An alcove at the acetyl-CoA synthase nickel active site is required for productive substrate CO binding and anaerobic carbon fixation.

One of seven natural CO2 fixation pathways, the anaerobic Wood-Ljungdahl Pathway (WLP) is unique in generating CO as a metabolic intermediate, operating through organometallic intermediates, and in conserving (versus utilizing) net ATP. The key enzyme in the WLP is acetyl-CoA synthase (ACS), which uses an active site [2Ni-4Fe-4S] cluster (A-cluster), a CO tunnel, and an organometallic (Ni-CO, Ni-methyl, and Ni-acetyl) reaction sequence to generate acetyl-CoA. Here we reveal that an alcove, which interfaces the tunnel and the A-cluster, is essential for CO2 fixation and autotrophic growth by the WLP. In vitro spectroscopy, kinetics, binding, and in vivo growth experiments reveal that a Phe229A substitution at one wall of the alcove decreases CO affinity thirty-fold and abolishes autotrophic growth; however, a F229W substitution enhances CO binding 80-fold. Our results indicate the structure of the alcove is exquisitely tuned to concentrate CO near the A-cluster; protect ACS from CO loss during catalysis, provide a haven for inhibitory CO, and stabilize the tetrahedral coordination at the Nip site where CO binds. The directing, concentrating, and protective effects of the alcove explain the inability of F209A to grow autotrophically. The alcove also could help explain current controversies over whether ACS binds CO and methyl through a random or ordered mechanism. Our work redefines what we historically refer to as the metallocenter "active site". The alcove is so crucial for enzymatic function that we propose it is part of the active site. The community should now look for such alcoves in all "gas handling" metalloenzymes.

Flexible parametrization of graph-theoretical features from individual-specific networks for prediction.

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.

Targeting Bcl6 in the TREX1 D18N murine model ameliorates autoimmunity by modulating T-follicular helper cells and germinal center B cells.

The Three Prime Repair EXonuclease I (TREX1) is critical for degrading post-apoptosis DNA. Mice expressing catalytically inactive TREX1 (TREX1 D18N) develop lupus-like autoimmunity due to chronic sensing of undegraded TREX1 DNA substrates, production of the inflammatory cytokines, and the inappropriate activation of innate and adaptive immunity. This study aimed to investigate Thelper (Th) dysregulation in the TREX1 D18N model system as a potential mechanism for lupus-like autoimmunity. Comparison of immune cells in secondary lymphoid organs, spleen and peripheral lymph nodes (LNs) between TREX1 D18N mice and the TREX1 null mice revealed that the TREX1 D18N mice exhibit a Th1 bias. Additionally, the T-follicular helper cells (Tfh) and the germinal celter (GC) B cells were also elevated in the TREX1 D18N mice. Targeting Bcl6, a lineage-defining transcription factor for Tfh and GC B cells, with a commercially available Bcl6 inhibitor, FX1, attenuated Tfh, GC, and Th1 responses, and rescued TREX1 D18N mice from autoimmunity. The study presents Tfh and GC B-cell responses as potential targets in autoimmunity and that Bcl6 inhibitors may offer therapeutic approach in TREX1-associated or other lupus-like diseases.

Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum.

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

Altered default mode network associated with pesticide exposure in Latinx children from rural farmworker families.

Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.

Required Gene Set for Autotrophic Growth of Clostridium autoethanogenum.

The majority of the genes present in bacterial genomes remain poorly characterized, with up to one-third of those that are protein encoding having no definitive function. Transposon insertion sequencing represents a high-throughput technique that can help rectify this deficiency. The technology, however, can only be realistically applied to those species in which high rates of DNA transfer can be achieved. Here, we have developed a number of approaches that overcome this barrier in the autotrophic species Clostridium autoethanogenum by using a mariner-based transposon system. The inherent instability of such systems in the Escherichia coli conjugation donor due to transposition events was counteracted through the incorporation of a conditionally lethal codA marker on the plasmid backbone. Relatively low frequencies of transformation of the plasmid into C. autoethanogenum were circumvented through the use of a plasmid that is conditional for replication coupled with the routine implementation of an Illumina library preparation protocol that eliminates plasmid-based reads. A transposon library was then used to determine the essential genes needed for growth using carbon monoxide as the sole carbon and energy source. IMPORTANCE Although microbial genome sequences are relatively easily determined, assigning gene function remains a bottleneck. Consequently, relatively few genes are well characterized, leaving the function of many as either hypothetical or entirely unknown. High-throughput transposon sequencing can help remedy this deficiency, but is generally only applicable to microbes with efficient DNA transfer procedures. These exclude many microorganisms of importance to humankind either as agents of disease or as industrial process organisms. Here, we developed approaches to facilitate transposon insertion sequencing in the acetogen Clostridium autoethanogenum, a chassis being exploited to convert single-carbon waste gases CO and CO2 into chemicals and fuels at an industrial scale. This allowed the determination of gene essentiality under heterotrophic and autotrophic growth, providing insights into the utilization of CO as a sole carbon and energy source. The strategies implemented are translatable and will allow others to apply transposon insertion sequencing to other microbes where DNA transfer has until now represented a barrier to progress.

