Deletion of Autism Risk Gene Shank3 Disrupts Prefrontal Connectivity.

Mutations in the synaptic scaffolding protein SHANK3 are a major cause of autism and are associated with prominent intellectual and language deficits. However, the neural mechanisms whereby SHANK3 deficiency affects higher-order socio-communicative functions remain unclear. Using high-resolution functional and structural MRI in adult male mice, here we show that loss of Shank3 (Shank3B-/-) results in disrupted local and long-range prefrontal and frontostriatal functional connectivity. We document that prefrontal hypoconnectivity is associated with reduced short-range cortical projections density, and reduced gray matter volume. Finally, we show that prefrontal disconnectivity is predictive of social communication deficits, as assessed with ultrasound vocalization recordings. Collectively, our results reveal a critical role of SHANK3 in the development of prefrontal anatomy and function, and suggest that SHANK3 deficiency may predispose to intellectual disability and socio-communicative impairments via dysregulation of higher-order cortical connectivity.SIGNIFICANCE STATEMENT Mutations in the synaptic scaffolding protein SHANK3 are commonly associated with autism, intellectual, and language deficits. Previous research has linked SHANK3 deficiency to basal ganglia dysfunction, motor stereotypies, and social deficits. However, the neural mechanism whereby Shank3 gene mutations affects cortical functional connectivity and higher-order socio-communicative functions remain unclear. Here we show that loss of SHANK3 in mice results in largely disrupted functional connectivity and abnormal gray matter anatomy in prefrontal areas. We also show that prefrontal connectivity disruption is tightly linked to socio-communicative deficits. Our findings suggest that SHANK3 is a critical orchestrator of frontocortical function, and that disrupted connectivity of prefrontal areas may underpin socio-communicative impairments observed in SHANK3 mutation carriers.

Autism-associated 16p11.2 microdeletion impairs prefrontal functional connectivity in mouse and human.

Human genetic studies are rapidly identifying variants that increase risk for neurodevelopmental disorders. However, it remains unclear how specific mutations impact brain function and contribute to neuropsychiatric risk. Chromosome 16p11.2 deletion is one of the most common copy number variations in autism and related neurodevelopmental disorders. Using resting state functional MRI data from the Simons Variation in Individuals Project (VIP) database, we show that 16p11.2 deletion carriers exhibit impaired prefrontal connectivity, resulting in weaker long-range functional coupling with temporal-parietal regions. These functional changes are associated with socio-cognitive impairments. We also document that a mouse with the same genetic deficiency exhibits similarly diminished prefrontal connectivity, together with thalamo-prefrontal miswiring and reduced long-range functional synchronization. These results reveal a mechanistic link between specific genetic risk for neurodevelopmental disorders and long-range functional coupling, and suggest that deletion in 16p11.2 may lead to impaired socio-cognitive function via dysregulation of prefrontal connectivity.

Homozygous Loss of Autism-Risk Gene CNTNAP2 Results in Reduced Local and Long-Range Prefrontal Functional Connectivity.

Functional connectivity aberrancies, as measured with resting-state functional magnetic resonance imaging (rsfMRI), have been consistently observed in the brain of autism spectrum disorders (ASD) patients. However, the genetic and neurobiological underpinnings of these findings remain unclear. Homozygous mutations in contactin associated protein-like 2 (CNTNAP2), a neurexin-related cell-adhesion protein, are strongly linked to autism and epilepsy. Here we used rsfMRI to show that homozygous mice lacking Cntnap2 exhibit reduced long-range and local functional connectivity in prefrontal and midline brain "connectivity hubs." Long-range rsfMRI connectivity impairments affected heteromodal cortical regions and were prominent between fronto-posterior components of the mouse default-mode network, an effect that was associated with reduced social investigation, a core "autism trait" in mice. Notably, viral tracing revealed reduced frequency of prefrontal-projecting neural clusters in the cingulate cortex of Cntnap2-/- mutants, suggesting a possible contribution of defective mesoscale axonal wiring to the observed functional impairments. Macroscale cortico-cortical white-matter organization appeared to be otherwise preserved in these animals. These findings reveal a key contribution of ASD-associated gene CNTNAP2 in modulating macroscale functional connectivity, and suggest that homozygous loss-of-function mutations in this gene may predispose to neurodevelopmental disorders and autism through a selective dysregulation of connectivity in integrative prefrontal areas.

Water, Energy, and Carbon Footprints of Bioethanol from the U.S. and Brazil.

