Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. Hence, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe GJB2-related skin disease in one neonate; BRAT1-related lethal neonatal rigidity and multifocal seizure syndrome in another infant; identified BCL9L as a novel, recessive visceral heterotaxy gene (HTX6) in a pedigree; and ruled out known candidate genes in one infant. Sequencing of parents or affected siblings expedited the identification of disease genes in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling.

A major obstacle to curing chronic myeloid leukemia (CML) is residual disease maintained by tyrosine kinase inhibitor (TKI)-persistent leukemic stem cells (LSC). These are BCR-ABL1 kinase independent, refractory to apoptosis, and serve as a reservoir to drive relapse or TKI resistance. We demonstrate that Polycomb Repressive Complex 2 is misregulated in chronic phase CML LSCs. This is associated with extensive reprogramming of H3K27me3 targets in LSCs, thus sensitizing them to apoptosis upon treatment with an EZH2-specifi c inhibitor (EZH2i). EZH2i does not impair normal hematopoietic stem cell survival. Strikingly, treatment of primary CML cells with either EZH2i or TKI alone caused signifi cant upregulation of H3K27me3 targets, and combined treatment further potentiated these effects and resulted in signifi cant loss of LSCs compared to TKI alone, in vitro , and in long-term bone marrow murine xenografts. Our fi ndings point to a promising epigenetic-based therapeutic strategy to more effectively target LSCs in patients with CML receiving TKIs. SIGNIFICANCE:In CML, TKI-persistent LSCs remain an obstacle to cure, and approaches to eradicate them remain a signifi cant unmet clinical need. We demonstrate that EZH2 and H3K27me3 reprogramming is important for LSC survival, but renders LSCs sensitive to the combined effects of EZH2i and TKI. This represents a novel approach to more effectively target LSCs in patients receiving TKI treatment. Cancer Discov; 6(11); 1248-57.

It has recently been shown that nucleosome distribution, histone modifications and RNA polymerase II (Pol II) occupancy show preferential association with exons (“exon-intron marking”), linking chromatin structure and function to co-transcriptional splicing in a variety of eukaryotes. Previous ChIP-sequencing studies suggested that these marking patterns reflect the nucleosomal landscape. By analyzing ChIP-chip datasets across the human genome in three cell types, we have found that this marking system is far more complex than previously observed. We show here that a range of histone modifications and Pol II are preferentially associated with exons. However, there is noticeable cell-type specificity in the degree of exon marking by histone modifications and, surprisingly, this is also reflected in some histone modifications patterns showing biases towards introns. Exon-intron marking is laid down in the absence of transcription on silent genes, with some marking biases changing or becoming reversed for genes expressed at different levels. Furthermore, the relationship of this marking system with splicing is not simple, with only some histone modifications reflecting exon usage/inclusion, while others mirror patterns of exon exclusion. By examining nucleosomal distributions in all three cell types, we demonstrate that these histone modification patterns cannot solely be accounted for by differences in nucleosome levels between exons and introns. In addition, because of inherent differences between ChIP-chip array and ChIP-sequencing approaches, these platforms report different nucleosome distribution patterns across the human genome. Our findings confound existing views and point to active cellular mechanisms which dynamically regulate histone modification levels and account for exon-intron marking. We believe that these histone modification patterns provide links between chromatin accessibility, Pol II movement and co-transcriptional splicing.

Introductory programming classes are renowned for their high dropout rates. The authors propose that this is because students learn to adopt a fixed mindset towards programming. This paper reports on a study carried out with an introductory programming class, based on Dweck's mindset research. Combinations of three interventions were carried out: tutors taught mindset to students; growth mindset feedback messages were given to students on their work; and, when stuck, students were encouraged to use a crib sheet with pathways to solve problems. The study found that the mixture of teaching mindset and giving mindset messages on returned work resulted in a significant change in mindset and a corresponding significant change in test scores -improvements in test scores were found in a class test given immediately after the six-week intervention and at the end-of-year exam. The authors discuss the results and the strengths and weaknesses of the study.

Bioinformatics is the computing response to the molecular revolution in biology. This revolution has reshaped the life sciences and given us a deep understanding of DNA sequences, RNA synthesis, and the generation of proteins. In the process of achieving this revolution in understanding, we have accumulated vast amounts of data.The scale of this data, its structure, and the nature of the analytic task have merited serious attention from computer scientists and prompted work in intelligent systems, data mining, visualization, and more. It has also demanded serious efforts in large-scale data curation and developing a worldwide infrastructure to support this. Bioinformatics, the handmaiden of molecular biology, poses novel computational challenges, stretches the state of the art, and opens unanticipated uses of computing concepts. In tackling these, computer scientists have the additional satisfaction of contributing to a scientific Grand Challenge.Bioinformatics is, however, only the first step in reshaping the life sciences. For further progress, we must return to the study of whole biological systems: the heart, cardiovascular system, brain, and liver-systems biology. To build an integrated physiology of whole systems, we must combine data from the many rich areas of biological information. Alongside the genome, which constitutes our knowledge about genes, we place the proteome, metabolome, and physiome, which embody knowledge about proteins, metabolic processes, and physiology.Systems biology is at least as demanding as, and perhaps more demanding than, the genomic challenge that has fired international science and gained public attention. Progressing in this discipline will involve computer scientists working in close partnership with life scientists and mathematicians. In contrast to the molecular biology revolution, computer science will proactively engage in shaping the endeavor rather than just clearing up afterwards! The prize to be attained is immense. From in silico drug design and testing to individualized medicine that will take into account physiology and genetic profiles, systems biology has the potential to profoundly affect healthcare and medical science generally. THE ROLE OF MODELINGSuppose we had a catalog of all the gene sequences, how they translate to make proteins, and which proteins interact with each other. Further, assume Progress in the study of biological systems such as the heart, brain, and liver will require computer scientists to work closely with life scientists and mathematicians. Computer science will play a key role in shaping the new discipline of systems biology and addressing the significant computational challenges it poses.