A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.     

Coxsackievirus A16 Infection Induces Neural Cell and Non-Neural Cell Apoptosis In Vitro

Coxsackievirus A16 (CA16) is one of the main causative pathogens of hand, foot and mouth disease (HFMD). Viral replication typically results in host cell apoptosis. Although CA16 infection has been reported to induce apoptosis in the human rhabdomyosarcoma (RD) cell line, it remains unclear whether CA16 induces apoptosis in diverse cell types, especially neural cells which have important clinical significance. In the current study, CA16 infection was found to induce similar apoptotic responses in both neural cells and non-neural cells in vitro, including nuclear fragmentation, DNA fragmentation and phosphatidylserine translocation. CA16 generally is not known to lead to serious neurological symptoms in vivo. In order to further clarify the correlation between clinical symptoms and cell apoptosis, two CA16 strains from patients with different clinical features were investigated. The results showed that both CA16 strains with or without neurological symptoms in infected patients led to neural and muscle cell apoptosis. Furthermore, mechanistic studies showed that CA16 infection induced apoptosis through the same mechanism in both neural and non-neural cells, namely via activation of both the mitochondrial (intrinsic) pathway-related caspase 9 protein and the Fas death receptor (extrinsic) pathway-related caspase 8 protein. Understanding the mechanisms by which CA16 infection induces apoptosis in both neural and non-neural cells will facilitate a better understanding of CA16 pathogenesis.     

Dropout typologies: Relating profiles of risk and support to later educational re-engagement

A large body of work has examined factors that push and pull youth to drop out. However, a relatively minimal amount of work has examined whether and how these factors cluster in individuals’ lives preceding or concurrent to leaving school. This study used Latent Class Analysis with a national sample (N?=?1,942) to examine how push, pull, and protective experiences clustered in the lives of individuals who left high school without graduating. Then, we asked how the resultant classes differentially predicted youth re-engagement in educational endeavors. We identified three classes: youth with the presence of protective factors and absence of push/pull factors (Quiet Dropouts), youth with the presence of protective factors and an abundance of push/pull factors (High Adversity), and youth with the presence of instability in factors related to social relationships and school or housing (Instability). Results indicated each profile differentially predicted youths’ re-engagement in education and achievement of educational outcomes.     

Oncology drug discovery applications using the FMAT 8100 HTS system

High-throughput screening (HTS) for potential anticancer agents requires a broad portfolio of assay platforms that may include kinase enzyme assays, protein-protein binding assays, and functional cell-based apoptosis assays. The authors have explored the use of fluorometric microvolume assay technology (the FMAT 8100 HTS System) in three distinct homogeneous HTS assays: (1). a Src tyrosine kinase enzyme assay, (2). a Grb2-SH2 protein-peptide interaction assay, and (3). an annexin V binding apoptosis assay. Data obtained from all three assays suggest that the FMAT system should facilitate the implementation of homogeneous assays for a wide variety of molecular targeted and cell-based screens.     

Nuclear localization of leukotriene A4 hydrolase in type II alveolar epithelial cells in normal and fibrotic lung

Leukotriene A4 (LTA4) hydrolase catalyzes the final step in leukotriene B4 (LTB4) synthesis. In addition to its role in LTB4 synthesis, the enzyme possesses aminopeptidase activity. In this study, we sought to define the subcellular distribution of LTA4 hydrolase in alveolar epithelial cells, which lack 5-lipoxygenase and do not synthesize LTA4. Immunohistochemical staining localized LTA4 hydrolase in the nucleus of type II but not type I alveolar epithelial cells of normal mouse, human, and rat lungs. Nuclear localization of LTA4 hydrolase was also demonstrated in proliferating type II-like A549 cells. The apparent redistribution of LTA4 hydrolase from the nucleus to the cytoplasm during type II-to-type I cell differentiation in vivo was recapitulated in vitro. Surprisingly, this change in localization of LTA4 hydrolase did not affect the capacity of isolated cells to convert LTA4 to LTB4. However, proliferation of A549 cells was inhibited by the aminopeptidase inhibitor bestatin. Nuclear accumulation of LTA4 hydrolase was also conspicuous in epithelial cells during alveolar repair following bleomycin-induced acute lung injury in mice, as well as in hyperplastic type II cells associated with fibrotic lung tissues from patients with idiopathic pulmonary fibrosis. These results show for the first time that LTA4 hydrolase can be accumulated in the nucleus of type II alveolar epithelial cells and that redistribution of the enzyme to the cytoplasm occurs with differentiation to the type I phenotype. Furthermore, the aminopeptidase activity of LTA4 hydrolase within the nucleus may play a role in promoting epithelial cell growth.     

Absence of XMRV in Peripheral Blood Mononuclear Cells of ARV-Treatment Na?ve HIV-1 Infected and HIV-1/HCV Coinfected Individuals and Blood Donors

Background

Xenotropic murine leukemia virus-related virus (XMRV) has been found in the prostatic tissue of prostate cancer patients and in the blood of chronic fatigue syndrome patients. However, numerous studies have found little to no trace of XMRV in different human cohorts. Based on evidence suggesting common transmission routes between XMRV and HIV-1, HIV-1 infected individuals may represent a high-risk group for XMRV infection and spread.

Methodology/Principal Findings

DNA was isolated from the peripheral blood mononuclear cells (PBMCs) of 179 HIV-1 infected treatment naïve patients, 86 of which were coinfected with HCV, and 54 healthy blood donors. DNA was screened for XMRV provirus with two sensitive, published PCR assays targeting XMRV gag and env and one sensitive, published nested PCR assay targeting env. Detection of XMRV was confirmed by DNA sequencing. One of the 179 HIV-1 infected patients tested positive for gag by non-nested PCR whereas the two other assays did not detect XMRV in any specimen. All healthy blood donors were negative for XMRV proviral sequences. Sera from 23 HIV-1 infected patients (15 HCV⁺) and 12 healthy donors were screened for the presence of XMRV-reactive antibodies by Western blot. Thirteen sera (57%) from HIV-1⁺ patients and 6 sera (50%) from healthy donors showed reactivity to XMRV-infected cell lysate.

Conclusions/Significance

The virtual absence of XMRV in PBMCs suggests that XMRV is not associated with HIV-1 infected or HIV-1/HCV coinfected patients, or blood donors. Although we noted isolated incidents of serum reactivity to XMRV, we are unable to verify the antibodies as XMRV specific.     

Partial trisomy 10p of paternal origin. 2 new cases in 2 different families]

Two unrelated patients with partial trisomy 10p due to a paternal balanced translocation are reported. Though the sizes of the trisomic segment are not identical, both patients show: severe growth retardation, important psychomotor retardation and a dysmorphic mouth recalling a "tortois mouth". These observations are compared to twelve others from the literature.