A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3'UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3' end processing, and integrates these features into a comprehensive, interpretable, cis-regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.

Multiplexed direct detection of barcoded protein reporters on a nanopore array.

Detection of specific proteins using nanopores is currently challenging. To address this challenge, we developed a collection of over twenty nanopore-addressable protein tags engineered as reporters (NanoporeTERs, or NTERs). NTERs are constructed with a secretion tag, folded domain and a nanopore-targeting C-terminal tail in which arbitrary peptide barcodes can be encoded. We demonstrate simultaneous detection of up to nine NTERs expressed in bacterial or human cells using MinION nanopore sensor arrays.

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity. Here, we develop deep exploration networks (DENs), a class of activation-maximizing generative models, which minimize the cost of a neural network fitness predictor by gradient descent. By penalizing any two generated patterns on the basis of a similarity metric, DENs explicitly maximize sequence diversity. To avoid drifting into low-confidence regions of the predictor, we incorporate variational autoencoders to maintain the likelihood ratio of generated sequences. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns, identified splice regulatory sequences predicted to result in highly differential splicing between cell lines, and improved on state-of-the-art results for protein design tasks.

Gains and losses of metabolic function inferred from a phylotranscriptomic analysis of algae.

Hidden Markov models representing 167 protein sequence families were used to infer the presence or absence of homologs within the transcriptomes of 183 algal species/strains. Statistical analyses of the distribution of HMM hits across major clades of algae, or at branch points on the phylogenetic tree of 98 chlorophytes, confirmed and extended known cases of metabolic loss and gain, most notably the loss of the mevalonate pathway for terpenoid synthesis in green algae but not, as we show here, in the streptophyte algae. Evidence for novel events was found as well, most remarkably in the recurrent and coordinated gain or loss of enzymes for the glyoxylate shunt. We find, as well, a curious pattern of retention (or re-gain) of HMG-CoA synthase in chlorophytes that have otherwise lost the mevalonate pathway, suggesting a novel, co-opted function for this enzyme in select lineages. Finally, we find striking, phylogenetically linked distributions of coding sequences for three pathways that synthesize the major membrane lipid phosphatidylcholine, and a complementary phylogenetic distribution pattern for the non-phospholipid DGTS (diacyl-glyceryl-trimethylhomoserine). Mass spectrometric analysis of lipids from 25 species was used to validate the inference of DGTS synthesis from sequence data.

Rab11 is required for epithelial cell viability, terminal differentiation, and suppression of tumor-like growth in the Drosophila egg chamber.

BACKGROUND: The Drosophila egg chamber provides an excellent system in which to study the specification and differentiation of epithelial cell fates because all of the steps, starting with the division of the corresponding stem cells, called follicle stem cells, have been well described and occur many times over in a single ovary. METHODOLOGY/PRINCIPAL FINDINGS: Here we investigate the role of the small Rab11 GTPase in follicle stem cells (FSCs) and in their differentiating daughters, which include main body epithelial cells, stalk cells and polar cells. We show that rab11-null FSCs maintain their ability to self renew, even though previous studies have shown that FSC self renewal is dependent on maintenance of E-cadherin-based intercellular junctions, which in many cell types, including Drosophila germline stem cells, requires Rab11. We also show that rab11-null FSCs give rise to normal numbers of cells that enter polar, stalk, and epithelial cell differentiation pathways, but that none of the cells complete their differentiation programs and that the epithelial cells undergo premature programmed cell death. Finally we show, through the induction of rab11-null clones at later points in the differentiation program, that Rab11 suppresses tumor-like growth of epithelial cells. Thus, rab11-null epithelial cells arrest differentiation early, assume an aberrant cell morphology, delaminate from the epithelium, and invade the neighboring germline cyst. These phenotypes are associated with defects in E-cadherin localization and a general loss of cell polarity. CONCLUSIONS/SIGNIFICANCE: While previous studies have revealed tumor suppressor or tumor suppressor-like activity for regulators of endocytosis, our study is the first to identify such activity for regulators of endocytic recycling. Our studies also support the recently emerging view that distinct mechanisms regulate junction stability and plasticity in different tissues.

Rab11 maintains connections between germline stem cells and niche cells in the Drosophila ovary.

All stem cells have the ability to balance their production of self-renewing and differentiating daughter cells. The germline stem cells (GSCs) of the Drosophila ovary maintain such balance through physical attachment to anterior niche cap cells and stereotypic cell division, whereby only one daughter remains attached to the niche. GSCs are attached to cap cells via adherens junctions, which also appear to orient GSC division through capture of the fusome, a germline-specific organizer of mitotic spindles. Here we show that the Rab11 GTPase is required in the ovary to maintain GSC-cap cell junctions and to anchor the fusome to the anterior cortex of the GSC. Thus, rab11-null GSCs detach from niche cap cells, contain displaced fusomes and undergo abnormal cell division, leading to an early arrest of GSC differentiation. Such defects are likely to reflect a role for Rab11 in E-cadherin trafficking as E-cadherin accumulates in Rab11-positive recycling endosomes (REs) and E-cadherin and Armadillo (beta-catenin) are both found in reduced amounts on the surface of rab11-null GSCs. The Rab11-positive REs through which E-cadherin transits are tightly associated with the fusome. We propose that this association polarizes the trafficking by Rab11 of E-cadherin and other cargoes toward the anterior cortex of the GSC, thus simultaneously fortifying GSC-niche junctions, fusome localization and asymmetric cell division. These studies bring into focus the important role of membrane trafficking in stem cell biology.