In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design.

The design and optimization of biosynthetic pathways for industrially relevant, non-model organisms is challenging due to transformation idiosyncrasies, reduced numbers of validated genetic parts and a lack of high-throughput workflows. Here we describe a platform for in vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE) to accelerate this process. In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE by screening 54 different cell-free pathways for 3-hydroxybutyrate production and optimizing a six-step butanol pathway across 205 permutations using data-driven design. Observing a strong correlation (r = 0.79) between cell-free and cellular performance, we then scaled up our highest-performing pathway, which improved in vivo 3-HB production in Clostridium by 20-fold to 14.63 ± 0.48 g l-1. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.

T Cells Produce IFN-α in the TREX1 D18N Model of Lupus-like Autoimmunity.

Autoimmunity can result when cells fail to properly dispose of DNA. Mutations in the three-prime repair exonuclease 1 (TREX1) cause a spectrum of human autoimmune diseases resembling systemic lupus erythematosus. The cytosolic dsDNA sensor, cyclic GMP-AMP synthase (cGAS), and the stimulator of IFN genes (STING) are required for pathogenesis, but specific cells in which DNA sensing and subsequent type I IFN (IFN-I) production occur remain elusive. In this study, we demonstrate that TREX1 D18N catalytic deficiency causes dysregulated IFN-I signaling and autoimmunity in mice. Moreover, we show that bone marrow-derived cells drive this process. We identify both innate immune and, surprisingly, activated T cells as sources of pathological IFN-α production. These findings demonstrate that TREX1 enzymatic activity is crucial to prevent inappropriate DNA sensing and IFN-I production in immune cells, including normally low-level IFN-α-producing cells. These results expand our understanding of DNA sensing and innate immunity in T cells and may have relevance to the pathogenesis of human disease caused by TREX1 mutation.

High mobility group A2 (HMGA2) deficiency in pigs leads to dwarfism, abnormal fetal resource allocation, and cryptorchidism.

Expression of HMGA2 is strongly associated with body size and growth in mice and humans. In mice, inactivation of one or both alleles of Hmga2 results in body-size reductions of 20% and 60%, respectively. In humans, microdeletions involving the HMGA2 locus result in short stature, suggesting the function of the HMGA2 protein is conserved among mammals. To test this hypothesis, we generated HMGA2-deficient pigs via gene editing and somatic cell nuclear transfer (SCNT). Examination of growth parameters revealed that HMGA2-/+ male and female pigs were on average 20% lighter and smaller than HMGA2+/+ matched controls (P < 0.05). HMGA2-/- boars showed significant size reduction ranging from 35 to 85% of controls depending on age (P < 0.05), and organ weights were also affected (P < 0.05). HMGA2-/+ gilts and boars exhibited normal reproductive development and fertility, while HMGA2-/- boars were sterile due to undescended testes (cryptorchidism). Crossbreeding HMGA2-/+ boars and gilts produced litters lacking the HMGA2-/- genotype. However, analysis of day (D) D40 and D78 pregnancies indicated that HMGA2-/- fetuses were present at the expected Mendelian ratio, but placental abnormalities were seen in the D78 HMGA2-/- concepti. Additionally, HMGA2-/- embryos generated by gene editing and SCNT produced multiple pregnancies and viable offspring, indicating that lack of HMGA2 is not lethal per se. Overall, our results show that the effect of HMGA2 with respect to growth regulation is highly conserved among mammals and opens up the possibility of regulating body and organ size in a variety of mammalian species including food and companion animals.

Development of a clostridia-based cell-free system for prototyping genetic parts and metabolic pathways.

Gas fermentation by autotrophic bacteria, such as clostridia, offers a sustainable path to numerous bioproducts from a range of local, highly abundant, waste and low-cost feedstocks, such as industrial flue gases or syngas generated from biomass or municipal waste. Unfortunately, designing and engineering clostridia remains laborious and slow. The ability to prototype individual genetic part function, gene expression patterns, and biosynthetic pathway performance in vitro before implementing designs in cells could help address these bottlenecks by speeding up design. Unfortunately, a high-yielding cell-free gene expression (CFE) system from clostridia has yet to be developed. Here, we report the development and optimization of a high-yielding (236 ± 24 µg/mL) batch CFE platform from the industrially relevant anaerobe, Clostridium autoethanogenum. A key feature of the platform is that both circular and linear DNA templates can be applied directly to the CFE reaction to program protein synthesis. We demonstrate the ability to prototype gene expression, and quantitatively map aerobic cell-free metabolism in lysates from this system. We anticipate that the C. autoethanogenum CFE platform will not only expand the protein synthesis toolkit for synthetic biology, but also serve as a platform in expediting the screening and prototyping of gene regulatory elements in non-model, industrially relevant microbes.