Driven by biofuel policies, which aim to reduce greenhouse gas (GHG) emissions and increase domestic energy supply, global production and consumption of bioethanol have doubled between 2007 and 2016, with rapid growth in corn-based bioethanol in the U.S. and sugar cane-based bioethanol in Brazil. Advances in crop yields, energy use efficiency in fertilizer production, biomass-to-ethanol conversion rates, and energy efficiency in ethanol production have improved the energy balance and GHG emission reduction potential of bioethanol. In the current study, the water, energy, and carbon footprints of bioethanol from corn in the U.S. and sugar cane in Brazil were assessed. The results show that U.S. corn bioethanol has a smaller water footprint (541 L water/L bioethanol) than Brazilian sugar cane bioethanol (1115 L water/L bioethanol). Brazilian sugar cane bioethanol has, however, a better energy balance (17.7 MJ/L bioethanol) and smaller carbon footprint (38.5 g CO2e/MJ) than U.S. bioethanol, which has an energy balance of 11.2 MJ/L bioethanol and carbon footprint of 44.9 g CO2e/MJ. The results show regional differences in the three footprints and highlight the need to take these differences into consideration to understand the implications of biofuel production for local water resources, net energy production, and climate change mitigation.

Functional connectivity hubs of the mouse brain.

Recent advances in functional connectivity methods have made it possible to identify brain hubs - a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states.

Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network.

The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN.

Emissions savings in the corn-ethanol life cycle from feeding coproducts to livestock.

Environmental regulations on greenhouse gas (GHG) emissions from corn (Zea mays L.)-ethanol production require accurate assessment methods to determine emissions savings from coproducts that are fed to livestock. We investigated current use of coproducts in livestock diets and estimated the magnitude and variability in the GHG emissions credit for coproducts in the corn-ethanol life cycle. The coproduct GHG emissions credit varied by more than twofold, from 11.5 to 28.3 g CO(2)e per MJ of ethanol produced, depending on the fraction of coproducts used without drying, the proportion of coproduct used to feed beef cattle (Bos taurus) vs. dairy or swine (Sus scrofa), and the location of corn production. Regional variability in the GHG intensity of crop production and future livestock feeding trends will determine the magnitude of the coproduct GHG offset against GHG emissions elsewhere in the corn-ethanol life cycle. Expansion of annual U.S. corn-ethanol production to 57 billion liters by 2015, as mandated in current federal law, will require feeding of coproduct at inclusion levels near the biological limit to the entire U.S. feedlot cattle, dairy, and swine herds. Under this future scenario, the coproduct GHG offset will decrease by 8% from current levels due to expanded use by dairy and swine, which are less efficient in use of coproduct than beef feedlot cattle. Because the coproduct GHG credit represents 19 to 38% of total life cycle GHG emissions, accurate estimation of the coproduct credit is important for determining the net impact of corn-ethanol production on atmospheric warming and whether corn-ethanol producers meet state- and national-level GHG emissions regulations.

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted impaired or aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological underpinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to aberrant connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. Despite some obvious limitations in reliably modeling the full phenotypic spectrum of a complex developmental disorder like ASD, mouse models have played a central role in advancing our basic mechanistic and molecular understanding of this syndrome. Recent progress in mouse brain connectivity mapping via resting-state fMRI (rsfMRI) offers the opportunity to generate and test mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism. Here we discuss recent progress toward this goal, and illustrate initial examples of how the approach can be employed to establish causal links between ASD-related mutations, developmental processes, and brain connectional architecture. As the spectrum of genetic and pathophysiological components of ASD modeled in the mouse is rapidly expanding, the use of rsfMRI can advance our mechanistic understanding of the origin and significance of the connectional alterations associated with autism, and their heterogeneous expression across patient cohorts.

Group-wise functional community detection through joint Laplacian diagonalization.

There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature.

Accounting for indirect land-use change in the life cycle assessment of biofuel supply chains.

The expansion of land used for crop production causes variable direct and indirect greenhouse gas emissions, and other economic, social and environmental effects. We analyse the use of life cycle analysis (LCA) for estimating the carbon intensity of biofuel production from indirect land-use change (ILUC). Two approaches are critiqued: direct, attributional life cycle analysis and consequential life cycle analysis (CLCA). A proposed hybrid 'combined model' of the two approaches for ILUC analysis relies on first defining the system boundary of the resulting full LCA. Choices are then made as to the modelling methodology (economic equilibrium or cause-effect), data inputs, land area analysis, carbon stock accounting and uncertainty analysis to be included. We conclude that CLCA is applicable for estimating the historic emissions from ILUC, although improvements to the hybrid approach proposed, coupled with regular updating, are required, and uncertainly values must be adequately represented; however, the scope and the depth of the expansion of the system boundaries required for CLCA remain controversial. In addition, robust prediction, monitoring and accounting frameworks for the dynamic and highly uncertain nature of future crop yields and the effectiveness of policies to reduce deforestation and encourage afforestation remain elusive. Finally, establishing compatible and comparable accounting frameworks for ILUC between the USA, the European Union, South East Asia, Africa, Brazil and other major biofuel trading blocs is urgently needed if substantial distortions between these markets, which would reduce its application in policy outcomes, are to be avoided.

Error-tolerant EST database searches by tandem mass spectrometry and multiTag software.

