Samuel J. Allon

Bio: Samuel J. Allon is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Somatic hypermutation & Affinity maturation. The author has an hindex of 6, co-authored 8 publications receiving 1457 citations. Previous affiliations of Samuel J. Allon include Ragon Institute of MGH, MIT and Harvard & Broad Institute.

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.

Abstract: Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

SARS-CoV-2 Receptor ACE2 is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Enriched in Specific Cell Subsets Across Tissues

Abstract: There is pressing urgency to better understand the pathogenesis of the severe acute respiratory syndrome (SARS) coronavirus (CoV) clade SARS-CoV-2. SARS-CoV-2, like SARS-CoV, utilizes ACE2 to bind host cells. While initial SARS-CoV-2 cell entry and infection depend on ACE2 in concert with the protease TMPRSS2 for spike (S) protein activation, the specific cell subsets targeted by SARS-CoV-2 in host tissues, and the factors that regulate ACE2 expression, remain unknown. Here, we leverage human and non-human primate (NHP) single-cell RNA-sequencing (scRNA-seq) datasets to uncover the cell subsets that may serve as cellular targets of SARS-CoV-2. We identify ACE2/TMPRSS2 co-expressing cells within type II pneumocytes, absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discover that ACE2 is an interferon-stimulated gene (ISG) in human barrier tissue epithelial cells. Thus, SARS-CoV-2 may exploit IFN-driven upregulation of ACE2, a key tissue-protective mediator during lung injury, to enhance infection.

Expression of Foxp3 by T follicular helper cells in end-stage germinal centers.

Abstract: Germinal centers (GCs) are the site of immunoglobulin somatic hypermutation and affinity maturation, processes essential to an effective antibody response. The formation of GCs has been studied in detail, but less is known about what leads to their regression and eventual termination, factors that ultimately limit the extent to which antibodies mature within a single reaction. We show that contraction of immunization-induced GCs is immediately preceded by an acute surge in GC-resident Foxp3+ T cells, attributed at least partly to up-regulation of the transcription factor Foxp3 by T follicular helper (TFH) cells. Ectopic expression of Foxp3 in TFH cells is sufficient to decrease GC size, implicating the natural up-regulation of Foxp3 by TFH cells as a potential regulator of GC lifetimes.

Cyclin D3 drives inertial cell cycling in dark zone germinal center B cells.

Abstract: During affinity maturation, germinal center (GC) B cells alternate between proliferation and somatic hypermutation in the dark zone (DZ) and affinity-dependent selection in the light zone (LZ). This anatomical segregation imposes that the vigorous proliferation that allows clonal expansion of positively selected GC B cells takes place ostensibly in the absence of the signals that triggered selection in the LZ, as if by "inertia." We find that such inertial cycles specifically require the cell cycle regulator cyclin D3. Cyclin D3 dose-dependently controls the extent to which B cells proliferate in the DZ and is essential for effective clonal expansion of GC B cells in response to strong T follicular helper (Tfh) cell help. Introduction into the Ccnd3 gene of a Burkitt lymphoma-associated gain-of-function mutation (T283A) leads to larger GCs with increased DZ proliferation and, in older mice, clonal B cell lymphoproliferation, suggesting that the DZ inertial cell cycle program can be coopted by B cells undergoing malignant transformation.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Extrapulmonary manifestations of COVID-19.

Abstract: Although COVID-19 is most well known for causing substantial respiratory pathology, it can also result in several extrapulmonary manifestations. These conditions include thrombotic complications, myocardial dysfunction and arrhythmia, acute coronary syndromes, acute kidney injury, gastrointestinal symptoms, hepatocellular injury, hyperglycemia and ketosis, neurologic illnesses, ocular symptoms, and dermatologic complications. Given that ACE2, the entry receptor for the causative coronavirus SARS-CoV-2, is expressed in multiple extrapulmonary tissues, direct viral tissue damage is a plausible mechanism of injury. In addition, endothelial damage and thromboinflammation, dysregulation of immune responses, and maladaptation of ACE2-related pathways might all contribute to these extrapulmonary manifestations of COVID-19. Here we review the extrapulmonary organ-specific pathophysiology, presentations and management considerations for patients with COVID-19 to aid clinicians and scientists in recognizing and monitoring the spectrum of manifestations, and in developing research priorities and therapeutic strategies for all organ systems involved.

Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages.

Abstract: The COVID-19 pandemic caused by infection with SARS-CoV-2 has led to more than 200,000 deaths worldwide. Several studies have now established that the hyperinflammatory response induced by SARS-CoV-2 is a major cause of disease severity and death in infected patients. Macrophages are a population of innate immune cells that sense and respond to microbial threats by producing inflammatory molecules that eliminate pathogens and promote tissue repair. However, a dysregulated macrophage response can be damaging to the host, as is seen in the macrophage activation syndrome induced by severe infections, including in infections with the related virus SARS-CoV. Here we describe the potentially pathological roles of macrophages during SARS-CoV-2 infection and discuss ongoing and prospective therapeutic strategies to modulate macrophage activation in patients with COVID-19.

Coronavirus biology and replication: implications for SARS-CoV-2.

Abstract: The SARS-CoV-2 pandemic and its unprecedented global societal and economic disruptive impact has marked the third zoonotic introduction of a highly pathogenic coronavirus into the human population. Although the previous coronavirus SARS-CoV and MERS-CoV epidemics raised awareness of the need for clinically available therapeutic or preventive interventions, to date, no treatments with proven efficacy are available. The development of effective intervention strategies relies on the knowledge of molecular and cellular mechanisms of coronavirus infections, which highlights the significance of studying virus-host interactions at the molecular level to identify targets for antiviral intervention and to elucidate critical viral and host determinants that are decisive for the development of severe disease. In this Review, we summarize the first discoveries that shape our current understanding of SARS-CoV-2 infection throughout the intracellular viral life cycle and relate that to our knowledge of coronavirus biology. The elucidation of similarities and differences between SARS-CoV-2 and other coronaviruses will support future preparedness and strategies to combat coronavirus infections.