The Influence of Diffusion Weighted Imaging Lesions on Outcomes in Patients with Acute Spontaneous Intracerebral Hemorrhage.

BACKGROUND/OBJECTIVE: Diffusion weighted imaging (DWI) lesions have been well described in patients with acute spontaneous intracerebral hemorrhage (sICH). However, there are limited data on the influence of these lesions on sICH functional outcomes. We conducted a prospective observational cohort study with blinded imaging and outcomes assessment to determine the influence of DWI lesions on long-term outcomes in patients with acute sICH. We hypothesized that DWI lesions are associated with worse modified Rankin Scale (mRS) at 3 months after hospital discharge. METHODS: Consecutive sICH patients meeting study criteria were consented for an magnetic resonance imaging (MRI) scan of the brain and evaluated for remote DWI lesions by neuroradiologists blinded to the patients' hospital course. Blinded mRS outcomes were obtained at 3 months. Logistic regression was used to determine significant factors (p < 0.05) associated with worse functional outcomes defined as an mRS of 4-6. The generalized estimating equation (GEE) approach was used to investigate the effect of DWI lesions on dichotomized mRS (0-3 vs 4-6) longitudinally. RESULTS: DWI lesions were found in 60 of 121 patients (49.6%). The presence of a DWI lesion was associated with increased odds for an mRS of 4-6 at 3 months (OR 5.987, 95% CI 1.409-25.435, p = 0.015) in logistic regression. Using the GEE model, patients with a DWI lesion were less likely to recover over time between 14 days/discharge and 3 months (p = 0.005). CONCLUSIONS: DWI lesions are common in primary sICH, occurring in almost half of our cohort. Our data suggest that DWI lesions are associated with worse mRS at 3 months in good grade sICH and are predictive of impaired recovery after hospital discharge. Further research into the pathophysiologic mechanisms underlying DWI lesions may lead to novel treatment options that may improve outcomes associated with this devastating disease.

Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Analysis of brain subnetworks within the context of their whole-brain networks.

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.

A MATLAB toolbox for multivariate analysis of brain networks.

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

Systems-level engineering and characterisation of Clostridium autoethanogenum through heterologous production of poly-3-hydroxybutyrate (PHB).

Gas fermentation is emerging as an economically attractive option for the sustainable production of fuels and chemicals from gaseous waste feedstocks. Clostridium autoethanogenum can use CO and/or CO2 + H2 as its sole carbon and energy sources. Fermentation of C. autoethanogenum is currently being deployed on a commercial scale for ethanol production. Expanding the product spectrum of acetogens will enhance the economics of gas fermentation. To achieve efficient heterologous product synthesis, limitations in redox and energy metabolism must be overcome. Here, we engineered and characterised at a systems-level, a recombinant poly-3-hydroxybutyrate (PHB)-producing strain of C. autoethanogenum. Cells were grown in CO-limited steady-state chemostats on two gas mixtures, one resembling syngas (20% H2) and the other steel mill off-gas (2% H2). Results were characterised using metabolomics and transcriptomics, and then integrated using a genome-scale metabolic model reconstruction. PHB-producing cells had an increased expression of the Rnf complex, suggesting energy limitations for heterologous production. Subsequent optimisation of the bioprocess led to a 12-fold increase in the cellular PHB content. The data suggest that the cellular redox state, rather than the acetyl-CoA pool, was limiting PHB production. Integration of the data into the genome-scale metabolic model showed that ATP availability limits PHB production. Altogether, the data presented here advances the fundamental understanding of heterologous product synthesis in gas-fermenting acetogens.

Wake Forest Alcohol Imagery Set: Development and Validation of a Large Standardized Alcohol Imagery Dataset.