The MultiTag method (Sunyaev et al., Anal. Chem. 2003 15, 1307-1315) employs multiple error-tolerant searches with peptide sequence tags (Mann and Wilm, Anal. Chem. 1994, 66, 4390-4399) for the identification of proteins from organisms with unsequenced genomes. Here we demonstrate that the error-tolerant capabilities of MultiTag increased the number of peptide alignments and improved the confidence of identifications in an EST database. The MultiTag outperformed conventional database searching software that only utilizes stringent matching of tandem mass spectra to nucleotide sequences of ESTs.

The morality of problem selection in proteomics.

The emerging power of new technologies in proteomics and the biological sciences to alter the human condition demands that scientists hold a new perspective on the social responsibilities of their research. Ethical theory can help scientists recognize not only those research projects that are harmful, but also those research paths that can create the greatest improvements in human health on a global scale. Whereas individual choices are important for the direction of scientific research, these choices may have limited social effects if they are not coordinated with larger institutional and inter-institutional structures. The perspective presented here calls for the Human Proteome Organization to recognize the ten most ethically significant proteomes to be characterized, with the hopes of rallying support and directing the research efforts of scientists in the proteomics community toward these goals.

Expanding the organismal scope of proteomics: cross-species protein identification by mass spectrometry and its implications.

Due to the limited applicability of conventional protein identification methods to the proteomes of organisms with unsequenced genomes, researchers have developed approaches to identify proteins using mass spectrometry and sequence similarity database searches. Both the integration of mass spectrometry with bioinformatics and genomic sequencing drive the expanding organismal scope of proteomics.

Enhanced photosynthesis and redox energy production contribute to salinity tolerance in Dunaliella as revealed by homology-based proteomics.

Salinity is a major limiting factor for the proliferation of plants and inhibits central metabolic activities such as photosynthesis. The halotolerant green alga Dunaliella can adapt to hypersaline environments and is considered a model photosynthetic organism for salinity tolerance. To clarify the molecular basis for salinity tolerance, a proteomic approach has been applied for identification of salt-induced proteins in Dunaliella. Seventy-six salt-induced proteins were selected from two-dimensional gel separations of different subcellular fractions and analyzed by mass spectrometry (MS). Application of nanoelectrospray mass spectrometry, combined with sequence-similarity database-searching algorithms, MS BLAST and MultiTag, enabled identification of 80% of the salt-induced proteins. Salinity stress up-regulated key enzymes in the Calvin cycle, starch mobilization, and redox energy production; regulatory factors in protein biosynthesis and degradation; and a homolog of a bacterial Na(+)-redox transporters. The results indicate that Dunaliella responds to high salinity by enhancement of photosynthetic CO(2) assimilation and by diversion of carbon and energy resources for synthesis of glycerol, the osmotic element in Dunaliella. The ability of Dunaliella to enhance photosynthetic activity at high salinity is remarkable because, in most plants and cyanobacteria, salt stress inhibits photosynthesis. The results demonstrated the power of MS BLAST searches for the identification of proteins in organisms whose genomes are not known and paved the way for dissecting molecular mechanisms of salinity tolerance in algae and higher plants.

MultiTag: multiple error-tolerant sequence tag search for the sequence-similarity identification of proteins by mass spectrometry.

The characterization of proteomes by mass spectrometry is largely limited to organisms with sequenced genomes. To identify proteins from organisms with unsequenced genomes, database sequences from related species must be employed for sequence-similarity protein identifications. Peptide sequence tags (Mann, 1994) have been used successfully for the identification of proteins in sequence databases using partially interpreted tandem mass spectra of tryptic peptides. We have extended the ability of sequence tag searching to the identification of proteins whose sequences are yet unknown but are homologous to known database entries. The MultiTag method presented here assigns statistical significance to matches of multiple error-tolerant sequence tags to a database entry and ranks alignments by their significance. The MultiTag approach has the distinct advantage over other sequence-similarity approaches of being able to perform sequence-similarity identifications using only very short (2-4) amino acid residue stretches of peptide sequences, rather than complete peptide sequences deduced by de novo interpretation of tandem mass spectra. This feature facilitates the identification of low abundance proteins, since noisy and low-intensity tandem mass spectra can be utilized.

Homology-based functional proteomics by mass spectrometry: application to the Xenopus microtubule-associated proteome.

The application of functional proteomics to important model organisms with unsequenced genomes is restricted because of the limited ability to identify proteins by conventional mass spectrometry (MS) methods. Here we applied MS and sequence-similarity database searching strategies to characterize the Xenopus laevis microtubule-associated proteome. We identified over 40 unique, and many novel, microtubule-bound proteins, as well as two macromolecular protein complexes involved in protein translation. This finding was corroborated by electron microscopy showing the presence of ribosomes on spindles assembled from frog egg extracts. Taken together, these results suggest that protein translation occurs on the spindle during meiosis in the Xenopus oocyte. These findings were made possible due to the application of sequence-similarity methods, which extended mass spectrometric protein identification capabilities by 2-fold compared to conventional methods.