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Build and visualize realistic maps of dynamic human genome architecture to decipher environmentally (or diseased) induced alterations of fundamental cellular functions.
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
Transgenic mice that differed in their p53 genetic status were exposed to an acute dose of highly charged and energetic (HZE) iron particle radiation. Micronuclei (MN) in two distinct populations of circulating peripheral blood erythrocytes, the immature reticulocytes (RETs) and the mature normochromatic erythrocytes (NCEs), were measured using a simple and efficient flow cytometric procedure. Our results show significant elevation in the frequency of micronucleated RETs (%MN-RETs) at 2 and 3 days post-radiation. At 3 days post-irradiation, the magnitude of the radiation-induced MN-RET was 2.3-fold higher in the irradiated p53 wild-type animals compared to the unirradiated controls, 2.5-fold higher in the p53 hemizygotes and 4.3-fold higher in the p53 nullizygotes. The persistence of this radiation-induced elevation of MN-RETs is dependent on the p53 genetic background of the animal. In the p53 wild-type and p53 hemizygotes, %MN-RETs returned to control levels by 9 days post-radiation. However, elevated levels of %MN-RETs in p53 nullizygous mice persisted beyond 56 days post-radiation. We also observed elevated MN-NCEs in the peripheral circulation after radiation, but the changes in radiation-induced levels of MN-NCEs appear dampened compared to those of the MN-RETs for all three strains of animals. These results suggest that the lack of p53 gene function may play a role in the iron particle radiation-induced genomic instability in stem cell populations in the hematopoietic system.
Gas fermentation by Clostridium autoethanogenum is a commercial process for the sustainable biomanufacturing of fuels and valuable chemicals using abundant, low-cost C1 feedstocks (CO and CO 2 ) from sources such as inedible biomass, unsorted and nonrecyclable municipal solid waste, and industrial emissions. Efforts toward pathway engineering and elucidation of gene function in this microbe have been limited by a lack of genetic tools to control gene expression and arduous genome engineering methods. To increase the pace of progress, here we developed an inducible CRISPR interference (CRISPRi) system for C. autoethanogenum and applied that system toward transcriptional repression of genes with ostensibly crucial functions in metabolism.
Chemical agents are commonly used to protect algal crops. Yet, few studies have characterized the effects of these agents on associated microbial communities to understand effects on microbial functions relevant to algal crop production and protection. Here, we used shotgun metagenomic sequencing and untargeted exometabolite profiling to link the application of bleach, a -cidal agent used to protect algae from pests, to changes in community composition, metabolic pathways, and exometabolies - at a whole community level. Bleach protected the algal crop from crashing but altered bacterial diversity. Analysis of metagenome-assembled genomes (MAGs) revealed a classic predator-prey cycle between Oligoflexus and our target alga Nannochloropsis. Olifoflexus genomes from our study were notably similar to a previously identified BALO (Bdellovibrio and like organism), FD111, known to kill Nannochloropsis cultures, providing strong evidence that an FD111-like organism was responsible for the crash. Metabolic pathway composition differed between bleached and unbleached ponds, with abundance of twelve pathways related to stress tolerance, including the superpathway of methylglyoxal degradation, lipid IVA biosynthesis, and ectoine biosynthesis, greater in bleached ponds compared to unbleached ponds. Virulence factors related to adherence, biofilm formation, motility, and pathogenicity increased dramatically in bleached ponds with time, although this increase was not coupled with an increase in pathogens - algal or otherwise - or a decline in algal health. Our study highlights the importance of coupling 16S rRNA gene sequencing with whole genome data and other -omics tools to sketch a larger picture of community structure and function in crop systems. Moreover, our results highlight that continued long-term bleaching may lead to negative effects to crop health or downstream adverse health effects to humans or animals, depending on the algal product (i.e. human supplements or animal feedstocks). Future work on alternative treatment methods that would reduce resistance is necessary in the field.
