A Peninsular Structure Coordinates Asynchronous Differentiation with Morphogenesis to Generate Pancreatic Islets.

The pancreatic islets of Langerhans regulate glucose homeostasis. The loss of insulin-producing ß cells within islets results in diabetes, and islet transplantation from cadaveric donors can cure the disease. In vitro production of whole islets, not just ß cells, will benefit from a better understanding of endocrine differentiation and islet morphogenesis. We used single-cell mRNA sequencing to obtain a detailed description of pancreatic islet development. Contrary to the prevailing dogma, we find islet morphology and endocrine differentiation to be directly related. As endocrine progenitors differentiate, they migrate in cohesion and form bud-like islet precursors, or "peninsulas" (literally "almost islands"). α cells, the first to develop, constitute the peninsular outer layer, and ß cells form later, beneath them. This spatiotemporal collinearity leads to the typical core-mantle architecture of the mature, spherical islet. Finally, we induce peninsula-like structures in differentiating human embryonic stem cells, laying the ground for the generation of entire islets in vitro.

Major ß cell-specific functions of NKX2.2 are mediated via the NK2-specific domain.

The consolidation of unambiguous cell fate commitment relies on the ability of transcription factors (TFs) to exert tissue-specific regulation of complex genetic networks. However, the mechanisms by which TFs establish such precise control over gene expression have remained elusive-especially in instances in which a single TF operates in two or more discrete cellular systems. In this study, we demonstrate that ß cell-specific functions of NKX2.2 are driven by the highly conserved NK2-specific domain (SD). Mutation of the endogenous NKX2.2 SD prevents the developmental progression of ß cell precursors into mature, insulin-expressing ß cells, resulting in overt neonatal diabetes. Within the adult ß cell, the SD stimulates ß cell performance through the activation and repression of a subset of NKX2.2-regulated transcripts critical for ß cell function. These irregularities in ß cell gene expression may be mediated via SD-contingent interactions with components of chromatin remodelers and the nuclear pore complex. However, in stark contrast to these pancreatic phenotypes, the SD is entirely dispensable for the development of NKX2.2-dependent cell types within the CNS. Together, these results reveal a previously undetermined mechanism through which NKX2.2 directs disparate transcriptional programs in the pancreas versus neuroepithelium.

Ultra-high-diversity factorizable libraries for efficient therapeutic discovery.

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Ranking reprogramming factors for cell differentiation.

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

Author Correction: Predictable and precise template-free CRISPR editing of pathogenic variants.

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

Predictable and precise template-free CRISPR editing of pathogenic variants.

Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.

spatzie: an R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions.

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay.

A key mechanism in cellular regulation is the ability of the transcriptional machinery to physically access DNA. Transcription factors interact with DNA to alter the accessibility of chromatin, which enables changes to gene expression during development or disease or as a response to environmental stimuli. However, the regulation of DNA accessibility via the recruitment of transcription factors is difficult to study in the context of the native genome because every genomic site is distinct in multiple ways. Here we introduce the multiplexed integrated accessibility assay (MIAA), an assay that measures chromatin accessibility of synthetic oligonucleotide sequence libraries integrated into a controlled genomic context with low native accessibility. We apply MIAA to measure the effects of sequence motifs on cell type-specific accessibility between mouse embryonic stem cells and embryonic stem cell-derived definitive endoderm cells, screening 7905 distinct DNA sequences. MIAA recapitulates differential accessibility patterns of 100-nt sequences derived from natively differential genomic regions, identifying E-box motifs common to epithelial-mesenchymal transition driver transcription factors in stem cell-specific accessible regions that become repressed in endoderm. We show that a single binding motif for a key regulatory transcription factor is sufficient to open chromatin, and classify sets of stem cell-specific, endoderm-specific, and shared accessibility-modifying transcription factor motifs. We also show that overexpression of two definitive endoderm transcription factors, T and Foxa2, results in changes to accessibility in DNA sequences containing their respective DNA-binding motifs and identify preferential motif arrangements that influence accessibility.

seqgra: principled selection of neural network architectures for genomics prediction tasks.

MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process. RESULTS: We show that seqgra can be used to (i) generate data under the assumption of a hypothesized model of genome regulation, (ii) identify neural network architectures capable of recovering the rules of said model and (iii) analyze a model's predictive performance as a function of training set size and the complexity of the rules behind the simulated data. AVAILABILITY AND IMPLEMENTATION: The source code of the seqgra package is hosted on GitHub (https://github.com/gifford-lab/seqgra). seqgra is a pip-installable Python package. Extensive documentation can be found at https://kkrismer.github.io/seqgra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning optimization of peptides for presentation by class II MHCs.

