Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relation-ships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able to discriminate GH7 CBHs and EGs with up to 99% ac-curacy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our machine learning results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function

Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours.

OBJECTIVE: To compare measurements of the arterial blood volume (aBV), a perfusion parameter calculated from arterial spin labelling (ASL), and cerebral blood volume (CBV), calculated from dynamic susceptibility contrast (DSC) MRI. In the clinic, CBV is used for grading of intracranial tumours. MATERIALS AND METHODS: Estimates of aBV from the model-free ASL technique quantitative STAR labelling of arterial regions (QUASAR) experiment and of DSC-CBV were obtained at 3T in ten patients with eleven tumours (three grade III gliomas, four glioblastomas and four meningiomas, two in one patient). Parametric values of aBV and CBV were determined in the tumour as well as in normal grey matter (GM), and tumour-to-GM aBV and CBV ratios were calculated. RESULTS: In a 4-pixel ROI representing maximal tumour values, the coefficient of determination R (2) was 0.61 for the comparison of ASL-based aBV tumour-to-GM ratios and DSC-MRI-based CBV tumour-to-GM ratios and 0.29 for the comparison of parametric values of ASL-aBV and DSC-CBV, under the assumption of proportionality. Both aBV and CBV showed a non-significant tendency to increase when going from grade III gliomas to glioblastomas to meningiomas. CONCLUSION: These results suggest that measurement of aBV is a potential tool for non-invasive assessment of blood volume in intracranial tumours

Refining complexity analyses in planning by exploiting the exponential time hypothesis

The use of computational complexity in planning, and in AI in general, has always been a disputed topic. A major problem with ordinary worst-case analyses is that they do not provide any quantitative information: they do not tell us much about the running time of concrete algorithms, nor do they tell us much about the running time of optimal algorithms. We address problems like this by presenting results based on the exponential time hypothesis (ETH), which is a widely accepted hypothesis concerning the time complexity of 3-SAT. By using this approach, we provide, for instance, almost matching upper and lower bounds onthe time complexity of propositional planning.Funding Agencies|National Graduate School in Computer Science (CUGS), Sweden; Swedish Research Council (VR) [621-2014-4086]</p

Prenatal maternal plasma DNA screening for cystic fibrosis: A computer modelling study of screening performance.

Background: Prenatal cystic fibrosis (CF) screening is currently based on determining the carrier status of both parents. We propose a new method based only on the analysis of DNA in maternal plasma. Methods: The method relies on the quantitative amplification of the CF gene to determine the percentage of DNA fragments in maternal plasma at targeted CF mutation sites that carry a CF mutation. Computer modelling was carried out to estimate the distributions of these percentages in pregnancies with and without a fetus affected with CF. This was done according to the number of DNA fragments counted and fetal fraction, using the 23 CF mutations recommended by the American College of Medical Genetics for parental carrier testing. Results: The estimated detection rate (sensitivity) is 70% (100% of those detected using the 23 mutations), the false-positive rate 0.002%, and the odds of being affected given a positive screening result 14:1, compared with 70%, 0.12%, and 1:3, respectively, with current prenatal screening based on parental carrier testing. Conclusions: Compared with current screening practice based on parental carrier testing, the proposed method would substantially reduce the number of invasive diagnostic procedures (amniocentesis or chorionic villus sampling) without reducing the CF detection rate. The expected advantages of the proposed method justify carrying out the necessary test development for use in a clinical validation study.The author(s) declared that no grants were involved in supporting this work

Variations of the Candidate SEZ6L2 Gene on Chromosome 16p11.2 in Patients with Autism Spectrum Disorders and in Human Populations

Background: Autism spectrum disorders (ASD) are a group of severe childhood neurodevelopmental disorders with still unknown etiology. One of the most frequently reported associations is the presence of recurrent de novo or inherited microdeletions and microduplications on chromosome 16p11.2. The analysis of rare variations of 8 candidate genes among the 27 genes located in this region suggested SEZ6L2 as a compelling candidate. Methodology/Principal Findings: We further explored the role of SEZ6L2 variations by screening its coding part in a group of 452 individuals, including 170 patients with ASD and 282 individuals from different ethnic backgrounds of the Human Genome Diversity Panel (HGDP), complementing the previously reported screening. We detected 7 previously unidentified non-synonymous variations of SEZ6L2 in ASD patients. We also identified 6 non-synonymous variations present only in HGDP. When we merged our results with the previously published, no enrichment of non-synonymous variation in SEZ6L2 was observed in the ASD group compared with controls. Conclusions/Significance: Our results provide an extensive ascertainment of the genetic variability of SEZ6L2 in human populations and do not support a major role for SEZ6L2 sequence variations in the susceptibility to ASD

Compartmental Genomics in Living Cells Revealed by Single-Cell Nanobiopsy

The ability to study the molecular biology of living single cells in heterogeneous cell populations is essential for next generation analysis of cellular circuitry and function. Here, we developed a single-cell nanobiopsy platform based on scanning ion conductance microscopy (SICM) for continuous sampling of intracellular content from individual cells. The nanobiopsy platform uses electrowetting within a nanopipette to extract cellular material from living cells with minimal disruption of the cellular milieu. We demonstrate the subcellular resolution of the nanobiopsy platform by isolating small subpopulations of mitochondria from single living cells, and quantify mutant mitochondrial genomes in those single cells with high throughput sequencing technology. These findings may provide the foundation for dynamic subcellular genomic analysis

Distinct Cytoplasmic and Nuclear Functions of the Stress Induced Protein DDIT3/CHOP/GADD153

DDIT3, also known as GADD153 or CHOP, encodes a basic leucine zipper transcription factor of the dimer forming C/EBP family. DDIT3 is known as a key regulator of cellular stress response, but its target genes and functions are not well characterized. Here, we applied a genome wide microarray based expression analysis to identify DDIT3 target genes and functions. By analyzing cells carrying tamoxifen inducible DDIT3 expression constructs we show distinct gene expression profiles for cells with cytoplasmic and nuclear localized DDIT3. Of 175 target genes identified only 3 were regulated by DDIT3 in both cellular localizations. More than two thirds of the genes were downregulated, supporting a role for DDIT3 as a dominant negative factor that could act by either cytoplasmic or nuclear sequestration of dimer forming transcription factor partners. Functional annotation of target genes showed cell migration, proliferation and apoptosis/survival as the most affected categories. Cytoplasmic DDIT3 affected more migration associated genes, while nuclear DDIT3 regulated more cell cycle controlling genes. Cell culture experiments confirmed that cytoplasmic DDIT3 inhibited migration, while nuclear DDIT3 caused a G1 cell cycle arrest. Promoters of target genes showed no common sequence motifs, reflecting that DDIT3 forms heterodimers with several alternative transcription factors that bind to different motifs. We conclude that expression of cytoplasmic DDIT3 regulated 94 genes. Nuclear translocation of DDIT3 regulated 81 additional genes linked to functions already affected by cytoplasmic DDIT3. Characterization of DDIT3 regulated functions helps understanding its role in stress response and involvement in cancer and degenerative disorders