Analysis of the recently completed Arabidopsis genome sequence indicates that approximately 31% of the predicted genes could not be assigned to functional categories, as they do not show any sequence similarity with proteins of known function from other organisms. Calmodulin (CaM), a ubiquitous and multifunctional Ca(2+) sensor, interacts with a wide variety of cellular proteins and modulates their activity/function in regulating diverse cellular processes. However, the primary amino acid sequence of the CaM-binding domain in different CaM-binding proteins (CBPs) is not conserved. One way to identify most of the CBPs in the Arabidopsis genome is by protein-protein interaction-based screening of expression libraries with CaM. Here, using a mixture of radiolabeled CaM isoforms from Arabidopsis, we screened several expression libraries prepared from flower meristem, seedlings, or tissues treated with hormones, an elicitor, or a pathogen. Sequence analysis of 77 positive clones that interact with CaM in a Ca(2+)-dependent manner revealed 20 CBPs, including 14 previously unknown CBPs. In addition, by searching the Arabidopsis genome sequence with the newly identified and known plant or animal CBPs, we identified a total of 27 CBPs. Among these, 16 CBPs are represented by families with 2-20 members in each family. Gene expression analysis revealed that CBPs and CBP paralogs are expressed differentially. Our data suggest that Arabidopsis has a large number of CBPs including several plant-specific ones. Although CaM is highly conserved between plants and animals, only a few CBPs are common to both plants and animals. Analysis of Arabidopsis CBPs revealed the presence of a variety of interesting domains. Our analyses identified several hypothetical proteins in the Arabidopsis genome as CaM targets, suggesting their involvement in Ca(2+)-mediated signaling networks.
Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.
Expansins are extracellular proteins that loosen plant cell walls in novel ways. They are thought to function in cell enlargement, pollen tube invasion of the stigma (in grasses), wall disassembly during fruit ripening, abscission and other cell separation events. Expansins are encoded by two multigene families and each gene is often expressed in highly specific locations and cell types. Structural analysis indicates that one expansin region resembles the catalytic domain of family-45 endoglucanases but glucanase activity has not been detected. The genome projects have revealed numerous expansin-related sequences but their putative wall-loosening functions remain to be assessed.
Phenazines (Phzs), a family of chemicals with a phenazine backbone, are secondary metabolites with diverse properties such as antibacterial, anti-fungal, or anticancer activity. The core derivatives of phenazine, phenazine-1-carboxylic acid (PCA) and phenazine-1,6-dicarboxylic acid (PDC), are themselves precursors for various other derivatives. Recent advances in genome mining tools have enabled researchers to identify many biosynthetic gene clusters (BGCs) that might produce novel Phzs. Here, to characterize the function of these BGCs efficiently, we performed modular construct assembly and subsequent multi-chassis heterologous expression using chassis-independent recombinase-assisted genome engineering (CRAGE). CRAGE allowed rapid integration of a PCA BGC into 23 diverse γ-proteobacteria species and allowed us to identify top PCA producers. We then used the top five chassis hosts to express four partially refactored PDC BGCs. A few of these platforms produced high levels of PDC. Specifically, Xenorhabdus doucetiae and Pseudomonas simiae produced PDC at a titer of 293 mg/L and 373 mg/L, respectively, in minimal media. These titers are significantly higher than those previously reported. Furthermore, selectivity toward PDC production over PCA production was improved by up to 9-fold. The results show that these strains are promising chassis for production of PCA, PDC, and their derivatives, as well as for function characterization of Phz BGCs identified via bioinformatics mining.
We report the complete genome sequence of Acidithiobacillus ferriphilus GT2, an acidophile isolated from gold mill tailings. The circular genome of GT2 contains 2,489 predicted protein-coding units and a single plasmid. Functional analysis indicates the metabolic potential to oxidize iron and reduced sulfur compounds and to fix N 2 and CO 2 .
Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.
Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.
