. Here, we discovered a novel size law that connects cell size to the inverse of the metabolic proteome mass fraction and the active fraction of ribosomes. We used a simple whole-cell coarse-grained model of cell physiology that combines the proteome allocation theory and the structural model of cell division. This integrated model captures all available experimental data connecting the cell proteome composition, ribosome activity, division size and growth rate in response to nutrient quality, antibiotic treatment and increased protein burden. Finally, a stochastic extension of the model explains non-trivial correlations observed in single cell experiments including the adder principle. This work provides a simple and robust theoretical framework for studying the fundamental principles of cell size determination in unicellular organisms.

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A simple, scalable approach to building a cross-platform transcriptome atlas

by Paul W. Angel, Nadia Rajab, Yidi Deng, Chris M. Pacheco, Tyrone Chen, Kim-Anh Lê Cao, Jarny Choi, Christine A. Wells

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro.
In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.

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