SUMMARY: T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by Major Histocompatibility Complex (MHC)-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovering differential genome sequence activity with interpretable and efficient deep learning.

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.

Detection of gene cis-regulatory element perturbations in single-cell transcriptomes.

We introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.

IDR2D identifies reproducible genomic interactions.

Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

Advancing clinical trials in nursing homes: A proposed roadmap to success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Pragmatic trials in long-term care: Challenges, opportunities, recommendations.

Randomized controlled trials are considered the most rigorous research design in efficacy and effectiveness research; however, such trials present numerous challenges that limit their applicability in real-world settings. As a consequence, pragmatic trials are increasingly viewed as a research design that overcomes some of these barriers with the potential to produce data that are more reproducible. Although pragmatic methodology in long-term care is receiving increasing attention as an approach to improve successful dissemination and implementation, pragmatic trials present complexities of their own. To address these complexities and related issues, experts with experience conducting pragmatic trials, developing nursing home policy, participating in advocacy efforts, and providing clinical care in long-term care settings participated in a virtual consensus conference funded by the National Institute on Aging in Spring 2021. Participants recommended 4 cross-cutting principles key to dissemination and implementation of pragmatic trial interventions: (1) engage stakeholders, (2) ensure diversity and inclusion, (3) assess organizational strain and readiness, and (4) learn from adaptations. Specifically related to implementation, participants provided 2 recommendations: (1) integrate interventions into existing workflows and (2) maintain agility and responsiveness. Finally, participants had 3 recommendations specific to dissemination: (1) package the message for the audience, (2) engage diverse audiences, and (3) apply dissemination and diffusion tools. Participants emphasized that implementation processes must be grounded in the perspectives of the people who will ultimately be responsible for implementing the intervention once it is proven to be effective. In addition, messaging must speak to long-term care staff and all others who have a stake in its outcomes. Although our understanding of dissemination and implementation strategies remains underdeveloped, this article is designed to guide long-term care researchers and community providers who are increasingly aware of the need for pragmatism in disseminating and implementing evidence-based care interventions.

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction.

The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated noncoding genetic variants. We present a novel TF binding motif representation, the k-mer set memory (KSM), which consists of a set of aligned k-mers that are overrepresented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix (PWM) models and other more complex motif models across a large set of ChIP-seq experiments. Furthermore, KSMs outperform PWMs and more complex motif models in predicting in vitro binding sites. KMAC also identifies correct motifs in more experiments than five state-of-the-art motif discovery methods. In addition, KSM-derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1600 ENCODE TF ChIP-seq data sets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of noncoding genetic variations.

Antibody complementarity determining region design using high-capacity machine learning.

MOTIVATION: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. RESULTS: Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. AVAILABILITY AND IMPLEMENTATION: Sequencing data of the phage panning experiment are deposited at NIH's Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Early COVID-19 First-Dose Vaccination Coverage Among Residents and Staff Members of Skilled Nursing Facilities Participating in the Pharmacy Partnership for Long-Term Care Program - United States, December 2020-January 2021.

Residents and staff members of long-term care facilities (LTCFs), because they live and work in congregate settings, are at increased risk for infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1,2). In particular, skilled nursing facilities (SNFs), LTCFs that provide skilled nursing care and rehabilitation services for persons with complex medical needs, have been documented settings of COVID-19 outbreaks (3). In addition, residents of LTCFs might be at increased risk for severe outcomes because of their advanced age or the presence of underlying chronic medical conditions (4). As a result, the Advisory Committee on Immunization Practices has recommended that residents and staff members of LTCFs be offered vaccination in the initial COVID-19 vaccine allocation phase (Phase 1a) in the United States (5). In December 2020, CDC launched the Pharmacy Partnership for Long-Term Care Program* to facilitate on-site vaccination of residents and staff members at enrolled LTCFs. To evaluate early receipt of vaccine during the first month of the program, the number of eligible residents and staff members in enrolled SNFs was estimated using resident census data from the National Healthcare Safety Network (NHSN†) and staffing data from the Centers for Medicare & Medicaid Services (CMS) Payroll-Based Journal.§ Among 11,460 SNFs with at least one vaccination clinic during the first month of the program (December 18, 2020-January 17, 2021), an estimated median of 77.8% of residents (interquartile range [IQR] = 61.3%- 93.1%) and a median of 37.5% (IQR = 23.2%- 56.8%) of staff members per facility received ≥1 dose of COVID-19 vaccine through the Pharmacy Partnership for Long-Term Care Program. The program achieved moderately high coverage among residents; however, continued development and implementation of focused communication and outreach strategies are needed to improve vaccination coverage among staff members in SNFs and other long-term care settings.

High resolution discovery of chromatin interactions.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.