BACKGROUND: The study of alcohol use frequency utilizes alcohol-related cue imagery. Although a number of alcohol-image databases currently exist, they have several limitations: Many are not publicly available, some use stock images or clip art rather than real photographs, several eliminate any photographs displaying brand information, and predominantly they contain relatively few images. The aim of this project was to develop a large, open-access database of alcohol-related cue images, containing photographs with and without brand information, taken in real-world environments, with images in a variety of orientations and dimensions. METHODS: The study collected 1,650 images voluntarily from the larger community, to capture photographs with a wide range of content, environments, and relation to alcohol. All images were then rated on scales of valence, arousal, and relation to alcohol by 1,008 Amazon Mechanical Turk workers, using classical emotion validation methods based on the International Affective Picture System (IAPS). Survey respondents were screened with the Alcohol Use Disorders Identification Test (AUDIT), and Cronbach's alpha scores were calculated to determine the interrater reliability of scores across the whole sample, and within low-risk, moderate-risk, and high-risk drinkers for each rating domain. Univariate ANOVAs were run to determine differences in ratings across drinking groups. RESULTS: All Cronbach's alpha scores indicated high interrater reliability within the whole sample, and across drinking severity groups. Tukey's HSD post hoc results indicated greater arousal and affect in response to image viewing in moderate- and high-risk drinkers, and higher relation-to-alcohol ratings in low-risk drinkers. All images had categorization tags assigned by members of the study team. CONCLUSIONS: The established imagery set includes 1,650 alcohol-related images, rated on scales of valence, arousal, and relation to alcohol, and categorized by type of alcohol depicted. The imagery database will be available for open-access download and use through Google Photos.

The carbonic anhydrase of Clostridium autoethanogenum represents a new subclass of ß-carbonic anhydrases.

Carbonic anhydrase catalyses the interconversion of carbon dioxide and water to bicarbonate and protons. It was unknown if the industrial-relevant acetogen Clostridium autoethanogenum possesses these enzymes. We identified two putative carbonic anhydrase genes in its genome, one of the ß class and one of the γ class. Carbonic anhydrase activity was found for the purified ß class enzyme, but not the γ class candidate. Functional complementation of an Escherichia coli carbonic anhydrase knock-out mutant showed that the ß class carbonic anhydrase could complement this activity, but not the γ class candidate gene. Phylogenetic analysis showed that the ß class carbonic anhydrase of Clostridium autoethanogenum represents a novel sub-class of ß class carbonic anhydrases that form the F-clade. The members of this clade have the shortest primary structure of any known carbonic anhydrase.

Engineering of vitamin prototrophy in Clostridium ljungdahlii and Clostridium autoethanogenum.

Clostridium autoethanogenum and Clostridium ljungdahlii are physiologically and genetically very similar strict anaerobic acetogens capable of growth on carbon monoxide as sole carbon source. While exact nutritional requirements have not been reported, we observed that for growth, the addition of vitamins to media already containing yeast extract was required, an indication that these are fastidious microorganisms. Elimination of complex components and individual vitamins from the medium revealed that the only organic compounds required for growth were pantothenate, biotin and thiamine. Analysis of the genome sequences revealed that three genes were missing from pantothenate and thiamine biosynthetic pathways, and five genes were absent from the pathway for biotin biosynthesis. Prototrophy in C. autoethanogenum and C. ljungdahlii for pantothenate was obtained by the introduction of plasmids carrying the heterologous gene clusters panBCD from Clostridium acetobutylicum, and for thiamine by the introduction of the thiC-purF operon from Clostridium ragsdalei. Integration of panBCD into the chromosome through allele-coupled exchange also conveyed prototrophy. C. autoethanogenum was converted to biotin prototrophy with gene sets bioBDF and bioHCA from Desulfotomaculum nigrificans strain CO-1-SRB, on plasmid and integrated in the chromosome. The genes could be used as auxotrophic selection markers in recombinant DNA technology. Additionally, transformation with a subset of the genes for pantothenate biosynthesis extended selection options with the pantothenate precursors pantolactone and/or beta-alanine. Similarly, growth was obtained with the biotin precursor pimelate combined with genes bioYDA from C. acetobutylicum. The work raises questions whether alternative steps exist in biotin and thiamine biosynthesis pathways in these acetogens.

A novel conjugal donor strain for improved DNA transfer into Clostridium spp.

Clostridium encompasses species which are relevant to human and animal disease as well as species which have industrial potential, for instance, as producers of chemicals and fuels or as tumour delivery vehicles. Genetic manipulation of these target organisms is critical for advances in these fields. DNA transfer efficiencies, however, vary between species. Low efficiencies can impede the progress of research efforts. A novel conjugal donor strain of Escherichia coli has been created which exhibits a greater than 10-fold increases in conjugation efficiency compared to the traditionally used CA434 strain in the three species tested; C. autoethanogenum DSM 10061, C. sporogenes NCIMB 10696 and C. difficile R20291. The novel strain, designated 'sExpress', does not methylate DNA at Dcm sites (CCWGG) which allows circumvention of cytosine-specific Type IV restriction systems. A robust protocol for conjugation is presented which routinely produces in the order of 105 transconjugants per millilitre of donor cells for C. autoethanogenum, 106 for C. sporogenes and 102 for C. difficile R20291. The novel strain created is predicted to be a superior conjugal donor in a wide range of species which possess Type IV restriction systems.

Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment.