Systematic identification and characterization of genes in the regulation and biogenesis of photosynthetic machinery.

Photosynthesis is central to food production and the Earth's biogeochemistry, yet the molecular basis for its regulation remains poorly understood. Here, using high-throughput genetics in the model eukaryotic alga Chlamydomonas reinhardtii, we identify with high confidence (false discovery rate [FDR] < 0.11) 70 poorly characterized genes required for photosynthesis. We then enable the functional characterization of these genes by providing a resource of proteomes of mutant strains, each lacking one of these genes. The data allow assignment of 34 genes to the biogenesis or regulation of one or more specific photosynthetic complexes. Further analysis uncovers biogenesis/regulatory roles for at least seven proteins, including five photosystem I mRNA maturation factors, the chloroplast translation factor MTF1, and the master regulator PMR1, which regulates chloroplast genes via nuclear-expressed factors. Our work provides a rich resource identifying regulatory and functional genes and placing them into pathways, thereby opening the door to a system-level understanding of photosynthesis.

A chloroplast protein atlas reveals punctate structures and spatial organization of biosynthetic pathways.

Chloroplasts are eukaryotic photosynthetic organelles that drive the global carbon cycle. Despite their importance, our understanding of their protein composition, function, and spatial organization remains limited. Here, we determined the localizations of 1,034 candidate chloroplast proteins using fluorescent protein tagging in the model alga Chlamydomonas reinhardtii. The localizations provide insights into the functions of poorly characterized proteins; identify novel components of nucleoids, plastoglobules, and the pyrenoid; and reveal widespread protein targeting to multiple compartments. We discovered and further characterized cellular organizational features, including eleven chloroplast punctate structures, cytosolic crescent structures, and unexpected spatial distributions of enzymes within the chloroplast. We also used machine learning to predict the localizations of other nuclear-encoded Chlamydomonas proteins. The strains and localization atlas developed here will serve as a resource to accelerate studies of chloroplast architecture and functions.

A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies.

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Leveraging protein language models for accurate multiple sequence alignments.

Multiple sequence alignment (MSA) is a critical step in the study of protein sequence and function. Typically, MSA algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. Although successful, standard methods struggle on sets of proteins with low sequence identity: the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverages massive sequence data sets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to MSA, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of MSA algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino-acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants.

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

A systematic, label-free method for identifying RNA-associated proteins in vivo provides insights into vertebrate ciliary beating machinery.

Cell-type specific RNA-associated proteins are essential for development and homeostasis in animals. Despite a massive recent effort to systematically identify RNA-associated proteins, we currently have few comprehensive rosters of cell-type specific RNA-associated proteins in vertebrate tissues. Here, we demonstrate the feasibility of determining the RNA-associated proteome of a defined vertebrate embryonic tissue using DIF-FRAC, a systematic and universal (i.e., label-free) method. Application of DIF-FRAC to cultured tissue explants of Xenopus mucociliary epithelium identified dozens of known RNA-associated proteins as expected, but also several novel RNA-associated proteins, including proteins related to assembly of the mitotic spindle and regulation of ciliary beating. In particular, we show that the inner dynein arm tether Cfap44 is an RNA-associated protein that localizes not only to axonemes, but also to liquid-like organelles in the cytoplasm called DynAPs. This result led us to discover that DynAPs are generally enriched for RNA. Together, these data provide a useful resource for a deeper understanding of mucociliary epithelia and demonstrate that DIF-FRAC will be broadly applicable for systematic identification of RNA-associated proteins from embryonic tissues.

Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k-cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease-associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders.

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

The protein organization of a red blood cell.

Red blood cells (RBCs) (erythrocytes) are the simplest primary human cells, lacking nuclei and major organelles and instead employing about a thousand proteins to dynamically control cellular function and morphology in response to physiological cues. In this study, we define a canonical RBC proteome and interactome using quantitative mass spectrometry and machine learning. Our data reveal an RBC interactome dominated by protein homeostasis, redox biology, cytoskeletal dynamics, and carbon metabolism. We validate protein complexes through electron microscopy and chemical crosslinking and, with these data, build 3D structural models of the ankyrin/Band 3/Band 4.2 complex that bridges the spectrin cytoskeleton to the RBC membrane. The model suggests spring-like compression of ankyrin may contribute to the characteristic RBC cell shape and flexibility. Taken together, our study provides an in-depth view of the global protein organization of human RBCs and serves as a comprehensive resource for future research.

Co-fractionation/mass spectrometry to identify protein complexes.

Co-fractionation/mass spectrometry (CF/MS) is a flexible and powerful method to detect physical associations of proteins. CF/MS can be applied to any tissue or organism without the need for protein-specific antibodies or epitope tags. Here, we outline two alternate protocols for MS preparation of samples (containing low or high salt) and a computational pipeline (cfmsflow) that together allow the successful application of this approach. These protocols are based on CF/MS of over 16 diverse organisms including plants and animals. For complete details on the use and execution of this protocol, please refer to McWhite et al. (2020).

Influenza passaging annotations: what they tell us and why we should listen.

Influenza databases now contain over 100,000 worldwide sequence records for strains influenza A(H3N2) and A(H1N1). Although these data facilitate global research efforts and vaccine development practices, they also represent a stumbling block for researchers because of their confusing and heterogeneous annotation. Unclear passaging annotations are particularly concerning given the recent work highlighting the presence and risk of false adaptation signals introduced by cell passaging of viral isolates. With this in mind, we aim to provide a concise outline of why viruses are passaged, a clear overview of passaging annotation nomenclature currently in use, and suggestions for a standardized nomenclature going forward. Our hope is that this summary will empower researchers and clinicians alike to more easily understand a virus sample's passage history when analyzing influenza sequences.

Systematic bacterialization of yeast genes identifies a near-universally swappable pathway.

Eukaryotes and prokaryotes last shared a common ancestor ~2 billion years ago, and while many present-day genes in these lineages predate this divergence, the extent to which these genes still perform their ancestral functions is largely unknown. To test principles governing retention of ancient function, we asked if prokaryotic genes could replace their essential eukaryotic orthologs. We systematically replaced essential genes in yeast by their 1:1 orthologs from Escherichia coli. After accounting for mitochondrial localization and alternative start codons, 31 out of 51 bacterial genes tested (61%) could complement a lethal growth defect and replace their yeast orthologs with minimal effects on growth rate. Replaceability was determined on a pathway-by-pathway basis; codon usage, abundance, and sequence similarity contributed predictive power. The heme biosynthesis pathway was particularly amenable to inter-kingdom exchange, with each yeast enzyme replaceable by its bacterial, human, or plant ortholog, suggesting it as a near-universally swappable pathway.

Towards Consensus Gene Ages.

Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a gene's age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use 13 orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses (geneages.org).

Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin.

Clinical influenza A virus isolates are frequently not sequenced directly. Instead, a majority of these isolates (~70% in 2015) are first subjected to passaging for amplification, most commonly in non-human cell culture. Here, we find that this passaging leaves distinct signals of adaptation, which can confound evolutionary analyses of the viral sequences. We find distinct patterns of adaptation to Madin-Darby (MDCK) and monkey cell culture absent from unpassaged hemagglutinin sequences. These patterns also dominate pooled datasets not separated by passaging type, and they increase in proportion to the number of passages performed. By contrast, MDCK-SIAT1 passaged sequences seem mostly (but not entirely) free of passaging adaptations. Contrary to previous studies, we find that using only internal branches of influenza virus phylogenetic trees is insufficient to correct for passaging artifacts. These artifacts can only be safely avoided by excluding passaged sequences entirely from subsequent analysis. We conclude that future influenza virus evolutionary analyses should appropriately control for potentially confounding effects of passaging adaptations.

Applications of comparative evolution to human disease genetics.

Direct comparison of human diseases with model phenotypes allows exploration of key areas of human biology which are often inaccessible for practical or ethical reasons. We review recent developments in comparative evolutionary approaches for finding models for genetic disease, including high-throughput generation of gene/phenotype relationship data, the linking of orthologous genes and phenotypes across species, and statistical methods for linking human diseases to model phenotypes.

Achaete-scute homologue-1 (ASH1) stimulates migration of lung cancer cells through Cdk5/p35 pathway.

Our previous data suggested that the human basic helix-loop-helix transcription factor achaete-scute homologue-1 (hASH1) may stimulate both proliferation and migration in the lung. In the CNS, cyclin-dependent kinase 5 (Cdk5) and its activator p35 are important for neuronal migration that is regulated by basic helix-loop-helix transcription factors. Cdk5/p35 may also play a role in carcinogenesis. In this study, we found that the neuronal activator p35 was commonly expressed in primary human lung cancers. Cdk5 and p35 were also expressed by several human lung cancer cell lines and coupled with migration and invasion. When the kinase activity was inhibited by the Cdk5 inhibitor roscovitine or dominant-negative (dn) Cdk5, the migration of lung cancer cells was reduced. In neuroendocrine cells expressing hASH1, such as a pulmonary carcinoid cell line, knocking down the gene expression by short hairpin RNA reduced the levels of Cdk5/p35, nuclear p35 protein, and migration. Furthermore, expression of hASH1 in lung adenocarcinoma cells normally lacking hASH1 increased p35/Cdk5 activity and enhanced cellular migration. We were also able to show that p35 was a direct target for hASH1. In conclusion, induction of Cdk5 activity is a novel mechanism through which hASH1 may regulate migration in lung carcinogenesis.