Borrelia spirochetes are the causative agents of Lyme disease and relapsing fever, two of the most common tick-borne illnesses. A characteristic feature of these spirochetes is their highly segmented genomes which consists of a linear chromosome and a mixture of up to approximately 24 linear and circular extrachromosomal plasmids. The complexity of this genomic arrangement requires multiple strategies for efficient replication and partitioning during cell division, including the generation of hairpin ends found on linear replicons mediated by the essential enzyme ResT, a telomere resolvase. Using an integrative structural biology approach employing advanced modelling, circular dichroism, X-ray crystallography and small-angle X-ray scattering, we have generated high resolution structural data on ResT from B. garinii. Our data provides the first high-resolution structures of ResT from Borrelia spirochetes and revealed active site positioning in the catalytic domain. We also demonstrate that the C-terminal domain of ResT is required for both transesterification steps of telomere resolution, and is a requirement for DNA binding, distinguishing ResT from other telomere resolvases from phage and bacteria. These results advance our understanding of the molecular function of this essential enzyme involved in genome maintenance in Borrelia pathogens.
Hachiman is a broad-spectrum antiphage defense system of unknown function. We show here that Hachiman is a heterodimeric nuclease-helicase complex, HamAB. HamA, previously a protein of unknown function, is the effector nuclease. HamB is the sensor helicase. HamB constrains HamA activity during surveillance of intact double-stranded DNA (dsDNA). When the HamAB complex detects DNA damage, HamB helicase activity activates HamA, unleashing nuclease activity. Hachiman activation degrades all DNA in the cell, creating "phantom" cells devoid of both phage and host DNA. We demonstrate Hachiman activation in the absence of phage by treatment with DNA-damaging agents, suggesting that Hachiman responds to aberrant DNA states. Phylogenetic similarities between the Hachiman helicase and enzymes from eukaryotes and archaea suggest deep functional symmetries with other important helicases across domains of life.
Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes.
Highlights: • The DNA N6-mA modification increased during skeletal muscle development, accompanied by a strong decrease of ALKBH1. • ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. • ALKBH1 impaired multiple genes and signaling pathways to promote proliferation and inhibit differentiation of C2C12 cells. DNA N{sup 6}-methyladenine (N6-mA) was recently recognized as a new epigenetic modification in mammalian genome, and ALKBH1 was discovered as its demethylase. Knock-out mice studies revealed that ALKBH1 was indispensable for normal embryonic development. However, the function of ALKBH1 in myogenesis is largely unknown. In this study, we found that N6-mA showed a steady increase, going along with a strong decrease of ALKBH1 during skeletal muscle development. Our results also showed that ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. Genome-wide transcriptome analysis and reporter assays further revealed that ALKBH1 accomplished the differentiation inhibiting function by regulating a core set of genes and multiple signaling pathways, including increasing chemokine (C-X-C motif) ligand 14 (CXCL14) and activating ERK signaling. Taken together, our results demonstrated that ALKBH1 is critical for the myogenic differentiation of C2C12 cells, and suggested that N6-mA might be a new epigenetic mechanism for the regulation of myogenesis.
Climate change is affecting fungal communities and their function in terrestrial ecosystems. Despite making progress in the understanding of how the fungal community responds to global change drivers in natural ecosystems, little is known on how fungi respond at the species level. Understanding how fungal species respond to global change drivers, such as warming, is critical, as it could reveal adaptation pathways to help us to better understand ecosystem functioning in response to global change. Here, we present a model study to track species-level responses of fungi to warming—and associated drying—in a decade-long global change field experiment; we focused on two free-living saprotrophic fungi which were found in high abundance in our site, Mortierella and Penicillium. Using microbiological isolation techniques, combined with whole genome sequencing of fungal isolates, and community level metatranscriptomics, we investigated transcription-level differences of functional categories and specific genes involved in catabolic processes, cell homeostasis, cell morphogenesis, DNA regulation and organization, and protein biosynthesis. We found that transcription-level responses were mostly species-specific but that under warming, both fungi consistently invested in the transcription of critical genes involved in catabolic processes, cell morphogenesis, and protein biosynthesis, likely allowing them to withstand a decade of chronic stress. Overall, our work supports the idea that fungi that invest in maintaining their catabolic rates and processes while growing and protecting their cells may survive under global climate change.
SUMMARY The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., thedark genome). High‐throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation ofOPEN leaf, an open‐source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf‐specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high‐throughput screens to identify and characterize previously unidentified phenotypes in a leaf‐specific time‐dependent manner. Moreover, the modular and open‐source design ofOPEN leafallows seamless integration of additional sensors based on users and experimental needs.