Poly(A) polymerase (PAP) is present in multiple forms in mammalian cells and tissues. Here we show that the 90-kDa isoform is the product of the gene PAPOLG, which is distinct from the previously identified genes for poly(A) polymerases. The 90-kDa isoform is referred to as human PAP␥ (hsPAP␥). hsPAP␥ shares 60% identity to human PAPII (hsPAPII) at the amino acid level. hsPAP␥ exhibits fundamental properties of a bona fide poly(A) polymerase, specificity for ATP, and cleavage and polyadenylation specificity factor/hexanucleotidedependent polyadenylation activity. The catalytic parameters indicate similar catalytic efficiency to that of hsPAPII. Mutational analysis and sequence comparison revealed that hsPAP␥ and hsPAPII have similar organization of structural and functional domains. hsPAP␥ contains a U1A protein-interacting region in its C terminus, and PAP␥ activity can be inhibited, as hsPAPII, by the U1A protein. hsPAP␥ is restricted to the nucleus as revealed by in situ staining and by transfection experiments. Based on this and previous studies, it is obvious that multiple isoforms of PAP are generated by three distinct mechanisms: gene duplication, alternative RNA processing, and post-translational modification. The exclusive nuclear localization of hsPAP␥ establishes that multiple forms of PAP are unevenly distributed in the cell, implying specialized roles for the various isoforms.

We have detected a surprising heterogeneity among human spliceosomal U1 small nuclear RNA (snRNA). Most interestingly, we have identified three U1 snRNA variants that lack complementarity to the canonical 59 splice site (59SS) GU dinucleotide. Furthermore, we have observed heterogeneity among the identified variant U1 snRNA genes caused by single nucleotide polymorphism (SNP). The identified snRNAs were ubiquitously expressed in a variety of human tissues representing different stages of development and displayed features of functional spliceosomal snRNAs, i.e., trimethylated cap structures, association with Sm proteins and presence in nuclear RNA-protein complexes. The unanticipated heterogeneity among spliceosomal snRNAs could contribute to the complexity of vertebrates by expanding the coding capacity of their genomes.

We have previously shown that a distal GU-rich downstream element of the mouse IgM secretory poly(A) site is important for polyadenylation in vivo and for polyadenylation specific complex formation in vitro. This element can be predicted to form a stem-loop structure with two asymmetric internal loops. As stem-loop structures commonly define protein RNA binding sites, we have probed the biological activity of the secondary structure of this element. We show that mutations affecting the stem of the structure abolish the biological activity of this element in vivo and in vitro at the level of cleavage and polyadenylation specificity factor/cleavage stimulation factor complex formation and that both internal loops contribute to the enhancing effect of the sequence in vivo. Lead (II) cleavage patterns and RNase H probing of the sequence element in vitro are consistent with the predicted secondary structure. Furthermore, mobility on native PAGE suggests a bent structure. We propose that the secondary structure of this downstream element optimizes its interaction with components of the polyadenylation complex.

The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies.Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5–20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained.Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data.Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high-quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects.Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas.A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous proces...

Biomedical research on Alzheimer’s disease (AD), breast cancer (BC) and prostate cancer (PC) has globally improved our understanding of the etiopathological mechanisms underlying the onset of these diseases, often with the goal to identify associated genetic and environmental risk factors and develop new medicines. However, the prevalence of these diseases and failure rate in drug development remain high. Being able to retrospectively monitor the major scientific breakthroughs and impact of such investment endeavors is important to re-address funding strategies if and when needed. The EU has supported research into those diseases via its successive framework programmes for research, technological development and innovation. The European Commission (EC) has already undertaken several activities to monitor research impact. As an additional contribution, the EC Joint Research Centre (JRC) launched in 2020 a survey addressed to former and current participants of EU-funded research projects in the fields of AD, BC and PC, with the aim to understand how EU-funded research has contributed to scientific innovation and societal impact, and how the selection of the experimental models may have underpinned the advances made. Further feedback was also gathered through in-depth interviews with some selected survey participants representative of the diverse pre-clinical models used in the EU-funded projects. A comprehensive analysis of survey replies, complemented with the information derived from the interviews, has recently been published in a Synopsis report. Here we discuss the main findings of this analysis and propose a set of priority actions that could be considered to help improving the translation of scientific innovation of biomedical research into societal impact.